mdciao.utils.sequence

Functions for and around sequence alignment

The alignment takes only in one place (my_bioalign), the rest of functions either prepare the alignment or produce other objects derived from it (DataFrames, dictionaries, maps between topologies etc)

Functions

Functions

align_tops_or_seqs(top0, top1[, …])

Align two sequence-containing objects, i.e.

alignment_result_to_list_of_dicts(ialg, …)

Input an alignment result ialg and return it as a list of per-residue dictionaries with other complementary keys.

df2maps(df[, allow_nonmatch])

Map the columns “idx_0” and “idx_1” of an alignment (a pandas.DataFrame)

maptops(top0, top1[, allow_nonmatch])

map residues between topologies or sequences via their serial indices a sequence alignment

my_bioalign(seq1, seq2[, method, match, …])

Align two sequences using Bio.Align.PairwiseAligner

print_verbose_dataframe(df)

Print the full dataframe no matter how big

re_match_df(df)

Return a copy of an alignment pandas.Dataframe with True ‘match’-values for non-matching blocks that have equal length.

superpose_w_CA_align(geom, ref[, …])

Pre align on CA-atoms before calling mdtraj.Trajectory.superpose

top2seq(top[, replacement_letter])

Return the AA sequence of top as a string

Classes

AlignmentDataFrame(*args, **kwargs)

Sub-class of an DataFrame to include the alignment_score as metadata.

class mdciao.utils.sequence.AlignmentDataFrame(*args, **kwargs)

Sub-class of an DataFrame to include the alignment_score as metadata.

Simply pass it as argument ‘ alignment_score=1’ and it:
  • can be then accessed via self.alignment_score and

  • it is preserved downstream after operating on the df

Check https://pandas.pydata.org/pandas-docs/stable/development/extending.html#define-original-properties for more info

abs() → FrameOrSeries

Return a Series/DataFrame with absolute numeric value of each element.

This function only applies to elements that are all numeric.

Returns

Series/DataFrame containing the absolute value of each element.

Return type

abs

See also

numpy.absolute()

Calculate the absolute value element-wise.

Notes

For complex inputs, 1.2 + 1j, the absolute value is \(\sqrt{ a^2 + b^2 }\).

Examples

Absolute numeric values in a Series.

>>> s = pd.Series([-1.10, 2, -3.33, 4])
>>> s.abs()
0    1.10
1    2.00
2    3.33
3    4.00
dtype: float64

Absolute numeric values in a Series with complex numbers.

>>> s = pd.Series([1.2 + 1j])
>>> s.abs()
0    1.56205
dtype: float64

Absolute numeric values in a Series with a Timedelta element.

>>> s = pd.Series([pd.Timedelta('1 days')])
>>> s.abs()
0   1 days
dtype: timedelta64[ns]

Select rows with data closest to certain value using argsort (from StackOverflow).

>>> df = pd.DataFrame({
...     'a': [4, 5, 6, 7],
...     'b': [10, 20, 30, 40],
...     'c': [100, 50, -30, -50]
... })
>>> df
     a    b    c
0    4   10  100
1    5   20   50
2    6   30  -30
3    7   40  -50
>>> df.loc[(df.c - 43).abs().argsort()]
     a    b    c
1    5   20   50
0    4   10  100
2    6   30  -30
3    7   40  -50
add(other, axis='columns', level=None, fill_value=None)

Get Addition of dataframe and other, element-wise (binary operator add).

Equivalent to dataframe + other, but with support to substitute a fill_value for missing data in one of the inputs. With reverse version, radd.

Among flexible wrappers (add, sub, mul, div, mod, pow) to arithmetic operators: +, -, *, /, //, %, **.

Parameters
  • other (scalar, sequence, Series, or DataFrame) – Any single or multiple element data structure, or list-like object.

  • axis ({0 or 'index', 1 or 'columns'}) – Whether to compare by the index (0 or ‘index’) or columns (1 or ‘columns’). For Series input, axis to match Series index on.

  • level (int or label) – Broadcast across a level, matching Index values on the passed MultiIndex level.

  • fill_value (float or None, default None) – Fill existing missing (NaN) values, and any new element needed for successful DataFrame alignment, with this value before computation. If data in both corresponding DataFrame locations is missing the result will be missing.

Returns

Result of the arithmetic operation.

Return type

DataFrame

See also

DataFrame.add()

Add DataFrames.

DataFrame.sub()

Subtract DataFrames.

DataFrame.mul()

Multiply DataFrames.

DataFrame.div()

Divide DataFrames (float division).

DataFrame.truediv()

Divide DataFrames (float division).

DataFrame.floordiv()

Divide DataFrames (integer division).

DataFrame.mod()

Calculate modulo (remainder after division).

DataFrame.pow()

Calculate exponential power.

Notes

Mismatched indices will be unioned together.

Examples

>>> df = pd.DataFrame({'angles': [0, 3, 4],
...                    'degrees': [360, 180, 360]},
...                   index=['circle', 'triangle', 'rectangle'])
>>> df
           angles  degrees
circle          0      360
triangle        3      180
rectangle       4      360

Add a scalar with operator version which return the same results.

>>> df + 1
           angles  degrees
circle          1      361
triangle        4      181
rectangle       5      361
>>> df.add(1)
           angles  degrees
circle          1      361
triangle        4      181
rectangle       5      361

Divide by constant with reverse version.

>>> df.div(10)
           angles  degrees
circle        0.0     36.0
triangle      0.3     18.0
rectangle     0.4     36.0
>>> df.rdiv(10)
             angles   degrees
circle          inf  0.027778
triangle   3.333333  0.055556
rectangle  2.500000  0.027778

Subtract a list and Series by axis with operator version.

>>> df - [1, 2]
           angles  degrees
circle         -1      358
triangle        2      178
rectangle       3      358
>>> df.sub([1, 2], axis='columns')
           angles  degrees
circle         -1      358
triangle        2      178
rectangle       3      358
>>> df.sub(pd.Series([1, 1, 1], index=['circle', 'triangle', 'rectangle']),
...        axis='index')
           angles  degrees
circle         -1      359
triangle        2      179
rectangle       3      359

Multiply a DataFrame of different shape with operator version.

>>> other = pd.DataFrame({'angles': [0, 3, 4]},
...                      index=['circle', 'triangle', 'rectangle'])
>>> other
           angles
circle          0
triangle        3
rectangle       4
>>> df * other
           angles  degrees
circle          0      NaN
triangle        9      NaN
rectangle      16      NaN
>>> df.mul(other, fill_value=0)
           angles  degrees
circle          0      0.0
triangle        9      0.0
rectangle      16      0.0

Divide by a MultiIndex by level.

>>> df_multindex = pd.DataFrame({'angles': [0, 3, 4, 4, 5, 6],
...                              'degrees': [360, 180, 360, 360, 540, 720]},
...                             index=[['A', 'A', 'A', 'B', 'B', 'B'],
...                                    ['circle', 'triangle', 'rectangle',
...                                     'square', 'pentagon', 'hexagon']])
>>> df_multindex
             angles  degrees
A circle          0      360
  triangle        3      180
  rectangle       4      360
B square          4      360
  pentagon        5      540
  hexagon         6      720
>>> df.div(df_multindex, level=1, fill_value=0)
             angles  degrees
A circle        NaN      1.0
  triangle      1.0      1.0
  rectangle     1.0      1.0
B square        0.0      0.0
  pentagon      0.0      0.0
  hexagon       0.0      0.0
add_prefix(prefix: str) → FrameOrSeries

Prefix labels with string prefix.

For Series, the row labels are prefixed. For DataFrame, the column labels are prefixed.

Parameters

prefix (str) – The string to add before each label.

Returns

New Series or DataFrame with updated labels.

Return type

Series or DataFrame

See also

Series.add_suffix()

Suffix row labels with string suffix.

DataFrame.add_suffix()

Suffix column labels with string suffix.

Examples

>>> s = pd.Series([1, 2, 3, 4])
>>> s
0    1
1    2
2    3
3    4
dtype: int64
>>> s.add_prefix('item_')
item_0    1
item_1    2
item_2    3
item_3    4
dtype: int64
>>> df = pd.DataFrame({'A': [1, 2, 3, 4], 'B': [3, 4, 5, 6]})
>>> df
   A  B
0  1  3
1  2  4
2  3  5
3  4  6
>>> df.add_prefix('col_')
     col_A  col_B
0       1       3
1       2       4
2       3       5
3       4       6
add_suffix(suffix: str) → FrameOrSeries

Suffix labels with string suffix.

For Series, the row labels are suffixed. For DataFrame, the column labels are suffixed.

Parameters

suffix (str) – The string to add after each label.

Returns

New Series or DataFrame with updated labels.

Return type

Series or DataFrame

See also

Series.add_prefix()

Prefix row labels with string prefix.

DataFrame.add_prefix()

Prefix column labels with string prefix.

Examples

>>> s = pd.Series([1, 2, 3, 4])
>>> s
0    1
1    2
2    3
3    4
dtype: int64
>>> s.add_suffix('_item')
0_item    1
1_item    2
2_item    3
3_item    4
dtype: int64
>>> df = pd.DataFrame({'A': [1, 2, 3, 4], 'B': [3, 4, 5, 6]})
>>> df
   A  B
0  1  3
1  2  4
2  3  5
3  4  6
>>> df.add_suffix('_col')
     A_col  B_col
0       1       3
1       2       4
2       3       5
3       4       6
agg(func=None, axis=0, *args, **kwargs)

Aggregate using one or more operations over the specified axis.

New in version 0.20.0.

Parameters
  • func (function, str, list or dict) –

    Function to use for aggregating the data. If a function, must either work when passed a DataFrame or when passed to DataFrame.apply.

    Accepted combinations are:

    • function

    • string function name

    • list of functions and/or function names, e.g. [np.sum, 'mean']

    • dict of axis labels -> functions, function names or list of such.

  • axis ({0 or 'index', 1 or 'columns'}, default 0) – If 0 or ‘index’: apply function to each column. If 1 or ‘columns’: apply function to each row.

  • *args – Positional arguments to pass to func.

  • **kwargs – Keyword arguments to pass to func.

Returns

  • scalar, Series or DataFrame – The return can be:

    • scalar : when Series.agg is called with single function

    • Series : when DataFrame.agg is called with a single function

    • DataFrame : when DataFrame.agg is called with several functions

    Return scalar, Series or DataFrame.

  • The aggregation operations are always performed over an axis, either the

  • index (default) or the column axis. This behavior is different from

  • numpy aggregation functions (mean, median, prod, sum, std,

  • var), where the default is to compute the aggregation of the flattened

  • array, e.g., numpy.mean(arr_2d) as opposed to

  • numpy.mean(arr_2d, axis=0).

  • agg is an alias for aggregate. Use the alias.

See also

DataFrame.apply()

Perform any type of operations.

DataFrame.transform()

Perform transformation type operations.

core.groupby.GroupBy()

Perform operations over groups.

core.resample.Resampler()

Perform operations over resampled bins.

core.window.Rolling()

Perform operations over rolling window.

core.window.Expanding()

Perform operations over expanding window.

core.window.ExponentialMovingWindow()

Perform operation over exponential weighted window.

Notes

agg is an alias for aggregate. Use the alias.

A passed user-defined-function will be passed a Series for evaluation.

Examples

>>> df = pd.DataFrame([[1, 2, 3],
...                    [4, 5, 6],
...                    [7, 8, 9],
...                    [np.nan, np.nan, np.nan]],
...                   columns=['A', 'B', 'C'])

Aggregate these functions over the rows.

>>> df.agg(['sum', 'min'])
        A     B     C
sum  12.0  15.0  18.0
min   1.0   2.0   3.0

Different aggregations per column.

>>> df.agg({'A' : ['sum', 'min'], 'B' : ['min', 'max']})
        A    B
max   NaN  8.0
min   1.0  2.0
sum  12.0  NaN

Aggregate over the columns.

>>> df.agg("mean", axis="columns")
0    2.0
1    5.0
2    8.0
3    NaN
dtype: float64
aggregate(func=None, axis=0, *args, **kwargs)

Aggregate using one or more operations over the specified axis.

New in version 0.20.0.

Parameters
  • func (function, str, list or dict) –

    Function to use for aggregating the data. If a function, must either work when passed a DataFrame or when passed to DataFrame.apply.

    Accepted combinations are:

    • function

    • string function name

    • list of functions and/or function names, e.g. [np.sum, 'mean']

    • dict of axis labels -> functions, function names or list of such.

  • axis ({0 or 'index', 1 or 'columns'}, default 0) – If 0 or ‘index’: apply function to each column. If 1 or ‘columns’: apply function to each row.

  • *args – Positional arguments to pass to func.

  • **kwargs – Keyword arguments to pass to func.

Returns

  • scalar, Series or DataFrame – The return can be:

    • scalar : when Series.agg is called with single function

    • Series : when DataFrame.agg is called with a single function

    • DataFrame : when DataFrame.agg is called with several functions

    Return scalar, Series or DataFrame.

  • The aggregation operations are always performed over an axis, either the

  • index (default) or the column axis. This behavior is different from

  • numpy aggregation functions (mean, median, prod, sum, std,

  • var), where the default is to compute the aggregation of the flattened

  • array, e.g., numpy.mean(arr_2d) as opposed to

  • numpy.mean(arr_2d, axis=0).

  • agg is an alias for aggregate. Use the alias.

See also

DataFrame.apply()

Perform any type of operations.

DataFrame.transform()

Perform transformation type operations.

core.groupby.GroupBy()

Perform operations over groups.

core.resample.Resampler()

Perform operations over resampled bins.

core.window.Rolling()

Perform operations over rolling window.

core.window.Expanding()

Perform operations over expanding window.

core.window.ExponentialMovingWindow()

Perform operation over exponential weighted window.

Notes

agg is an alias for aggregate. Use the alias.

A passed user-defined-function will be passed a Series for evaluation.

Examples

>>> df = pd.DataFrame([[1, 2, 3],
...                    [4, 5, 6],
...                    [7, 8, 9],
...                    [np.nan, np.nan, np.nan]],
...                   columns=['A', 'B', 'C'])

Aggregate these functions over the rows.

>>> df.agg(['sum', 'min'])
        A     B     C
sum  12.0  15.0  18.0
min   1.0   2.0   3.0

Different aggregations per column.

>>> df.agg({'A' : ['sum', 'min'], 'B' : ['min', 'max']})
        A    B
max   NaN  8.0
min   1.0  2.0
sum  12.0  NaN

Aggregate over the columns.

>>> df.agg("mean", axis="columns")
0    2.0
1    5.0
2    8.0
3    NaN
dtype: float64
align(other, join='outer', axis=None, level=None, copy=True, fill_value=None, method=None, limit=None, fill_axis=0, broadcast_axis=None) → pandas.core.frame.DataFrame

Align two objects on their axes with the specified join method.

Join method is specified for each axis Index.

Parameters
  • other (DataFrame or Series) –

  • join ({'outer', 'inner', 'left', 'right'}, default 'outer') –

  • axis (allowed axis of the other object, default None) – Align on index (0), columns (1), or both (None).

  • level (int or level name, default None) – Broadcast across a level, matching Index values on the passed MultiIndex level.

  • copy (bool, default True) – Always returns new objects. If copy=False and no reindexing is required then original objects are returned.

  • fill_value (scalar, default np.NaN) – Value to use for missing values. Defaults to NaN, but can be any “compatible” value.

  • method ({'backfill', 'bfill', 'pad', 'ffill', None}, default None) –

    Method to use for filling holes in reindexed Series:

    • pad / ffill: propagate last valid observation forward to next valid.

    • backfill / bfill: use NEXT valid observation to fill gap.

  • limit (int, default None) – If method is specified, this is the maximum number of consecutive NaN values to forward/backward fill. In other words, if there is a gap with more than this number of consecutive NaNs, it will only be partially filled. If method is not specified, this is the maximum number of entries along the entire axis where NaNs will be filled. Must be greater than 0 if not None.

  • fill_axis ({0 or 'index', 1 or 'columns'}, default 0) – Filling axis, method and limit.

  • broadcast_axis ({0 or 'index', 1 or 'columns'}, default None) – Broadcast values along this axis, if aligning two objects of different dimensions.

Returns

(left, right) – Aligned objects.

Return type

(DataFrame, type of other)

all(axis=0, bool_only=None, skipna=True, level=None, **kwargs)

Return whether all elements are True, potentially over an axis.

Returns True unless there at least one element within a series or along a Dataframe axis that is False or equivalent (e.g. zero or empty).

Parameters
  • axis ({0 or 'index', 1 or 'columns', None}, default 0) –

    Indicate which axis or axes should be reduced.

    • 0 / ‘index’ : reduce the index, return a Series whose index is the original column labels.

    • 1 / ‘columns’ : reduce the columns, return a Series whose index is the original index.

    • None : reduce all axes, return a scalar.

  • bool_only (bool, default None) – Include only boolean columns. If None, will attempt to use everything, then use only boolean data. Not implemented for Series.

  • skipna (bool, default True) – Exclude NA/null values. If the entire row/column is NA and skipna is True, then the result will be True, as for an empty row/column. If skipna is False, then NA are treated as True, because these are not equal to zero.

  • level (int or level name, default None) – If the axis is a MultiIndex (hierarchical), count along a particular level, collapsing into a Series.

  • **kwargs (any, default None) – Additional keywords have no effect but might be accepted for compatibility with NumPy.

Returns

If level is specified, then, DataFrame is returned; otherwise, Series is returned.

Return type

Series or DataFrame

See also

Series.all()

Return True if all elements are True.

DataFrame.any()

Return True if one (or more) elements are True.

Examples

Series

>>> pd.Series([True, True]).all()
True
>>> pd.Series([True, False]).all()
False
>>> pd.Series([]).all()
True
>>> pd.Series([np.nan]).all()
True
>>> pd.Series([np.nan]).all(skipna=False)
True

DataFrames

Create a dataframe from a dictionary.

>>> df = pd.DataFrame({'col1': [True, True], 'col2': [True, False]})
>>> df
   col1   col2
0  True   True
1  True  False

Default behaviour checks if column-wise values all return True.

>>> df.all()
col1     True
col2    False
dtype: bool

Specify axis='columns' to check if row-wise values all return True.

>>> df.all(axis='columns')
0     True
1    False
dtype: bool

Or axis=None for whether every value is True.

>>> df.all(axis=None)
False
any(axis=0, bool_only=None, skipna=True, level=None, **kwargs)

Return whether any element is True, potentially over an axis.

Returns False unless there at least one element within a series or along a Dataframe axis that is True or equivalent (e.g. non-zero or non-empty).

Parameters
  • axis ({0 or 'index', 1 or 'columns', None}, default 0) –

    Indicate which axis or axes should be reduced.

    • 0 / ‘index’ : reduce the index, return a Series whose index is the original column labels.

    • 1 / ‘columns’ : reduce the columns, return a Series whose index is the original index.

    • None : reduce all axes, return a scalar.

  • bool_only (bool, default None) – Include only boolean columns. If None, will attempt to use everything, then use only boolean data. Not implemented for Series.

  • skipna (bool, default True) – Exclude NA/null values. If the entire row/column is NA and skipna is True, then the result will be False, as for an empty row/column. If skipna is False, then NA are treated as True, because these are not equal to zero.

  • level (int or level name, default None) – If the axis is a MultiIndex (hierarchical), count along a particular level, collapsing into a Series.

  • **kwargs (any, default None) – Additional keywords have no effect but might be accepted for compatibility with NumPy.

Returns

If level is specified, then, DataFrame is returned; otherwise, Series is returned.

Return type

Series or DataFrame

See also

numpy.any()

Numpy version of this method.

Series.any()

Return whether any element is True.

Series.all()

Return whether all elements are True.

DataFrame.any()

Return whether any element is True over requested axis.

DataFrame.all()

Return whether all elements are True over requested axis.

Examples

Series

For Series input, the output is a scalar indicating whether any element is True.

>>> pd.Series([False, False]).any()
False
>>> pd.Series([True, False]).any()
True
>>> pd.Series([]).any()
False
>>> pd.Series([np.nan]).any()
False
>>> pd.Series([np.nan]).any(skipna=False)
True

DataFrame

Whether each column contains at least one True element (the default).

>>> df = pd.DataFrame({"A": [1, 2], "B": [0, 2], "C": [0, 0]})
>>> df
   A  B  C
0  1  0  0
1  2  2  0
>>> df.any()
A     True
B     True
C    False
dtype: bool

Aggregating over the columns.

>>> df = pd.DataFrame({"A": [True, False], "B": [1, 2]})
>>> df
       A  B
0   True  1
1  False  2
>>> df.any(axis='columns')
0    True
1    True
dtype: bool
>>> df = pd.DataFrame({"A": [True, False], "B": [1, 0]})
>>> df
       A  B
0   True  1
1  False  0
>>> df.any(axis='columns')
0    True
1    False
dtype: bool

Aggregating over the entire DataFrame with axis=None.

>>> df.any(axis=None)
True

any for an empty DataFrame is an empty Series.

>>> pd.DataFrame([]).any()
Series([], dtype: bool)
append(other, ignore_index=False, verify_integrity=False, sort=False) → pandas.core.frame.DataFrame

Append rows of other to the end of caller, returning a new object.

Columns in other that are not in the caller are added as new columns.

Parameters
  • other (DataFrame or Series/dict-like object, or list of these) – The data to append.

  • ignore_index (bool, default False) – If True, the resulting axis will be labeled 0, 1, …, n - 1.

  • verify_integrity (bool, default False) – If True, raise ValueError on creating index with duplicates.

  • sort (bool, default False) –

    Sort columns if the columns of self and other are not aligned.

    New in version 0.23.0.

    Changed in version 1.0.0: Changed to not sort by default.

Returns

Return type

DataFrame

See also

concat()

General function to concatenate DataFrame or Series objects.

Notes

If a list of dict/series is passed and the keys are all contained in the DataFrame’s index, the order of the columns in the resulting DataFrame will be unchanged.

Iteratively appending rows to a DataFrame can be more computationally intensive than a single concatenate. A better solution is to append those rows to a list and then concatenate the list with the original DataFrame all at once.

Examples

>>> df = pd.DataFrame([[1, 2], [3, 4]], columns=list('AB'))
>>> df
   A  B
0  1  2
1  3  4
>>> df2 = pd.DataFrame([[5, 6], [7, 8]], columns=list('AB'))
>>> df.append(df2)
   A  B
0  1  2
1  3  4
0  5  6
1  7  8

With ignore_index set to True:

>>> df.append(df2, ignore_index=True)
   A  B
0  1  2
1  3  4
2  5  6
3  7  8

The following, while not recommended methods for generating DataFrames, show two ways to generate a DataFrame from multiple data sources.

Less efficient:

>>> df = pd.DataFrame(columns=['A'])
>>> for i in range(5):
...     df = df.append({'A': i}, ignore_index=True)
>>> df
   A
0  0
1  1
2  2
3  3
4  4

More efficient:

>>> pd.concat([pd.DataFrame([i], columns=['A']) for i in range(5)],
...           ignore_index=True)
   A
0  0
1  1
2  2
3  3
4  4
apply(func, axis=0, raw=False, result_type=None, args=(), **kwds)

Apply a function along an axis of the DataFrame.

Objects passed to the function are Series objects whose index is either the DataFrame’s index (axis=0) or the DataFrame’s columns (axis=1). By default (result_type=None), the final return type is inferred from the return type of the applied function. Otherwise, it depends on the result_type argument.

Parameters
  • func (function) – Function to apply to each column or row.

  • axis ({0 or 'index', 1 or 'columns'}, default 0) –

    Axis along which the function is applied:

    • 0 or ‘index’: apply function to each column.

    • 1 or ‘columns’: apply function to each row.

  • raw (bool, default False) –

    Determines if row or column is passed as a Series or ndarray object:

    • False : passes each row or column as a Series to the function.

    • True : the passed function will receive ndarray objects instead. If you are just applying a NumPy reduction function this will achieve much better performance.

  • result_type ({'expand', 'reduce', 'broadcast', None}, default None) –

    These only act when axis=1 (columns):

    • ’expand’ : list-like results will be turned into columns.

    • ’reduce’ : returns a Series if possible rather than expanding list-like results. This is the opposite of ‘expand’.

    • ’broadcast’ : results will be broadcast to the original shape of the DataFrame, the original index and columns will be retained.

    The default behaviour (None) depends on the return value of the applied function: list-like results will be returned as a Series of those. However if the apply function returns a Series these are expanded to columns.

    New in version 0.23.0.

  • args (tuple) – Positional arguments to pass to func in addition to the array/series.

  • **kwds – Additional keyword arguments to pass as keywords arguments to func.

Returns

Result of applying func along the given axis of the DataFrame.

Return type

Series or DataFrame

See also

DataFrame.applymap()

For elementwise operations.

DataFrame.aggregate()

Only perform aggregating type operations.

DataFrame.transform()

Only perform transforming type operations.

Examples

>>> df = pd.DataFrame([[4, 9]] * 3, columns=['A', 'B'])
>>> df
   A  B
0  4  9
1  4  9
2  4  9

Using a numpy universal function (in this case the same as np.sqrt(df)):

>>> df.apply(np.sqrt)
     A    B
0  2.0  3.0
1  2.0  3.0
2  2.0  3.0

Using a reducing function on either axis

>>> df.apply(np.sum, axis=0)
A    12
B    27
dtype: int64
>>> df.apply(np.sum, axis=1)
0    13
1    13
2    13
dtype: int64

Returning a list-like will result in a Series

>>> df.apply(lambda x: [1, 2], axis=1)
0    [1, 2]
1    [1, 2]
2    [1, 2]
dtype: object

Passing result_type='expand' will expand list-like results to columns of a Dataframe

>>> df.apply(lambda x: [1, 2], axis=1, result_type='expand')
   0  1
0  1  2
1  1  2
2  1  2

Returning a Series inside the function is similar to passing result_type='expand'. The resulting column names will be the Series index.

>>> df.apply(lambda x: pd.Series([1, 2], index=['foo', 'bar']), axis=1)
   foo  bar
0    1    2
1    1    2
2    1    2

Passing result_type='broadcast' will ensure the same shape result, whether list-like or scalar is returned by the function, and broadcast it along the axis. The resulting column names will be the originals.

>>> df.apply(lambda x: [1, 2], axis=1, result_type='broadcast')
   A  B
0  1  2
1  1  2
2  1  2
applymap(func) → pandas.core.frame.DataFrame

Apply a function to a Dataframe elementwise.

This method applies a function that accepts and returns a scalar to every element of a DataFrame.

Parameters

func (callable) – Python function, returns a single value from a single value.

Returns

Transformed DataFrame.

Return type

DataFrame

See also

DataFrame.apply()

Apply a function along input axis of DataFrame.

Examples

>>> df = pd.DataFrame([[1, 2.12], [3.356, 4.567]])
>>> df
       0      1
0  1.000  2.120
1  3.356  4.567
>>> df.applymap(lambda x: len(str(x)))
   0  1
0  3  4
1  5  5

Note that a vectorized version of func often exists, which will be much faster. You could square each number elementwise.

>>> df.applymap(lambda x: x**2)
           0          1
0   1.000000   4.494400
1  11.262736  20.857489

But it’s better to avoid applymap in that case.

>>> df ** 2
           0          1
0   1.000000   4.494400
1  11.262736  20.857489
asfreq(freq, method=None, how: Optional[str] = None, normalize: bool = False, fill_value=None) → FrameOrSeries

Convert TimeSeries to specified frequency.

Optionally provide filling method to pad/backfill missing values.

Returns the original data conformed to a new index with the specified frequency. resample is more appropriate if an operation, such as summarization, is necessary to represent the data at the new frequency.

Parameters
  • freq (DateOffset or str) – Frequency DateOffset or string.

  • method ({'backfill'/'bfill', 'pad'/'ffill'}, default None) –

    Method to use for filling holes in reindexed Series (note this does not fill NaNs that already were present):

    • ’pad’ / ‘ffill’: propagate last valid observation forward to next valid

    • ’backfill’ / ‘bfill’: use NEXT valid observation to fill.

  • how ({'start', 'end'}, default end) – For PeriodIndex only (see PeriodIndex.asfreq).

  • normalize (bool, default False) – Whether to reset output index to midnight.

  • fill_value (scalar, optional) – Value to use for missing values, applied during upsampling (note this does not fill NaNs that already were present).

Returns

Object converted to the specified frequency.

Return type

Same type as caller

See also

reindex()

Conform DataFrame to new index with optional filling logic.

Notes

To learn more about the frequency strings, please see this link.

Examples

Start by creating a series with 4 one minute timestamps.

>>> index = pd.date_range('1/1/2000', periods=4, freq='T')
>>> series = pd.Series([0.0, None, 2.0, 3.0], index=index)
>>> df = pd.DataFrame({'s':series})
>>> df
                       s
2000-01-01 00:00:00    0.0
2000-01-01 00:01:00    NaN
2000-01-01 00:02:00    2.0
2000-01-01 00:03:00    3.0

Upsample the series into 30 second bins.

>>> df.asfreq(freq='30S')
                       s
2000-01-01 00:00:00    0.0
2000-01-01 00:00:30    NaN
2000-01-01 00:01:00    NaN
2000-01-01 00:01:30    NaN
2000-01-01 00:02:00    2.0
2000-01-01 00:02:30    NaN
2000-01-01 00:03:00    3.0

Upsample again, providing a fill value.

>>> df.asfreq(freq='30S', fill_value=9.0)
                       s
2000-01-01 00:00:00    0.0
2000-01-01 00:00:30    9.0
2000-01-01 00:01:00    NaN
2000-01-01 00:01:30    9.0
2000-01-01 00:02:00    2.0
2000-01-01 00:02:30    9.0
2000-01-01 00:03:00    3.0

Upsample again, providing a method.

>>> df.asfreq(freq='30S', method='bfill')
                       s
2000-01-01 00:00:00    0.0
2000-01-01 00:00:30    NaN
2000-01-01 00:01:00    NaN
2000-01-01 00:01:30    2.0
2000-01-01 00:02:00    2.0
2000-01-01 00:02:30    3.0
2000-01-01 00:03:00    3.0
asof(where, subset=None)

Return the last row(s) without any NaNs before where.

The last row (for each element in where, if list) without any NaN is taken. In case of a DataFrame, the last row without NaN considering only the subset of columns (if not None)

If there is no good value, NaN is returned for a Series or a Series of NaN values for a DataFrame

Parameters
  • where (date or array-like of dates) – Date(s) before which the last row(s) are returned.

  • subset (str or array-like of str, default None) – For DataFrame, if not None, only use these columns to check for NaNs.

Returns

The return can be:

  • scalar : when self is a Series and where is a scalar

  • Series: when self is a Series and where is an array-like, or when self is a DataFrame and where is a scalar

  • DataFrame : when self is a DataFrame and where is an array-like

Return scalar, Series, or DataFrame.

Return type

scalar, Series, or DataFrame

See also

merge_asof()

Perform an asof merge. Similar to left join.

Notes

Dates are assumed to be sorted. Raises if this is not the case.

Examples

A Series and a scalar where.

>>> s = pd.Series([1, 2, np.nan, 4], index=[10, 20, 30, 40])
>>> s
10    1.0
20    2.0
30    NaN
40    4.0
dtype: float64
>>> s.asof(20)
2.0

For a sequence where, a Series is returned. The first value is NaN, because the first element of where is before the first index value.

>>> s.asof([5, 20])
5     NaN
20    2.0
dtype: float64

Missing values are not considered. The following is 2.0, not NaN, even though NaN is at the index location for 30.

>>> s.asof(30)
2.0

Take all columns into consideration

>>> df = pd.DataFrame({'a': [10, 20, 30, 40, 50],
...                    'b': [None, None, None, None, 500]},
...                   index=pd.DatetimeIndex(['2018-02-27 09:01:00',
...                                           '2018-02-27 09:02:00',
...                                           '2018-02-27 09:03:00',
...                                           '2018-02-27 09:04:00',
...                                           '2018-02-27 09:05:00']))
>>> df.asof(pd.DatetimeIndex(['2018-02-27 09:03:30',
...                           '2018-02-27 09:04:30']))
                      a   b
2018-02-27 09:03:30 NaN NaN
2018-02-27 09:04:30 NaN NaN

Take a single column into consideration

>>> df.asof(pd.DatetimeIndex(['2018-02-27 09:03:30',
...                           '2018-02-27 09:04:30']),
...         subset=['a'])
                         a   b
2018-02-27 09:03:30   30.0 NaN
2018-02-27 09:04:30   40.0 NaN
assign(**kwargs) → pandas.core.frame.DataFrame

Assign new columns to a DataFrame.

Returns a new object with all original columns in addition to new ones. Existing columns that are re-assigned will be overwritten.

Parameters

**kwargs (dict of {str: callable or Series}) – The column names are keywords. If the values are callable, they are computed on the DataFrame and assigned to the new columns. The callable must not change input DataFrame (though pandas doesn’t check it). If the values are not callable, (e.g. a Series, scalar, or array), they are simply assigned.

Returns

A new DataFrame with the new columns in addition to all the existing columns.

Return type

DataFrame

Notes

Assigning multiple columns within the same assign is possible. Later items in ‘**kwargs’ may refer to newly created or modified columns in ‘df’; items are computed and assigned into ‘df’ in order.

Changed in version 0.23.0: Keyword argument order is maintained.

Examples

>>> df = pd.DataFrame({'temp_c': [17.0, 25.0]},
...                   index=['Portland', 'Berkeley'])
>>> df
          temp_c
Portland    17.0
Berkeley    25.0

Where the value is a callable, evaluated on df:

>>> df.assign(temp_f=lambda x: x.temp_c * 9 / 5 + 32)
          temp_c  temp_f
Portland    17.0    62.6
Berkeley    25.0    77.0

Alternatively, the same behavior can be achieved by directly referencing an existing Series or sequence:

>>> df.assign(temp_f=df['temp_c'] * 9 / 5 + 32)
          temp_c  temp_f
Portland    17.0    62.6
Berkeley    25.0    77.0

You can create multiple columns within the same assign where one of the columns depends on another one defined within the same assign:

>>> df.assign(temp_f=lambda x: x['temp_c'] * 9 / 5 + 32,
...           temp_k=lambda x: (x['temp_f'] +  459.67) * 5 / 9)
          temp_c  temp_f  temp_k
Portland    17.0    62.6  290.15
Berkeley    25.0    77.0  298.15
astype(dtype, copy: bool = True, errors: str = 'raise') → FrameOrSeries

Cast a pandas object to a specified dtype dtype.

Parameters
  • dtype (data type, or dict of column name -> data type) – Use a numpy.dtype or Python type to cast entire pandas object to the same type. Alternatively, use {col: dtype, …}, where col is a column label and dtype is a numpy.dtype or Python type to cast one or more of the DataFrame’s columns to column-specific types.

  • copy (bool, default True) – Return a copy when copy=True (be very careful setting copy=False as changes to values then may propagate to other pandas objects).

  • errors ({'raise', 'ignore'}, default 'raise') –

    Control raising of exceptions on invalid data for provided dtype.

    • raise : allow exceptions to be raised

    • ignore : suppress exceptions. On error return original object.

Returns

casted

Return type

same type as caller

See also

to_datetime()

Convert argument to datetime.

to_timedelta()

Convert argument to timedelta.

to_numeric()

Convert argument to a numeric type.

numpy.ndarray.astype()

Cast a numpy array to a specified type.

Examples

Create a DataFrame:

>>> d = {'col1': [1, 2], 'col2': [3, 4]}
>>> df = pd.DataFrame(data=d)
>>> df.dtypes
col1    int64
col2    int64
dtype: object

Cast all columns to int32:

>>> df.astype('int32').dtypes
col1    int32
col2    int32
dtype: object

Cast col1 to int32 using a dictionary:

>>> df.astype({'col1': 'int32'}).dtypes
col1    int32
col2    int64
dtype: object

Create a series:

>>> ser = pd.Series([1, 2], dtype='int32')
>>> ser
0    1
1    2
dtype: int32
>>> ser.astype('int64')
0    1
1    2
dtype: int64

Convert to categorical type:

>>> ser.astype('category')
0    1
1    2
dtype: category
Categories (2, int64): [1, 2]

Convert to ordered categorical type with custom ordering:

>>> cat_dtype = pd.api.types.CategoricalDtype(
...     categories=[2, 1], ordered=True)
>>> ser.astype(cat_dtype)
0    1
1    2
dtype: category
Categories (2, int64): [2 < 1]

Note that using copy=False and changing data on a new pandas object may propagate changes:

>>> s1 = pd.Series([1, 2])
>>> s2 = s1.astype('int64', copy=False)
>>> s2[0] = 10
>>> s1  # note that s1[0] has changed too
0    10
1     2
dtype: int64

Create a series of dates:

>>> ser_date = pd.Series(pd.date_range('20200101', periods=3))
>>> ser_date
0   2020-01-01
1   2020-01-02
2   2020-01-03
dtype: datetime64[ns]

Datetimes are localized to UTC first before converting to the specified timezone:

>>> ser_date.astype('datetime64[ns, US/Eastern]')
0   2019-12-31 19:00:00-05:00
1   2020-01-01 19:00:00-05:00
2   2020-01-02 19:00:00-05:00
dtype: datetime64[ns, US/Eastern]
property at

Access a single value for a row/column label pair.

Similar to loc, in that both provide label-based lookups. Use at if you only need to get or set a single value in a DataFrame or Series.

Raises

KeyError – If ‘label’ does not exist in DataFrame.

See also

DataFrame.iat

Access a single value for a row/column pair by integer position.

DataFrame.loc

Access a group of rows and columns by label(s).

Series.at

Access a single value using a label.

Examples

>>> df = pd.DataFrame([[0, 2, 3], [0, 4, 1], [10, 20, 30]],
...                   index=[4, 5, 6], columns=['A', 'B', 'C'])
>>> df
    A   B   C
4   0   2   3
5   0   4   1
6  10  20  30

Get value at specified row/column pair

>>> df.at[4, 'B']
2

Set value at specified row/column pair

>>> df.at[4, 'B'] = 10
>>> df.at[4, 'B']
10

Get value within a Series

>>> df.loc[5].at['B']
4
at_time(time, asof: bool = False, axis=None) → FrameOrSeries

Select values at particular time of day (e.g., 9:30AM).

Parameters
  • time (datetime.time or str) –

  • axis ({0 or 'index', 1 or 'columns'}, default 0) –

    New in version 0.24.0.

Returns

Return type

Series or DataFrame

Raises

TypeError – If the index is not a DatetimeIndex

See also

between_time()

Select values between particular times of the day.

first()

Select initial periods of time series based on a date offset.

last()

Select final periods of time series based on a date offset.

DatetimeIndex.indexer_at_time()

Get just the index locations for values at particular time of the day.

Examples

>>> i = pd.date_range('2018-04-09', periods=4, freq='12H')
>>> ts = pd.DataFrame({'A': [1, 2, 3, 4]}, index=i)
>>> ts
                     A
2018-04-09 00:00:00  1
2018-04-09 12:00:00  2
2018-04-10 00:00:00  3
2018-04-10 12:00:00  4
>>> ts.at_time('12:00')
                     A
2018-04-09 12:00:00  2
2018-04-10 12:00:00  4
property attrs

Dictionary of global attributes on this object.

Warning

attrs is experimental and may change without warning.

property axes

Return a list representing the axes of the DataFrame.

It has the row axis labels and column axis labels as the only members. They are returned in that order.

Examples

>>> df = pd.DataFrame({'col1': [1, 2], 'col2': [3, 4]})
>>> df.axes
[RangeIndex(start=0, stop=2, step=1), Index(['col1', 'col2'],
dtype='object')]
backfill(axis=None, inplace: bool = False, limit=None, downcast=None) → Optional[FrameOrSeries]

Synonym for DataFrame.fillna() with method='bfill'.

Returns

Object with missing values filled or None if inplace=True.

Return type

{klass} or None

between_time(start_time, end_time, include_start: bool = True, include_end: bool = True, axis=None) → FrameOrSeries

Select values between particular times of the day (e.g., 9:00-9:30 AM).

By setting start_time to be later than end_time, you can get the times that are not between the two times.

Parameters
  • start_time (datetime.time or str) – Initial time as a time filter limit.

  • end_time (datetime.time or str) – End time as a time filter limit.

  • include_start (bool, default True) – Whether the start time needs to be included in the result.

  • include_end (bool, default True) – Whether the end time needs to be included in the result.

  • axis ({0 or 'index', 1 or 'columns'}, default 0) –

    Determine range time on index or columns value.

    New in version 0.24.0.

Returns

Data from the original object filtered to the specified dates range.

Return type

Series or DataFrame

Raises

TypeError – If the index is not a DatetimeIndex

See also

at_time()

Select values at a particular time of the day.

first()

Select initial periods of time series based on a date offset.

last()

Select final periods of time series based on a date offset.

DatetimeIndex.indexer_between_time()

Get just the index locations for values between particular times of the day.

Examples

>>> i = pd.date_range('2018-04-09', periods=4, freq='1D20min')
>>> ts = pd.DataFrame({'A': [1, 2, 3, 4]}, index=i)
>>> ts
                     A
2018-04-09 00:00:00  1
2018-04-10 00:20:00  2
2018-04-11 00:40:00  3
2018-04-12 01:00:00  4
>>> ts.between_time('0:15', '0:45')
                     A
2018-04-10 00:20:00  2
2018-04-11 00:40:00  3

You get the times that are not between two times by setting start_time later than end_time:

>>> ts.between_time('0:45', '0:15')
                     A
2018-04-09 00:00:00  1
2018-04-12 01:00:00  4
bfill(axis=None, inplace: bool = False, limit=None, downcast=None) → Optional[FrameOrSeries]

Synonym for DataFrame.fillna() with method='bfill'.

Returns

Object with missing values filled or None if inplace=True.

Return type

{klass} or None

bool()

Return the bool of a single element Series or DataFrame.

This must be a boolean scalar value, either True or False. It will raise a ValueError if the Series or DataFrame does not have exactly 1 element, or that element is not boolean (integer values 0 and 1 will also raise an exception).

Returns

The value in the Series or DataFrame.

Return type

bool

See also

Series.astype()

Change the data type of a Series, including to boolean.

DataFrame.astype()

Change the data type of a DataFrame, including to boolean.

numpy.bool_()

NumPy boolean data type, used by pandas for boolean values.

Examples

The method will only work for single element objects with a boolean value:

>>> pd.Series([True]).bool()
True
>>> pd.Series([False]).bool()
False
>>> pd.DataFrame({'col': [True]}).bool()
True
>>> pd.DataFrame({'col': [False]}).bool()
False
boxplot(column=None, by=None, ax=None, fontsize=None, rot=0, grid=True, figsize=None, layout=None, return_type=None, backend=None, **kwargs)

Make a box plot from DataFrame columns.

Make a box-and-whisker plot from DataFrame columns, optionally grouped by some other columns. A box plot is a method for graphically depicting groups of numerical data through their quartiles. The box extends from the Q1 to Q3 quartile values of the data, with a line at the median (Q2). The whiskers extend from the edges of box to show the range of the data. By default, they extend no more than 1.5 * IQR (IQR = Q3 - Q1) from the edges of the box, ending at the farthest data point within that interval. Outliers are plotted as separate dots.

For further details see Wikipedia’s entry for boxplot.

Parameters
  • column (str or list of str, optional) – Column name or list of names, or vector. Can be any valid input to pandas.DataFrame.groupby().

  • by (str or array-like, optional) – Column in the DataFrame to pandas.DataFrame.groupby(). One box-plot will be done per value of columns in by.

  • ax (object of class matplotlib.axes.Axes, optional) – The matplotlib axes to be used by boxplot.

  • fontsize (float or str) – Tick label font size in points or as a string (e.g., large).

  • rot (int or float, default 0) – The rotation angle of labels (in degrees) with respect to the screen coordinate system.

  • grid (bool, default True) – Setting this to True will show the grid.

  • figsize (A tuple (width, height) in inches) – The size of the figure to create in matplotlib.

  • layout (tuple (rows, columns), optional) – For example, (3, 5) will display the subplots using 3 columns and 5 rows, starting from the top-left.

  • return_type ({'axes', 'dict', 'both'} or None, default 'axes') –

    The kind of object to return. The default is axes.

    • ’axes’ returns the matplotlib axes the boxplot is drawn on.

    • ’dict’ returns a dictionary whose values are the matplotlib Lines of the boxplot.

    • ’both’ returns a namedtuple with the axes and dict.

    • when grouping with by, a Series mapping columns to return_type is returned.

      If return_type is None, a NumPy array of axes with the same shape as layout is returned.

  • backend (str, default None) –

    Backend to use instead of the backend specified in the option plotting.backend. For instance, ‘matplotlib’. Alternatively, to specify the plotting.backend for the whole session, set pd.options.plotting.backend.

    New in version 1.0.0.

  • **kwargs – All other plotting keyword arguments to be passed to matplotlib.pyplot.boxplot().

Returns

See Notes.

Return type

result

See also

Series.plot.hist()

Make a histogram.

matplotlib.pyplot.boxplot()

Matplotlib equivalent plot.

Notes

The return type depends on the return_type parameter:

  • ‘axes’ : object of class matplotlib.axes.Axes

  • ‘dict’ : dict of matplotlib.lines.Line2D objects

  • ‘both’ : a namedtuple with structure (ax, lines)

For data grouped with by, return a Series of the above or a numpy array:

  • Series

  • array (for return_type = None)

Use return_type='dict' when you want to tweak the appearance of the lines after plotting. In this case a dict containing the Lines making up the boxes, caps, fliers, medians, and whiskers is returned.

Examples

Boxplots can be created for every column in the dataframe by df.boxplot() or indicating the columns to be used:

Boxplots of variables distributions grouped by the values of a third variable can be created using the option by. For instance:

A list of strings (i.e. ['X', 'Y']) can be passed to boxplot in order to group the data by combination of the variables in the x-axis:

The layout of boxplot can be adjusted giving a tuple to layout:

Additional formatting can be done to the boxplot, like suppressing the grid (grid=False), rotating the labels in the x-axis (i.e. rot=45) or changing the fontsize (i.e. fontsize=15):

The parameter return_type can be used to select the type of element returned by boxplot. When return_type='axes' is selected, the matplotlib axes on which the boxplot is drawn are returned:

>>> boxplot = df.boxplot(column=['Col1', 'Col2'], return_type='axes')
>>> type(boxplot)
<class 'matplotlib.axes._subplots.AxesSubplot'>

When grouping with by, a Series mapping columns to return_type is returned:

>>> boxplot = df.boxplot(column=['Col1', 'Col2'], by='X',
...                      return_type='axes')
>>> type(boxplot)
<class 'pandas.core.series.Series'>

If return_type is None, a NumPy array of axes with the same shape as layout is returned:

>>> boxplot = df.boxplot(column=['Col1', 'Col2'], by='X',
...                      return_type=None)
>>> type(boxplot)
<class 'numpy.ndarray'>
clip(lower=None, upper=None, axis=None, inplace: bool = False, *args, **kwargs) → FrameOrSeries

Trim values at input threshold(s).

Assigns values outside boundary to boundary values. Thresholds can be singular values or array like, and in the latter case the clipping is performed element-wise in the specified axis.

Parameters
  • lower (float or array_like, default None) – Minimum threshold value. All values below this threshold will be set to it.

  • upper (float or array_like, default None) – Maximum threshold value. All values above this threshold will be set to it.

  • axis (int or str axis name, optional) – Align object with lower and upper along the given axis.

  • inplace (bool, default False) – Whether to perform the operation in place on the data.

  • **kwargs (*args,) –

    Additional keywords have no effect but might be accepted for compatibility with numpy.

Returns

Same type as calling object with the values outside the clip boundaries replaced.

Return type

Series or DataFrame

See also

Series.clip()

Trim values at input threshold in series.

DataFrame.clip()

Trim values at input threshold in dataframe.

numpy.clip()

Clip (limit) the values in an array.

Examples

>>> data = {'col_0': [9, -3, 0, -1, 5], 'col_1': [-2, -7, 6, 8, -5]}
>>> df = pd.DataFrame(data)
>>> df
   col_0  col_1
0      9     -2
1     -3     -7
2      0      6
3     -1      8
4      5     -5

Clips per column using lower and upper thresholds:

>>> df.clip(-4, 6)
   col_0  col_1
0      6     -2
1     -3     -4
2      0      6
3     -1      6
4      5     -4

Clips using specific lower and upper thresholds per column element:

>>> t = pd.Series([2, -4, -1, 6, 3])
>>> t
0    2
1   -4
2   -1
3    6
4    3
dtype: int64
>>> df.clip(t, t + 4, axis=0)
   col_0  col_1
0      6      2
1     -3     -4
2      0      3
3      6      8
4      5      3
columns

The column labels of the DataFrame.

combine(other: pandas.core.frame.DataFrame, func, fill_value=None, overwrite=True) → pandas.core.frame.DataFrame

Perform column-wise combine with another DataFrame.

Combines a DataFrame with other DataFrame using func to element-wise combine columns. The row and column indexes of the resulting DataFrame will be the union of the two.

Parameters
  • other (DataFrame) – The DataFrame to merge column-wise.

  • func (function) – Function that takes two series as inputs and return a Series or a scalar. Used to merge the two dataframes column by columns.

  • fill_value (scalar value, default None) – The value to fill NaNs with prior to passing any column to the merge func.

  • overwrite (bool, default True) – If True, columns in self that do not exist in other will be overwritten with NaNs.

Returns

Combination of the provided DataFrames.

Return type

DataFrame

See also

DataFrame.combine_first()

Combine two DataFrame objects and default to non-null values in frame calling the method.

Examples

Combine using a simple function that chooses the smaller column.

>>> df1 = pd.DataFrame({'A': [0, 0], 'B': [4, 4]})
>>> df2 = pd.DataFrame({'A': [1, 1], 'B': [3, 3]})
>>> take_smaller = lambda s1, s2: s1 if s1.sum() < s2.sum() else s2
>>> df1.combine(df2, take_smaller)
   A  B
0  0  3
1  0  3

Example using a true element-wise combine function.

>>> df1 = pd.DataFrame({'A': [5, 0], 'B': [2, 4]})
>>> df2 = pd.DataFrame({'A': [1, 1], 'B': [3, 3]})
>>> df1.combine(df2, np.minimum)
   A  B
0  1  2
1  0  3

Using fill_value fills Nones prior to passing the column to the merge function.

>>> df1 = pd.DataFrame({'A': [0, 0], 'B': [None, 4]})
>>> df2 = pd.DataFrame({'A': [1, 1], 'B': [3, 3]})
>>> df1.combine(df2, take_smaller, fill_value=-5)
   A    B
0  0 -5.0
1  0  4.0

However, if the same element in both dataframes is None, that None is preserved

>>> df1 = pd.DataFrame({'A': [0, 0], 'B': [None, 4]})
>>> df2 = pd.DataFrame({'A': [1, 1], 'B': [None, 3]})
>>> df1.combine(df2, take_smaller, fill_value=-5)
    A    B
0  0 -5.0
1  0  3.0

Example that demonstrates the use of overwrite and behavior when the axis differ between the dataframes.

>>> df1 = pd.DataFrame({'A': [0, 0], 'B': [4, 4]})
>>> df2 = pd.DataFrame({'B': [3, 3], 'C': [-10, 1], }, index=[1, 2])
>>> df1.combine(df2, take_smaller)
     A    B     C
0  NaN  NaN   NaN
1  NaN  3.0 -10.0
2  NaN  3.0   1.0
>>> df1.combine(df2, take_smaller, overwrite=False)
     A    B     C
0  0.0  NaN   NaN
1  0.0  3.0 -10.0
2  NaN  3.0   1.0

Demonstrating the preference of the passed in dataframe.

>>> df2 = pd.DataFrame({'B': [3, 3], 'C': [1, 1], }, index=[1, 2])
>>> df2.combine(df1, take_smaller)
   A    B   C
0  0.0  NaN NaN
1  0.0  3.0 NaN
2  NaN  3.0 NaN
>>> df2.combine(df1, take_smaller, overwrite=False)
     A    B   C
0  0.0  NaN NaN
1  0.0  3.0 1.0
2  NaN  3.0 1.0
combine_first(other: pandas.core.frame.DataFrame) → pandas.core.frame.DataFrame

Update null elements with value in the same location in other.

Combine two DataFrame objects by filling null values in one DataFrame with non-null values from other DataFrame. The row and column indexes of the resulting DataFrame will be the union of the two.

Parameters

other (DataFrame) – Provided DataFrame to use to fill null values.

Returns

Return type

DataFrame

See also

DataFrame.combine()

Perform series-wise operation on two DataFrames using a given function.

Examples

>>> df1 = pd.DataFrame({'A': [None, 0], 'B': [None, 4]})
>>> df2 = pd.DataFrame({'A': [1, 1], 'B': [3, 3]})
>>> df1.combine_first(df2)
     A    B
0  1.0  3.0
1  0.0  4.0

Null values still persist if the location of that null value does not exist in other

>>> df1 = pd.DataFrame({'A': [None, 0], 'B': [4, None]})
>>> df2 = pd.DataFrame({'B': [3, 3], 'C': [1, 1]}, index=[1, 2])
>>> df1.combine_first(df2)
     A    B    C
0  NaN  4.0  NaN
1  0.0  3.0  1.0
2  NaN  3.0  1.0
compare(other: pandas.core.frame.DataFrame, align_axis: Union[str, int] = 1, keep_shape: bool = False, keep_equal: bool = False) → pandas.core.frame.DataFrame

Compare to another DataFrame and show the differences.

New in version 1.1.0.

Parameters
  • other (DataFrame) – Object to compare with.

  • align_axis ({0 or 'index', 1 or 'columns'}, default 1) –

    Determine which axis to align the comparison on.

    • 0, or ‘index’Resulting differences are stacked vertically

      with rows drawn alternately from self and other.

    • 1, or ‘columns’Resulting differences are aligned horizontally

      with columns drawn alternately from self and other.

  • keep_shape (bool, default False) – If true, all rows and columns are kept. Otherwise, only the ones with different values are kept.

  • keep_equal (bool, default False) – If true, the result keeps values that are equal. Otherwise, equal values are shown as NaNs.

Returns

DataFrame that shows the differences stacked side by side.

The resulting index will be a MultiIndex with ‘self’ and ‘other’ stacked alternately at the inner level.

Return type

DataFrame

See also

Series.compare()

Compare with another Series and show differences.

Notes

Matching NaNs will not appear as a difference.

Examples

>>> df = pd.DataFrame(
...     {
...         "col1": ["a", "a", "b", "b", "a"],
...         "col2": [1.0, 2.0, 3.0, np.nan, 5.0],
...         "col3": [1.0, 2.0, 3.0, 4.0, 5.0]
...     },
...     columns=["col1", "col2", "col3"],
... )
>>> df
  col1  col2  col3
0    a   1.0   1.0
1    a   2.0   2.0
2    b   3.0   3.0
3    b   NaN   4.0
4    a   5.0   5.0
>>> df2 = df.copy()
>>> df2.loc[0, 'col1'] = 'c'
>>> df2.loc[2, 'col3'] = 4.0
>>> df2
  col1  col2  col3
0    c   1.0   1.0
1    a   2.0   2.0
2    b   3.0   4.0
3    b   NaN   4.0
4    a   5.0   5.0

Align the differences on columns

>>> df.compare(df2)
  col1       col3
  self other self other
0    a     c  NaN   NaN
2  NaN   NaN  3.0   4.0

Stack the differences on rows

>>> df.compare(df2, align_axis=0)
        col1  col3
0 self     a   NaN
  other    c   NaN
2 self   NaN   3.0
  other  NaN   4.0

Keep the equal values

>>> df.compare(df2, keep_equal=True)
  col1       col3
  self other self other
0    a     c  1.0   1.0
2    b     b  3.0   4.0

Keep all original rows and columns

>>> df.compare(df2, keep_shape=True)
  col1       col2       col3
  self other self other self other
0    a     c  NaN   NaN  NaN   NaN
1  NaN   NaN  NaN   NaN  NaN   NaN
2  NaN   NaN  NaN   NaN  3.0   4.0
3  NaN   NaN  NaN   NaN  NaN   NaN
4  NaN   NaN  NaN   NaN  NaN   NaN

Keep all original rows and columns and also all original values

>>> df.compare(df2, keep_shape=True, keep_equal=True)
  col1       col2       col3
  self other self other self other
0    a     c  1.0   1.0  1.0   1.0
1    a     a  2.0   2.0  2.0   2.0
2    b     b  3.0   3.0  3.0   4.0
3    b     b  NaN   NaN  4.0   4.0
4    a     a  5.0   5.0  5.0   5.0
convert_dtypes(infer_objects: bool = True, convert_string: bool = True, convert_integer: bool = True, convert_boolean: bool = True) → FrameOrSeries

Convert columns to best possible dtypes using dtypes supporting pd.NA.

New in version 1.0.0.

Parameters
  • infer_objects (bool, default True) – Whether object dtypes should be converted to the best possible types.

  • convert_string (bool, default True) – Whether object dtypes should be converted to StringDtype().

  • convert_integer (bool, default True) – Whether, if possible, conversion can be done to integer extension types.

  • convert_boolean (bool, defaults True) – Whether object dtypes should be converted to BooleanDtypes().

Returns

Copy of input object with new dtype.

Return type

Series or DataFrame

See also

infer_objects()

Infer dtypes of objects.

to_datetime()

Convert argument to datetime.

to_timedelta()

Convert argument to timedelta.

to_numeric()

Convert argument to a numeric type.

Notes

By default, convert_dtypes will attempt to convert a Series (or each Series in a DataFrame) to dtypes that support pd.NA. By using the options convert_string, convert_integer, and convert_boolean, it is possible to turn off individual conversions to StringDtype, the integer extension types or BooleanDtype, respectively.

For object-dtyped columns, if infer_objects is True, use the inference rules as during normal Series/DataFrame construction. Then, if possible, convert to StringDtype, BooleanDtype or an appropriate integer extension type, otherwise leave as object.

If the dtype is integer, convert to an appropriate integer extension type.

If the dtype is numeric, and consists of all integers, convert to an appropriate integer extension type.

In the future, as new dtypes are added that support pd.NA, the results of this method will change to support those new dtypes.

Examples

>>> df = pd.DataFrame(
...     {
...         "a": pd.Series([1, 2, 3], dtype=np.dtype("int32")),
...         "b": pd.Series(["x", "y", "z"], dtype=np.dtype("O")),
...         "c": pd.Series([True, False, np.nan], dtype=np.dtype("O")),
...         "d": pd.Series(["h", "i", np.nan], dtype=np.dtype("O")),
...         "e": pd.Series([10, np.nan, 20], dtype=np.dtype("float")),
...         "f": pd.Series([np.nan, 100.5, 200], dtype=np.dtype("float")),
...     }
... )

Start with a DataFrame with default dtypes.

>>> df
   a  b      c    d     e      f
0  1  x   True    h  10.0    NaN
1  2  y  False    i   NaN  100.5
2  3  z    NaN  NaN  20.0  200.0
>>> df.dtypes
a      int32
b     object
c     object
d     object
e    float64
f    float64
dtype: object

Convert the DataFrame to use best possible dtypes.

>>> dfn = df.convert_dtypes()
>>> dfn
   a  b      c     d     e      f
0  1  x   True     h    10    NaN
1  2  y  False     i  <NA>  100.5
2  3  z   <NA>  <NA>    20  200.0
>>> dfn.dtypes
a      Int32
b     string
c    boolean
d     string
e      Int64
f    float64
dtype: object

Start with a Series of strings and missing data represented by np.nan.

>>> s = pd.Series(["a", "b", np.nan])
>>> s
0      a
1      b
2    NaN
dtype: object

Obtain a Series with dtype StringDtype.

>>> s.convert_dtypes()
0       a
1       b
2    <NA>
dtype: string
copy(deep: bool = True) → FrameOrSeries

Make a copy of this object’s indices and data.

When deep=True (default), a new object will be created with a copy of the calling object’s data and indices. Modifications to the data or indices of the copy will not be reflected in the original object (see notes below).

When deep=False, a new object will be created without copying the calling object’s data or index (only references to the data and index are copied). Any changes to the data of the original will be reflected in the shallow copy (and vice versa).

Parameters

deep (bool, default True) – Make a deep copy, including a copy of the data and the indices. With deep=False neither the indices nor the data are copied.

Returns

copy – Object type matches caller.

Return type

Series or DataFrame

Notes

When deep=True, data is copied but actual Python objects will not be copied recursively, only the reference to the object. This is in contrast to copy.deepcopy in the Standard Library, which recursively copies object data (see examples below).

While Index objects are copied when deep=True, the underlying numpy array is not copied for performance reasons. Since Index is immutable, the underlying data can be safely shared and a copy is not needed.

Examples

>>> s = pd.Series([1, 2], index=["a", "b"])
>>> s
a    1
b    2
dtype: int64
>>> s_copy = s.copy()
>>> s_copy
a    1
b    2
dtype: int64

Shallow copy versus default (deep) copy:

>>> s = pd.Series([1, 2], index=["a", "b"])
>>> deep = s.copy()
>>> shallow = s.copy(deep=False)

Shallow copy shares data and index with original.

>>> s is shallow
False
>>> s.values is shallow.values and s.index is shallow.index
True

Deep copy has own copy of data and index.

>>> s is deep
False
>>> s.values is deep.values or s.index is deep.index
False

Updates to the data shared by shallow copy and original is reflected in both; deep copy remains unchanged.

>>> s[0] = 3
>>> shallow[1] = 4
>>> s
a    3
b    4
dtype: int64
>>> shallow
a    3
b    4
dtype: int64
>>> deep
a    1
b    2
dtype: int64

Note that when copying an object containing Python objects, a deep copy will copy the data, but will not do so recursively. Updating a nested data object will be reflected in the deep copy.

>>> s = pd.Series([[1, 2], [3, 4]])
>>> deep = s.copy()
>>> s[0][0] = 10
>>> s
0    [10, 2]
1     [3, 4]
dtype: object
>>> deep
0    [10, 2]
1     [3, 4]
dtype: object
corr(method='pearson', min_periods=1) → pandas.core.frame.DataFrame

Compute pairwise correlation of columns, excluding NA/null values.

Parameters
  • method ({'pearson', 'kendall', 'spearman'} or callable) –

    Method of correlation:

    • pearson : standard correlation coefficient

    • kendall : Kendall Tau correlation coefficient

    • spearman : Spearman rank correlation

    • callable: callable with input two 1d ndarrays

      and returning a float. Note that the returned matrix from corr will have 1 along the diagonals and will be symmetric regardless of the callable’s behavior.

      New in version 0.24.0.

  • min_periods (int, optional) – Minimum number of observations required per pair of columns to have a valid result. Currently only available for Pearson and Spearman correlation.

Returns

Correlation matrix.

Return type

DataFrame

See also

DataFrame.corrwith()

Compute pairwise correlation with another DataFrame or Series.

Series.corr()

Compute the correlation between two Series.

Examples

>>> def histogram_intersection(a, b):
...     v = np.minimum(a, b).sum().round(decimals=1)
...     return v
>>> df = pd.DataFrame([(.2, .3), (.0, .6), (.6, .0), (.2, .1)],
...                   columns=['dogs', 'cats'])
>>> df.corr(method=histogram_intersection)
      dogs  cats
dogs   1.0   0.3
cats   0.3   1.0
corrwith(other, axis=0, drop=False, method='pearson') → pandas.core.series.Series

Compute pairwise correlation.

Pairwise correlation is computed between rows or columns of DataFrame with rows or columns of Series or DataFrame. DataFrames are first aligned along both axes before computing the correlations.

Parameters
  • other (DataFrame, Series) – Object with which to compute correlations.

  • axis ({0 or 'index', 1 or 'columns'}, default 0) – The axis to use. 0 or ‘index’ to compute column-wise, 1 or ‘columns’ for row-wise.

  • drop (bool, default False) – Drop missing indices from result.

  • method ({'pearson', 'kendall', 'spearman'} or callable) –

    Method of correlation:

    • pearson : standard correlation coefficient

    • kendall : Kendall Tau correlation coefficient

    • spearman : Spearman rank correlation

    • callable: callable with input two 1d ndarrays

      and returning a float.

    New in version 0.24.0.

Returns

Pairwise correlations.

Return type

Series

See also

DataFrame.corr()

Compute pairwise correlation of columns.

count(axis=0, level=None, numeric_only=False)

Count non-NA cells for each column or row.

The values None, NaN, NaT, and optionally numpy.inf (depending on pandas.options.mode.use_inf_as_na) are considered NA.

Parameters
  • axis ({0 or 'index', 1 or 'columns'}, default 0) – If 0 or ‘index’ counts are generated for each column. If 1 or ‘columns’ counts are generated for each row.

  • level (int or str, optional) – If the axis is a MultiIndex (hierarchical), count along a particular level, collapsing into a DataFrame. A str specifies the level name.

  • numeric_only (bool, default False) – Include only float, int or boolean data.

Returns

For each column/row the number of non-NA/null entries. If level is specified returns a DataFrame.

Return type

Series or DataFrame

See also

Series.count()

Number of non-NA elements in a Series.

DataFrame.shape()

Number of DataFrame rows and columns (including NA elements).

DataFrame.isna()

Boolean same-sized DataFrame showing places of NA elements.

Examples

Constructing DataFrame from a dictionary:

>>> df = pd.DataFrame({"Person":
...                    ["John", "Myla", "Lewis", "John", "Myla"],
...                    "Age": [24., np.nan, 21., 33, 26],
...                    "Single": [False, True, True, True, False]})
>>> df
   Person   Age  Single
0    John  24.0   False
1    Myla   NaN    True
2   Lewis  21.0    True
3    John  33.0    True
4    Myla  26.0   False

Notice the uncounted NA values:

>>> df.count()
Person    5
Age       4
Single    5
dtype: int64

Counts for each row:

>>> df.count(axis='columns')
0    3
1    2
2    3
3    3
4    3
dtype: int64

Counts for one level of a MultiIndex:

>>> df.set_index(["Person", "Single"]).count(level="Person")
        Age
Person
John      2
Lewis     1
Myla      1
cov(min_periods: Optional[int] = None, ddof: Optional[int] = 1) → pandas.core.frame.DataFrame

Compute pairwise covariance of columns, excluding NA/null values.

Compute the pairwise covariance among the series of a DataFrame. The returned data frame is the covariance matrix of the columns of the DataFrame.

Both NA and null values are automatically excluded from the calculation. (See the note below about bias from missing values.) A threshold can be set for the minimum number of observations for each value created. Comparisons with observations below this threshold will be returned as NaN.

This method is generally used for the analysis of time series data to understand the relationship between different measures across time.

Parameters
  • min_periods (int, optional) – Minimum number of observations required per pair of columns to have a valid result.

  • ddof (int, default 1) –

    Delta degrees of freedom. The divisor used in calculations is N - ddof, where N represents the number of elements.

    New in version 1.1.0.

Returns

The covariance matrix of the series of the DataFrame.

Return type

DataFrame

See also

Series.cov()

Compute covariance with another Series.

core.window.ExponentialMovingWindow.cov()

Exponential weighted sample covariance.

core.window.Expanding.cov()

Expanding sample covariance.

core.window.Rolling.cov()

Rolling sample covariance.

Notes

Returns the covariance matrix of the DataFrame’s time series. The covariance is normalized by N-ddof.

For DataFrames that have Series that are missing data (assuming that data is missing at random) the returned covariance matrix will be an unbiased estimate of the variance and covariance between the member Series.

However, for many applications this estimate may not be acceptable because the estimate covariance matrix is not guaranteed to be positive semi-definite. This could lead to estimate correlations having absolute values which are greater than one, and/or a non-invertible covariance matrix. See Estimation of covariance matrices for more details.

Examples

>>> df = pd.DataFrame([(1, 2), (0, 3), (2, 0), (1, 1)],
...                   columns=['dogs', 'cats'])
>>> df.cov()
          dogs      cats
dogs  0.666667 -1.000000
cats -1.000000  1.666667
>>> np.random.seed(42)
>>> df = pd.DataFrame(np.random.randn(1000, 5),
...                   columns=['a', 'b', 'c', 'd', 'e'])
>>> df.cov()
          a         b         c         d         e
a  0.998438 -0.020161  0.059277 -0.008943  0.014144
b -0.020161  1.059352 -0.008543 -0.024738  0.009826
c  0.059277 -0.008543  1.010670 -0.001486 -0.000271
d -0.008943 -0.024738 -0.001486  0.921297 -0.013692
e  0.014144  0.009826 -0.000271 -0.013692  0.977795

Minimum number of periods

This method also supports an optional min_periods keyword that specifies the required minimum number of non-NA observations for each column pair in order to have a valid result:

>>> np.random.seed(42)
>>> df = pd.DataFrame(np.random.randn(20, 3),
...                   columns=['a', 'b', 'c'])
>>> df.loc[df.index[:5], 'a'] = np.nan
>>> df.loc[df.index[5:10], 'b'] = np.nan
>>> df.cov(min_periods=12)
          a         b         c
a  0.316741       NaN -0.150812
b       NaN  1.248003  0.191417
c -0.150812  0.191417  0.895202
cummax(axis=None, skipna=True, *args, **kwargs)

Return cumulative maximum over a DataFrame or Series axis.

Returns a DataFrame or Series of the same size containing the cumulative maximum.

Parameters
  • axis ({0 or 'index', 1 or 'columns'}, default 0) – The index or the name of the axis. 0 is equivalent to None or ‘index’.

  • skipna (bool, default True) – Exclude NA/null values. If an entire row/column is NA, the result will be NA.

  • **kwargs (*args,) –

    Additional keywords have no effect but might be accepted for compatibility with NumPy.

Returns

Return cumulative maximum of Series or DataFrame.

Return type

Series or DataFrame

See also

core.window.Expanding.max()

Similar functionality but ignores NaN values.

DataFrame.max()

Return the maximum over DataFrame axis.

DataFrame.cummax()

Return cumulative maximum over DataFrame axis.

DataFrame.cummin()

Return cumulative minimum over DataFrame axis.

DataFrame.cumsum()

Return cumulative sum over DataFrame axis.

DataFrame.cumprod()

Return cumulative product over DataFrame axis.

Examples

Series

>>> s = pd.Series([2, np.nan, 5, -1, 0])
>>> s
0    2.0
1    NaN
2    5.0
3   -1.0
4    0.0
dtype: float64

By default, NA values are ignored.

>>> s.cummax()
0    2.0
1    NaN
2    5.0
3    5.0
4    5.0
dtype: float64

To include NA values in the operation, use skipna=False

>>> s.cummax(skipna=False)
0    2.0
1    NaN
2    NaN
3    NaN
4    NaN
dtype: float64

DataFrame

>>> df = pd.DataFrame([[2.0, 1.0],
...                    [3.0, np.nan],
...                    [1.0, 0.0]],
...                    columns=list('AB'))
>>> df
     A    B
0  2.0  1.0
1  3.0  NaN
2  1.0  0.0

By default, iterates over rows and finds the maximum in each column. This is equivalent to axis=None or axis='index'.

>>> df.cummax()
     A    B
0  2.0  1.0
1  3.0  NaN
2  3.0  1.0

To iterate over columns and find the maximum in each row, use axis=1

>>> df.cummax(axis=1)
     A    B
0  2.0  2.0
1  3.0  NaN
2  1.0  1.0
cummin(axis=None, skipna=True, *args, **kwargs)

Return cumulative minimum over a DataFrame or Series axis.

Returns a DataFrame or Series of the same size containing the cumulative minimum.

Parameters
  • axis ({0 or 'index', 1 or 'columns'}, default 0) – The index or the name of the axis. 0 is equivalent to None or ‘index’.

  • skipna (bool, default True) – Exclude NA/null values. If an entire row/column is NA, the result will be NA.

  • **kwargs (*args,) –

    Additional keywords have no effect but might be accepted for compatibility with NumPy.

Returns

Return cumulative minimum of Series or DataFrame.

Return type

Series or DataFrame

See also

core.window.Expanding.min()

Similar functionality but ignores NaN values.

DataFrame.min()

Return the minimum over DataFrame axis.

DataFrame.cummax()

Return cumulative maximum over DataFrame axis.

DataFrame.cummin()

Return cumulative minimum over DataFrame axis.

DataFrame.cumsum()

Return cumulative sum over DataFrame axis.

DataFrame.cumprod()

Return cumulative product over DataFrame axis.

Examples

Series

>>> s = pd.Series([2, np.nan, 5, -1, 0])
>>> s
0    2.0
1    NaN
2    5.0
3   -1.0
4    0.0
dtype: float64

By default, NA values are ignored.

>>> s.cummin()
0    2.0
1    NaN
2    2.0
3   -1.0
4   -1.0
dtype: float64

To include NA values in the operation, use skipna=False

>>> s.cummin(skipna=False)
0    2.0
1    NaN
2    NaN
3    NaN
4    NaN
dtype: float64

DataFrame

>>> df = pd.DataFrame([[2.0, 1.0],
...                    [3.0, np.nan],
...                    [1.0, 0.0]],
...                    columns=list('AB'))
>>> df
     A    B
0  2.0  1.0
1  3.0  NaN
2  1.0  0.0

By default, iterates over rows and finds the minimum in each column. This is equivalent to axis=None or axis='index'.

>>> df.cummin()
     A    B
0  2.0  1.0
1  2.0  NaN
2  1.0  0.0

To iterate over columns and find the minimum in each row, use axis=1

>>> df.cummin(axis=1)
     A    B
0  2.0  1.0
1  3.0  NaN
2  1.0  0.0
cumprod(axis=None, skipna=True, *args, **kwargs)

Return cumulative product over a DataFrame or Series axis.

Returns a DataFrame or Series of the same size containing the cumulative product.

Parameters
  • axis ({0 or 'index', 1 or 'columns'}, default 0) – The index or the name of the axis. 0 is equivalent to None or ‘index’.

  • skipna (bool, default True) – Exclude NA/null values. If an entire row/column is NA, the result will be NA.

  • **kwargs (*args,) –

    Additional keywords have no effect but might be accepted for compatibility with NumPy.

Returns

Return cumulative product of Series or DataFrame.

Return type

Series or DataFrame

See also

core.window.Expanding.prod()

Similar functionality but ignores NaN values.

DataFrame.prod()

Return the product over DataFrame axis.

DataFrame.cummax()

Return cumulative maximum over DataFrame axis.

DataFrame.cummin()

Return cumulative minimum over DataFrame axis.

DataFrame.cumsum()

Return cumulative sum over DataFrame axis.

DataFrame.cumprod()

Return cumulative product over DataFrame axis.

Examples

Series

>>> s = pd.Series([2, np.nan, 5, -1, 0])
>>> s
0    2.0
1    NaN
2    5.0
3   -1.0
4    0.0
dtype: float64

By default, NA values are ignored.

>>> s.cumprod()
0     2.0
1     NaN
2    10.0
3   -10.0
4    -0.0
dtype: float64

To include NA values in the operation, use skipna=False

>>> s.cumprod(skipna=False)
0    2.0
1    NaN
2    NaN
3    NaN
4    NaN
dtype: float64

DataFrame

>>> df = pd.DataFrame([[2.0, 1.0],
...                    [3.0, np.nan],
...                    [1.0, 0.0]],
...                    columns=list('AB'))
>>> df
     A    B
0  2.0  1.0
1  3.0  NaN
2  1.0  0.0

By default, iterates over rows and finds the product in each column. This is equivalent to axis=None or axis='index'.

>>> df.cumprod()
     A    B
0  2.0  1.0
1  6.0  NaN
2  6.0  0.0

To iterate over columns and find the product in each row, use axis=1

>>> df.cumprod(axis=1)
     A    B
0  2.0  2.0
1  3.0  NaN
2  1.0  0.0
cumsum(axis=None, skipna=True, *args, **kwargs)

Return cumulative sum over a DataFrame or Series axis.

Returns a DataFrame or Series of the same size containing the cumulative sum.

Parameters
  • axis ({0 or 'index', 1 or 'columns'}, default 0) – The index or the name of the axis. 0 is equivalent to None or ‘index’.

  • skipna (bool, default True) – Exclude NA/null values. If an entire row/column is NA, the result will be NA.

  • **kwargs (*args,) –

    Additional keywords have no effect but might be accepted for compatibility with NumPy.

Returns

Return cumulative sum of Series or DataFrame.

Return type

Series or DataFrame

See also

core.window.Expanding.sum()

Similar functionality but ignores NaN values.

DataFrame.sum()

Return the sum over DataFrame axis.

DataFrame.cummax()

Return cumulative maximum over DataFrame axis.

DataFrame.cummin()

Return cumulative minimum over DataFrame axis.

DataFrame.cumsum()

Return cumulative sum over DataFrame axis.

DataFrame.cumprod()

Return cumulative product over DataFrame axis.

Examples

Series

>>> s = pd.Series([2, np.nan, 5, -1, 0])
>>> s
0    2.0
1    NaN
2    5.0
3   -1.0
4    0.0
dtype: float64

By default, NA values are ignored.

>>> s.cumsum()
0    2.0
1    NaN
2    7.0
3    6.0
4    6.0
dtype: float64

To include NA values in the operation, use skipna=False

>>> s.cumsum(skipna=False)
0    2.0
1    NaN
2    NaN
3    NaN
4    NaN
dtype: float64

DataFrame

>>> df = pd.DataFrame([[2.0, 1.0],
...                    [3.0, np.nan],
...                    [1.0, 0.0]],
...                    columns=list('AB'))
>>> df
     A    B
0  2.0  1.0
1  3.0  NaN
2  1.0  0.0

By default, iterates over rows and finds the sum in each column. This is equivalent to axis=None or axis='index'.

>>> df.cumsum()
     A    B
0  2.0  1.0
1  5.0  NaN
2  6.0  1.0

To iterate over columns and find the sum in each row, use axis=1

>>> df.cumsum(axis=1)
     A    B
0  2.0  3.0
1  3.0  NaN
2  1.0  1.0
describe(percentiles=None, include=None, exclude=None, datetime_is_numeric=False) → FrameOrSeries

Generate descriptive statistics.

Descriptive statistics include those that summarize the central tendency, dispersion and shape of a dataset’s distribution, excluding NaN values.

Analyzes both numeric and object series, as well as DataFrame column sets of mixed data types. The output will vary depending on what is provided. Refer to the notes below for more detail.

Parameters
  • percentiles (list-like of numbers, optional) – The percentiles to include in the output. All should fall between 0 and 1. The default is [.25, .5, .75], which returns the 25th, 50th, and 75th percentiles.

  • include ('all', list-like of dtypes or None (default), optional) –

    A white list of data types to include in the result. Ignored for Series. Here are the options:

    • ’all’ : All columns of the input will be included in the output.

    • A list-like of dtypes : Limits the results to the provided data types. To limit the result to numeric types submit numpy.number. To limit it instead to object columns submit the numpy.object data type. Strings can also be used in the style of select_dtypes (e.g. df.describe(include=['O'])). To select pandas categorical columns, use 'category'

    • None (default) : The result will include all numeric columns.

  • exclude (list-like of dtypes or None (default), optional,) –

    A black list of data types to omit from the result. Ignored for Series. Here are the options:

    • A list-like of dtypes : Excludes the provided data types from the result. To exclude numeric types submit numpy.number. To exclude object columns submit the data type numpy.object. Strings can also be used in the style of select_dtypes (e.g. df.describe(include=['O'])). To exclude pandas categorical columns, use 'category'

    • None (default) : The result will exclude nothing.

  • datetime_is_numeric (bool, default False) –

    Whether to treat datetime dtypes as numeric. This affects statistics calculated for the column. For DataFrame input, this also controls whether datetime columns are included by default.

    New in version 1.1.0.

Returns

Summary statistics of the Series or Dataframe provided.

Return type

Series or DataFrame

See also

DataFrame.count()

Count number of non-NA/null observations.

DataFrame.max()

Maximum of the values in the object.

DataFrame.min()

Minimum of the values in the object.

DataFrame.mean()

Mean of the values.

DataFrame.std()

Standard deviation of the observations.

DataFrame.select_dtypes()

Subset of a DataFrame including/excluding columns based on their dtype.

Notes

For numeric data, the result’s index will include count, mean, std, min, max as well as lower, 50 and upper percentiles. By default the lower percentile is 25 and the upper percentile is 75. The 50 percentile is the same as the median.

For object data (e.g. strings or timestamps), the result’s index will include count, unique, top, and freq. The top is the most common value. The freq is the most common value’s frequency. Timestamps also include the first and last items.

If multiple object values have the highest count, then the count and top results will be arbitrarily chosen from among those with the highest count.

For mixed data types provided via a DataFrame, the default is to return only an analysis of numeric columns. If the dataframe consists only of object and categorical data without any numeric columns, the default is to return an analysis of both the object and categorical columns. If include='all' is provided as an option, the result will include a union of attributes of each type.

The include and exclude parameters can be used to limit which columns in a DataFrame are analyzed for the output. The parameters are ignored when analyzing a Series.

Examples

Describing a numeric Series.

>>> s = pd.Series([1, 2, 3])
>>> s.describe()
count    3.0
mean     2.0
std      1.0
min      1.0
25%      1.5
50%      2.0
75%      2.5
max      3.0
dtype: float64

Describing a categorical Series.

>>> s = pd.Series(['a', 'a', 'b', 'c'])
>>> s.describe()
count     4
unique    3
top       a
freq      2
dtype: object

Describing a timestamp Series.

>>> s = pd.Series([
...   np.datetime64("2000-01-01"),
...   np.datetime64("2010-01-01"),
...   np.datetime64("2010-01-01")
... ])
>>> s.describe(datetime_is_numeric=True)
count                      3
mean     2006-09-01 08:00:00
min      2000-01-01 00:00:00
25%      2004-12-31 12:00:00
50%      2010-01-01 00:00:00
75%      2010-01-01 00:00:00
max      2010-01-01 00:00:00
dtype: object

Describing a DataFrame. By default only numeric fields are returned.

>>> df = pd.DataFrame({'categorical': pd.Categorical(['d','e','f']),
...                    'numeric': [1, 2, 3],
...                    'object': ['a', 'b', 'c']
...                   })
>>> df.describe()
       numeric
count      3.0
mean       2.0
std        1.0
min        1.0
25%        1.5
50%        2.0
75%        2.5
max        3.0

Describing all columns of a DataFrame regardless of data type.

>>> df.describe(include='all')  
       categorical  numeric object
count            3      3.0      3
unique           3      NaN      3
top              f      NaN      a
freq             1      NaN      1
mean           NaN      2.0    NaN
std            NaN      1.0    NaN
min            NaN      1.0    NaN
25%            NaN      1.5    NaN
50%            NaN      2.0    NaN
75%            NaN      2.5    NaN
max            NaN      3.0    NaN

Describing a column from a DataFrame by accessing it as an attribute.

>>> df.numeric.describe()
count    3.0
mean     2.0
std      1.0
min      1.0
25%      1.5
50%      2.0
75%      2.5
max      3.0
Name: numeric, dtype: float64

Including only numeric columns in a DataFrame description.

>>> df.describe(include=[np.number])
       numeric
count      3.0
mean       2.0
std        1.0
min        1.0
25%        1.5
50%        2.0
75%        2.5
max        3.0

Including only string columns in a DataFrame description.

>>> df.describe(include=[object])  
       object
count       3
unique      3
top         a
freq        1

Including only categorical columns from a DataFrame description.

>>> df.describe(include=['category'])
       categorical
count            3
unique           3
top              f
freq             1

Excluding numeric columns from a DataFrame description.

>>> df.describe(exclude=[np.number])  
       categorical object
count            3      3
unique           3      3
top              f      a
freq             1      1

Excluding object columns from a DataFrame description.

>>> df.describe(exclude=[object])  
       categorical  numeric
count            3      3.0
unique           3      NaN
top              f      NaN
freq             1      NaN
mean           NaN      2.0
std            NaN      1.0
min            NaN      1.0
25%            NaN      1.5
50%            NaN      2.0
75%            NaN      2.5
max            NaN      3.0
diff(periods: int = 1, axis: Union[str, int] = 0) → pandas.core.frame.DataFrame

First discrete difference of element.

Calculates the difference of a Dataframe element compared with another element in the Dataframe (default is element in previous row).

Parameters
  • periods (int, default 1) – Periods to shift for calculating difference, accepts negative values.

  • axis ({0 or 'index', 1 or 'columns'}, default 0) – Take difference over rows (0) or columns (1).

Returns

First differences of the Series.

Return type

Dataframe

See also

Dataframe.pct_change()

Percent change over given number of periods.

Dataframe.shift()

Shift index by desired number of periods with an optional time freq.

Series.diff()

First discrete difference of object.

Notes

For boolean dtypes, this uses operator.xor() rather than operator.sub(). The result is calculated according to current dtype in Dataframe, however dtype of the result is always float64.

Examples

Difference with previous row

>>> df = pd.DataFrame({'a': [1, 2, 3, 4, 5, 6],
...                    'b': [1, 1, 2, 3, 5, 8],
...                    'c': [1, 4, 9, 16, 25, 36]})
>>> df
   a  b   c
0  1  1   1
1  2  1   4
2  3  2   9
3  4  3  16
4  5  5  25
5  6  8  36
>>> df.diff()
     a    b     c
0  NaN  NaN   NaN
1  1.0  0.0   3.0
2  1.0  1.0   5.0
3  1.0  1.0   7.0
4  1.0  2.0   9.0
5  1.0  3.0  11.0

Difference with previous column

>>> df.diff(axis=1)
    a    b     c
0 NaN  0.0   0.0
1 NaN -1.0   3.0
2 NaN -1.0   7.0
3 NaN -1.0  13.0
4 NaN  0.0  20.0
5 NaN  2.0  28.0

Difference with 3rd previous row

>>> df.diff(periods=3)
     a    b     c
0  NaN  NaN   NaN
1  NaN  NaN   NaN
2  NaN  NaN   NaN
3  3.0  2.0  15.0
4  3.0  4.0  21.0
5  3.0  6.0  27.0

Difference with following row

>>> df.diff(periods=-1)
     a    b     c
0 -1.0  0.0  -3.0
1 -1.0 -1.0  -5.0
2 -1.0 -1.0  -7.0
3 -1.0 -2.0  -9.0
4 -1.0 -3.0 -11.0
5  NaN  NaN   NaN

Overflow in input dtype

>>> df = pd.DataFrame({'a': [1, 0]}, dtype=np.uint8)
>>> df.diff()
       a
0    NaN
1  255.0
div(other, axis='columns', level=None, fill_value=None)

Get Floating division of dataframe and other, element-wise (binary operator truediv).

Equivalent to dataframe / other, but with support to substitute a fill_value for missing data in one of the inputs. With reverse version, rtruediv.

Among flexible wrappers (add, sub, mul, div, mod, pow) to arithmetic operators: +, -, *, /, //, %, **.

Parameters
  • other (scalar, sequence, Series, or DataFrame) – Any single or multiple element data structure, or list-like object.

  • axis ({0 or 'index', 1 or 'columns'}) – Whether to compare by the index (0 or ‘index’) or columns (1 or ‘columns’). For Series input, axis to match Series index on.

  • level (int or label) – Broadcast across a level, matching Index values on the passed MultiIndex level.

  • fill_value (float or None, default None) – Fill existing missing (NaN) values, and any new element needed for successful DataFrame alignment, with this value before computation. If data in both corresponding DataFrame locations is missing the result will be missing.

Returns

Result of the arithmetic operation.

Return type

DataFrame

See also

DataFrame.add()

Add DataFrames.

DataFrame.sub()

Subtract DataFrames.

DataFrame.mul()

Multiply DataFrames.

DataFrame.div()

Divide DataFrames (float division).

DataFrame.truediv()

Divide DataFrames (float division).

DataFrame.floordiv()

Divide DataFrames (integer division).

DataFrame.mod()

Calculate modulo (remainder after division).

DataFrame.pow()

Calculate exponential power.

Notes

Mismatched indices will be unioned together.

Examples

>>> df = pd.DataFrame({'angles': [0, 3, 4],
...                    'degrees': [360, 180, 360]},
...                   index=['circle', 'triangle', 'rectangle'])
>>> df
           angles  degrees
circle          0      360
triangle        3      180
rectangle       4      360

Add a scalar with operator version which return the same results.

>>> df + 1
           angles  degrees
circle          1      361
triangle        4      181
rectangle       5      361
>>> df.add(1)
           angles  degrees
circle          1      361
triangle        4      181
rectangle       5      361

Divide by constant with reverse version.

>>> df.div(10)
           angles  degrees
circle        0.0     36.0
triangle      0.3     18.0
rectangle     0.4     36.0
>>> df.rdiv(10)
             angles   degrees
circle          inf  0.027778
triangle   3.333333  0.055556
rectangle  2.500000  0.027778

Subtract a list and Series by axis with operator version.

>>> df - [1, 2]
           angles  degrees
circle         -1      358
triangle        2      178
rectangle       3      358
>>> df.sub([1, 2], axis='columns')
           angles  degrees
circle         -1      358
triangle        2      178
rectangle       3      358
>>> df.sub(pd.Series([1, 1, 1], index=['circle', 'triangle', 'rectangle']),
...        axis='index')
           angles  degrees
circle         -1      359
triangle        2      179
rectangle       3      359

Multiply a DataFrame of different shape with operator version.

>>> other = pd.DataFrame({'angles': [0, 3, 4]},
...                      index=['circle', 'triangle', 'rectangle'])
>>> other
           angles
circle          0
triangle        3
rectangle       4
>>> df * other
           angles  degrees
circle          0      NaN
triangle        9      NaN
rectangle      16      NaN
>>> df.mul(other, fill_value=0)
           angles  degrees
circle          0      0.0
triangle        9      0.0
rectangle      16      0.0

Divide by a MultiIndex by level.

>>> df_multindex = pd.DataFrame({'angles': [0, 3, 4, 4, 5, 6],
...                              'degrees': [360, 180, 360, 360, 540, 720]},
...                             index=[['A', 'A', 'A', 'B', 'B', 'B'],
...                                    ['circle', 'triangle', 'rectangle',
...                                     'square', 'pentagon', 'hexagon']])
>>> df_multindex
             angles  degrees
A circle          0      360
  triangle        3      180
  rectangle       4      360
B square          4      360
  pentagon        5      540
  hexagon         6      720
>>> df.div(df_multindex, level=1, fill_value=0)
             angles  degrees
A circle        NaN      1.0
  triangle      1.0      1.0
  rectangle     1.0      1.0
B square        0.0      0.0
  pentagon      0.0      0.0
  hexagon       0.0      0.0
divide(other, axis='columns', level=None, fill_value=None)

Get Floating division of dataframe and other, element-wise (binary operator truediv).

Equivalent to dataframe / other, but with support to substitute a fill_value for missing data in one of the inputs. With reverse version, rtruediv.

Among flexible wrappers (add, sub, mul, div, mod, pow) to arithmetic operators: +, -, *, /, //, %, **.

Parameters
  • other (scalar, sequence, Series, or DataFrame) – Any single or multiple element data structure, or list-like object.

  • axis ({0 or 'index', 1 or 'columns'}) – Whether to compare by the index (0 or ‘index’) or columns (1 or ‘columns’). For Series input, axis to match Series index on.

  • level (int or label) – Broadcast across a level, matching Index values on the passed MultiIndex level.

  • fill_value (float or None, default None) – Fill existing missing (NaN) values, and any new element needed for successful DataFrame alignment, with this value before computation. If data in both corresponding DataFrame locations is missing the result will be missing.

Returns

Result of the arithmetic operation.

Return type

DataFrame

See also

DataFrame.add()

Add DataFrames.

DataFrame.sub()

Subtract DataFrames.

DataFrame.mul()

Multiply DataFrames.

DataFrame.div()

Divide DataFrames (float division).

DataFrame.truediv()

Divide DataFrames (float division).

DataFrame.floordiv()

Divide DataFrames (integer division).

DataFrame.mod()

Calculate modulo (remainder after division).

DataFrame.pow()

Calculate exponential power.

Notes

Mismatched indices will be unioned together.

Examples

>>> df = pd.DataFrame({'angles': [0, 3, 4],
...                    'degrees': [360, 180, 360]},
...                   index=['circle', 'triangle', 'rectangle'])
>>> df
           angles  degrees
circle          0      360
triangle        3      180
rectangle       4      360

Add a scalar with operator version which return the same results.

>>> df + 1
           angles  degrees
circle          1      361
triangle        4      181
rectangle       5      361
>>> df.add(1)
           angles  degrees
circle          1      361
triangle        4      181
rectangle       5      361

Divide by constant with reverse version.

>>> df.div(10)
           angles  degrees
circle        0.0     36.0
triangle      0.3     18.0
rectangle     0.4     36.0
>>> df.rdiv(10)
             angles   degrees
circle          inf  0.027778
triangle   3.333333  0.055556
rectangle  2.500000  0.027778

Subtract a list and Series by axis with operator version.

>>> df - [1, 2]
           angles  degrees
circle         -1      358
triangle        2      178
rectangle       3      358
>>> df.sub([1, 2], axis='columns')
           angles  degrees
circle         -1      358
triangle        2      178
rectangle       3      358
>>> df.sub(pd.Series([1, 1, 1], index=['circle', 'triangle', 'rectangle']),
...        axis='index')
           angles  degrees
circle         -1      359
triangle        2      179
rectangle       3      359

Multiply a DataFrame of different shape with operator version.

>>> other = pd.DataFrame({'angles': [0, 3, 4]},
...                      index=['circle', 'triangle', 'rectangle'])
>>> other
           angles
circle          0
triangle        3
rectangle       4
>>> df * other
           angles  degrees
circle          0      NaN
triangle        9      NaN
rectangle      16      NaN
>>> df.mul(other, fill_value=0)
           angles  degrees
circle          0      0.0
triangle        9      0.0
rectangle      16      0.0

Divide by a MultiIndex by level.

>>> df_multindex = pd.DataFrame({'angles': [0, 3, 4, 4, 5, 6],
...                              'degrees': [360, 180, 360, 360, 540, 720]},
...                             index=[['A', 'A', 'A', 'B', 'B', 'B'],
...                                    ['circle', 'triangle', 'rectangle',
...                                     'square', 'pentagon', 'hexagon']])
>>> df_multindex
             angles  degrees
A circle          0      360
  triangle        3      180
  rectangle       4      360
B square          4      360
  pentagon        5      540
  hexagon         6      720
>>> df.div(df_multindex, level=1, fill_value=0)
             angles  degrees
A circle        NaN      1.0
  triangle      1.0      1.0
  rectangle     1.0      1.0
B square        0.0      0.0
  pentagon      0.0      0.0
  hexagon       0.0      0.0
dot(other)

Compute the matrix multiplication between the DataFrame and other.

This method computes the matrix product between the DataFrame and the values of an other Series, DataFrame or a numpy array.

It can also be called using self @ other in Python >= 3.5.

Parameters

other (Series, DataFrame or array-like) – The other object to compute the matrix product with.

Returns

If other is a Series, return the matrix product between self and other as a Series. If other is a DataFrame or a numpy.array, return the matrix product of self and other in a DataFrame of a np.array.

Return type

Series or DataFrame

See also

Series.dot()

Similar method for Series.

Notes

The dimensions of DataFrame and other must be compatible in order to compute the matrix multiplication. In addition, the column names of DataFrame and the index of other must contain the same values, as they will be aligned prior to the multiplication.

The dot method for Series computes the inner product, instead of the matrix product here.

Examples

Here we multiply a DataFrame with a Series.

>>> df = pd.DataFrame([[0, 1, -2, -1], [1, 1, 1, 1]])
>>> s = pd.Series([1, 1, 2, 1])
>>> df.dot(s)
0    -4
1     5
dtype: int64

Here we multiply a DataFrame with another DataFrame.

>>> other = pd.DataFrame([[0, 1], [1, 2], [-1, -1], [2, 0]])
>>> df.dot(other)
    0   1
0   1   4
1   2   2

Note that the dot method give the same result as @

>>> df @ other
    0   1
0   1   4
1   2   2

The dot method works also if other is an np.array.

>>> arr = np.array([[0, 1], [1, 2], [-1, -1], [2, 0]])
>>> df.dot(arr)
    0   1
0   1   4
1   2   2

Note how shuffling of the objects does not change the result.

>>> s2 = s.reindex([1, 0, 2, 3])
>>> df.dot(s2)
0    -4
1     5
dtype: int64
drop(labels=None, axis=0, index=None, columns=None, level=None, inplace=False, errors='raise')

Drop specified labels from rows or columns.

Remove rows or columns by specifying label names and corresponding axis, or by specifying directly index or column names. When using a multi-index, labels on different levels can be removed by specifying the level.

Parameters
  • labels (single label or list-like) – Index or column labels to drop.

  • axis ({0 or 'index', 1 or 'columns'}, default 0) – Whether to drop labels from the index (0 or ‘index’) or columns (1 or ‘columns’).

  • index (single label or list-like) – Alternative to specifying axis (labels, axis=0 is equivalent to index=labels).

  • columns (single label or list-like) – Alternative to specifying axis (labels, axis=1 is equivalent to columns=labels).

  • level (int or level name, optional) – For MultiIndex, level from which the labels will be removed.

  • inplace (bool, default False) – If False, return a copy. Otherwise, do operation inplace and return None.

  • errors ({'ignore', 'raise'}, default 'raise') – If ‘ignore’, suppress error and only existing labels are dropped.

Returns

DataFrame without the removed index or column labels.

Return type

DataFrame

Raises

KeyError – If any of the labels is not found in the selected axis.

See also

DataFrame.loc()

Label-location based indexer for selection by label.

DataFrame.dropna()

Return DataFrame with labels on given axis omitted where (all or any) data are missing.

DataFrame.drop_duplicates()

Return DataFrame with duplicate rows removed, optionally only considering certain columns.

Series.drop()

Return Series with specified index labels removed.

Examples

>>> df = pd.DataFrame(np.arange(12).reshape(3, 4),
...                   columns=['A', 'B', 'C', 'D'])
>>> df
   A  B   C   D
0  0  1   2   3
1  4  5   6   7
2  8  9  10  11

Drop columns

>>> df.drop(['B', 'C'], axis=1)
   A   D
0  0   3
1  4   7
2  8  11
>>> df.drop(columns=['B', 'C'])
   A   D
0  0   3
1  4   7
2  8  11

Drop a row by index

>>> df.drop([0, 1])
   A  B   C   D
2  8  9  10  11

Drop columns and/or rows of MultiIndex DataFrame

>>> midx = pd.MultiIndex(levels=[['lama', 'cow', 'falcon'],
...                              ['speed', 'weight', 'length']],
...                      codes=[[0, 0, 0, 1, 1, 1, 2, 2, 2],
...                             [0, 1, 2, 0, 1, 2, 0, 1, 2]])
>>> df = pd.DataFrame(index=midx, columns=['big', 'small'],
...                   data=[[45, 30], [200, 100], [1.5, 1], [30, 20],
...                         [250, 150], [1.5, 0.8], [320, 250],
...                         [1, 0.8], [0.3, 0.2]])
>>> df
                big     small
lama    speed   45.0    30.0
        weight  200.0   100.0
        length  1.5     1.0
cow     speed   30.0    20.0
        weight  250.0   150.0
        length  1.5     0.8
falcon  speed   320.0   250.0
        weight  1.0     0.8
        length  0.3     0.2
>>> df.drop(index='cow', columns='small')
                big
lama    speed   45.0
        weight  200.0
        length  1.5
falcon  speed   320.0
        weight  1.0
        length  0.3
>>> df.drop(index='length', level=1)
                big     small
lama    speed   45.0    30.0
        weight  200.0   100.0
cow     speed   30.0    20.0
        weight  250.0   150.0
falcon  speed   320.0   250.0
        weight  1.0     0.8
drop_duplicates(subset: Union[Hashable, Sequence[Hashable], None] = None, keep: Union[str, bool] = 'first', inplace: bool = False, ignore_index: bool = False) → Optional[pandas.core.frame.DataFrame]

Return DataFrame with duplicate rows removed.

Considering certain columns is optional. Indexes, including time indexes are ignored.

Parameters
  • subset (column label or sequence of labels, optional) – Only consider certain columns for identifying duplicates, by default use all of the columns.

  • keep ({'first', 'last', False}, default 'first') – Determines which duplicates (if any) to keep. - first : Drop duplicates except for the first occurrence. - last : Drop duplicates except for the last occurrence. - False : Drop all duplicates.

  • inplace (bool, default False) – Whether to drop duplicates in place or to return a copy.

  • ignore_index (bool, default False) –

    If True, the resulting axis will be labeled 0, 1, …, n - 1.

    New in version 1.0.0.

Returns

DataFrame with duplicates removed or None if inplace=True.

Return type

DataFrame

See also

DataFrame.value_counts()

Count unique combinations of columns.

Examples

Consider dataset containing ramen rating.

>>> df = pd.DataFrame({
...     'brand': ['Yum Yum', 'Yum Yum', 'Indomie', 'Indomie', 'Indomie'],
...     'style': ['cup', 'cup', 'cup', 'pack', 'pack'],
...     'rating': [4, 4, 3.5, 15, 5]
... })
>>> df
    brand style  rating
0  Yum Yum   cup     4.0
1  Yum Yum   cup     4.0
2  Indomie   cup     3.5
3  Indomie  pack    15.0
4  Indomie  pack     5.0

By default, it removes duplicate rows based on all columns.

>>> df.drop_duplicates()
    brand style  rating
0  Yum Yum   cup     4.0
2  Indomie   cup     3.5
3  Indomie  pack    15.0
4  Indomie  pack     5.0

To remove duplicates on specific column(s), use subset.

>>> df.drop_duplicates(subset=['brand'])
    brand style  rating
0  Yum Yum   cup     4.0
2  Indomie   cup     3.5

To remove duplicates and keep last occurences, use keep.

>>> df.drop_duplicates(subset=['brand', 'style'], keep='last')
    brand style  rating
1  Yum Yum   cup     4.0
2  Indomie   cup     3.5
4  Indomie  pack     5.0
droplevel(level, axis=0) → FrameOrSeries

Return DataFrame with requested index / column level(s) removed.

New in version 0.24.0.

Parameters
  • level (int, str, or list-like) – If a string is given, must be the name of a level If list-like, elements must be names or positional indexes of levels.

  • axis ({0 or 'index', 1 or 'columns'}, default 0) –

    Axis along which the level(s) is removed:

    • 0 or ‘index’: remove level(s) in column.

    • 1 or ‘columns’: remove level(s) in row.

Returns

DataFrame with requested index / column level(s) removed.

Return type

DataFrame

Examples

>>> df = pd.DataFrame([
...     [1, 2, 3, 4],
...     [5, 6, 7, 8],
...     [9, 10, 11, 12]
... ]).set_index([0, 1]).rename_axis(['a', 'b'])
>>> df.columns = pd.MultiIndex.from_tuples([
...     ('c', 'e'), ('d', 'f')
... ], names=['level_1', 'level_2'])
>>> df
level_1   c   d
level_2   e   f
a b
1 2      3   4
5 6      7   8
9 10    11  12
>>> df.droplevel('a')
level_1   c   d
level_2   e   f
b
2        3   4
6        7   8
10      11  12
>>> df.droplevel('level_2', axis=1)
level_1   c   d
a b
1 2      3   4
5 6      7   8
9 10    11  12
dropna(axis=0, how='any', thresh=None, subset=None, inplace=False)

Remove missing values.

See the User Guide for more on which values are considered missing, and how to work with missing data.

Parameters
  • axis ({0 or 'index', 1 or 'columns'}, default 0) –

    Determine if rows or columns which contain missing values are removed.

    • 0, or ‘index’ : Drop rows which contain missing values.

    • 1, or ‘columns’ : Drop columns which contain missing value.

    Changed in version 1.0.0: Pass tuple or list to drop on multiple axes. Only a single axis is allowed.

  • how ({'any', 'all'}, default 'any') –

    Determine if row or column is removed from DataFrame, when we have at least one NA or all NA.

    • ’any’ : If any NA values are present, drop that row or column.

    • ’all’ : If all values are NA, drop that row or column.

  • thresh (int, optional) – Require that many non-NA values.

  • subset (array-like, optional) – Labels along other axis to consider, e.g. if you are dropping rows these would be a list of columns to include.

  • inplace (bool, default False) – If True, do operation inplace and return None.

Returns

DataFrame with NA entries dropped from it.

Return type

DataFrame

See also

DataFrame.isna()

Indicate missing values.

DataFrame.notna()

Indicate existing (non-missing) values.

DataFrame.fillna()

Replace missing values.

Series.dropna()

Drop missing values.

Index.dropna()

Drop missing indices.

Examples

>>> df = pd.DataFrame({"name": ['Alfred', 'Batman', 'Catwoman'],
...                    "toy": [np.nan, 'Batmobile', 'Bullwhip'],
...                    "born": [pd.NaT, pd.Timestamp("1940-04-25"),
...                             pd.NaT]})
>>> df
       name        toy       born
0    Alfred        NaN        NaT
1    Batman  Batmobile 1940-04-25
2  Catwoman   Bullwhip        NaT

Drop the rows where at least one element is missing.

>>> df.dropna()
     name        toy       born
1  Batman  Batmobile 1940-04-25

Drop the columns where at least one element is missing.

>>> df.dropna(axis='columns')
       name
0    Alfred
1    Batman
2  Catwoman

Drop the rows where all elements are missing.

>>> df.dropna(how='all')
       name        toy       born
0    Alfred        NaN        NaT
1    Batman  Batmobile 1940-04-25
2  Catwoman   Bullwhip        NaT

Keep only the rows with at least 2 non-NA values.

>>> df.dropna(thresh=2)
       name        toy       born
1    Batman  Batmobile 1940-04-25
2  Catwoman   Bullwhip        NaT

Define in which columns to look for missing values.

>>> df.dropna(subset=['name', 'born'])
       name        toy       born
1    Batman  Batmobile 1940-04-25

Keep the DataFrame with valid entries in the same variable.

>>> df.dropna(inplace=True)
>>> df
     name        toy       born
1  Batman  Batmobile 1940-04-25
property dtypes

Return the dtypes in the DataFrame.

This returns a Series with the data type of each column. The result’s index is the original DataFrame’s columns. Columns with mixed types are stored with the object dtype. See the User Guide for more.

Returns

The data type of each column.

Return type

pandas.Series

Examples

>>> df = pd.DataFrame({'float': [1.0],
...                    'int': [1],
...                    'datetime': [pd.Timestamp('20180310')],
...                    'string': ['foo']})
>>> df.dtypes
float              float64
int                  int64
datetime    datetime64[ns]
string              object
dtype: object
duplicated(subset: Union[Hashable, Sequence[Hashable], None] = None, keep: Union[str, bool] = 'first') → pandas.core.series.Series

Return boolean Series denoting duplicate rows.

Considering certain columns is optional.

Parameters
  • subset (column label or sequence of labels, optional) – Only consider certain columns for identifying duplicates, by default use all of the columns.

  • keep ({'first', 'last', False}, default 'first') –

    Determines which duplicates (if any) to mark.

    • first : Mark duplicates as True except for the first occurrence.

    • last : Mark duplicates as True except for the last occurrence.

    • False : Mark all duplicates as True.

Returns

Boolean series for each duplicated rows.

Return type

Series

See also

Index.duplicated()

Equivalent method on index.

Series.duplicated()

Equivalent method on Series.

Series.drop_duplicates()

Remove duplicate values from Series.

DataFrame.drop_duplicates()

Remove duplicate values from DataFrame.

Examples

Consider dataset containing ramen rating.

>>> df = pd.DataFrame({
...     'brand': ['Yum Yum', 'Yum Yum', 'Indomie', 'Indomie', 'Indomie'],
...     'style': ['cup', 'cup', 'cup', 'pack', 'pack'],
...     'rating': [4, 4, 3.5, 15, 5]
... })
>>> df
    brand style  rating
0  Yum Yum   cup     4.0
1  Yum Yum   cup     4.0
2  Indomie   cup     3.5
3  Indomie  pack    15.0
4  Indomie  pack     5.0

By default, for each set of duplicated values, the first occurrence is set on False and all others on True.

>>> df.duplicated()
0    False
1     True
2    False
3    False
4    False
dtype: bool

By using ‘last’, the last occurrence of each set of duplicated values is set on False and all others on True.

>>> df.duplicated(keep='last')
0     True
1    False
2    False
3    False
4    False
dtype: bool

By setting keep on False, all duplicates are True.

>>> df.duplicated(keep=False)
0     True
1     True
2    False
3    False
4    False
dtype: bool

To find duplicates on specific column(s), use subset.

>>> df.duplicated(subset=['brand'])
0    False
1     True
2    False
3     True
4     True
dtype: bool
property empty

Indicator whether DataFrame is empty.

True if DataFrame is entirely empty (no items), meaning any of the axes are of length 0.

Returns

If DataFrame is empty, return True, if not return False.

Return type

bool

See also

Series.dropna

Return series without null values.

DataFrame.dropna

Return DataFrame with labels on given axis omitted where (all or any) data are missing.

Notes

If DataFrame contains only NaNs, it is still not considered empty. See the example below.

Examples

An example of an actual empty DataFrame. Notice the index is empty:

>>> df_empty = pd.DataFrame({'A' : []})
>>> df_empty
Empty DataFrame
Columns: [A]
Index: []
>>> df_empty.empty
True

If we only have NaNs in our DataFrame, it is not considered empty! We will need to drop the NaNs to make the DataFrame empty:

>>> df = pd.DataFrame({'A' : [np.nan]})
>>> df
    A
0 NaN
>>> df.empty
False
>>> df.dropna().empty
True
eq(other, axis='columns', level=None)

Get Equal to of dataframe and other, element-wise (binary operator eq).

Among flexible wrappers (eq, ne, le, lt, ge, gt) to comparison operators.

Equivalent to ==, =!, <=, <, >=, > with support to choose axis (rows or columns) and level for comparison.

Parameters
  • other (scalar, sequence, Series, or DataFrame) – Any single or multiple element data structure, or list-like object.

  • axis ({0 or 'index', 1 or 'columns'}, default 'columns') – Whether to compare by the index (0 or ‘index’) or columns (1 or ‘columns’).

  • level (int or label) – Broadcast across a level, matching Index values on the passed MultiIndex level.

Returns

Result of the comparison.

Return type

DataFrame of bool

See also

DataFrame.eq()

Compare DataFrames for equality elementwise.

DataFrame.ne()

Compare DataFrames for inequality elementwise.

DataFrame.le()

Compare DataFrames for less than inequality or equality elementwise.

DataFrame.lt()

Compare DataFrames for strictly less than inequality elementwise.

DataFrame.ge()

Compare DataFrames for greater than inequality or equality elementwise.

DataFrame.gt()

Compare DataFrames for strictly greater than inequality elementwise.

Notes

Mismatched indices will be unioned together. NaN values are considered different (i.e. NaN != NaN).

Examples

>>> df = pd.DataFrame({'cost': [250, 150, 100],
...                    'revenue': [100, 250, 300]},
...                   index=['A', 'B', 'C'])
>>> df
   cost  revenue
A   250      100
B   150      250
C   100      300

Comparison with a scalar, using either the operator or method:

>>> df == 100
    cost  revenue
A  False     True
B  False    False
C   True    False
>>> df.eq(100)
    cost  revenue
A  False     True
B  False    False
C   True    False

When other is a Series, the columns of a DataFrame are aligned with the index of other and broadcast:

>>> df != pd.Series([100, 250], index=["cost", "revenue"])
    cost  revenue
A   True     True
B   True    False
C  False     True

Use the method to control the broadcast axis:

>>> df.ne(pd.Series([100, 300], index=["A", "D"]), axis='index')
   cost  revenue
A  True    False
B  True     True
C  True     True
D  True     True

When comparing to an arbitrary sequence, the number of columns must match the number elements in other:

>>> df == [250, 100]
    cost  revenue
A   True     True
B  False    False
C  False    False

Use the method to control the axis:

>>> df.eq([250, 250, 100], axis='index')
    cost  revenue
A   True    False
B  False     True
C   True    False

Compare to a DataFrame of different shape.

>>> other = pd.DataFrame({'revenue': [300, 250, 100, 150]},
...                      index=['A', 'B', 'C', 'D'])
>>> other
   revenue
A      300
B      250
C      100
D      150
>>> df.gt(other)
    cost  revenue
A  False    False
B  False    False
C  False     True
D  False    False

Compare to a MultiIndex by level.

>>> df_multindex = pd.DataFrame({'cost': [250, 150, 100, 150, 300, 220],
...                              'revenue': [100, 250, 300, 200, 175, 225]},
...                             index=[['Q1', 'Q1', 'Q1', 'Q2', 'Q2', 'Q2'],
...                                    ['A', 'B', 'C', 'A', 'B', 'C']])
>>> df_multindex
      cost  revenue
Q1 A   250      100
   B   150      250
   C   100      300
Q2 A   150      200
   B   300      175
   C   220      225
>>> df.le(df_multindex, level=1)
       cost  revenue
Q1 A   True     True
   B   True     True
   C   True     True
Q2 A  False     True
   B   True    False
   C   True    False
equals(other)

Test whether two objects contain the same elements.

This function allows two Series or DataFrames to be compared against each other to see if they have the same shape and elements. NaNs in the same location are considered equal. The column headers do not need to have the same type, but the elements within the columns must be the same dtype.

Parameters

other (Series or DataFrame) – The other Series or DataFrame to be compared with the first.

Returns

True if all elements are the same in both objects, False otherwise.

Return type

bool

See also

Series.eq()

Compare two Series objects of the same length and return a Series where each element is True if the element in each Series is equal, False otherwise.

DataFrame.eq()

Compare two DataFrame objects of the same shape and return a DataFrame where each element is True if the respective element in each DataFrame is equal, False otherwise.

testing.assert_series_equal()

Raises an AssertionError if left and right are not equal. Provides an easy interface to ignore inequality in dtypes, indexes and precision among others.

testing.assert_frame_equal()

Like assert_series_equal, but targets DataFrames.

numpy.array_equal()

Return True if two arrays have the same shape and elements, False otherwise.

Notes

This function requires that the elements have the same dtype as their respective elements in the other Series or DataFrame. However, the column labels do not need to have the same type, as long as they are still considered equal.

Examples

>>> df = pd.DataFrame({1: [10], 2: [20]})
>>> df
    1   2
0  10  20

DataFrames df and exactly_equal have the same types and values for their elements and column labels, which will return True.

>>> exactly_equal = pd.DataFrame({1: [10], 2: [20]})
>>> exactly_equal
    1   2
0  10  20
>>> df.equals(exactly_equal)
True

DataFrames df and different_column_type have the same element types and values, but have different types for the column labels, which will still return True.

>>> different_column_type = pd.DataFrame({1.0: [10], 2.0: [20]})
>>> different_column_type
   1.0  2.0
0   10   20
>>> df.equals(different_column_type)
True

DataFrames df and different_data_type have different types for the same values for their elements, and will return False even though their column labels are the same values and types.

>>> different_data_type = pd.DataFrame({1: [10.0], 2: [20.0]})
>>> different_data_type
      1     2
0  10.0  20.0
>>> df.equals(different_data_type)
False
eval(expr, inplace=False, **kwargs)

Evaluate a string describing operations on DataFrame columns.

Operates on columns only, not specific rows or elements. This allows eval to run arbitrary code, which can make you vulnerable to code injection if you pass user input to this function.

Parameters
  • expr (str) – The expression string to evaluate.

  • inplace (bool, default False) – If the expression contains an assignment, whether to perform the operation inplace and mutate the existing DataFrame. Otherwise, a new DataFrame is returned.

  • **kwargs – See the documentation for eval() for complete details on the keyword arguments accepted by query().

Returns

The result of the evaluation.

Return type

ndarray, scalar, or pandas object

See also

DataFrame.query()

Evaluates a boolean expression to query the columns of a frame.

DataFrame.assign()

Can evaluate an expression or function to create new values for a column.

eval()

Evaluate a Python expression as a string using various backends.

Notes

For more details see the API documentation for eval(). For detailed examples see enhancing performance with eval.

Examples

>>> df = pd.DataFrame({'A': range(1, 6), 'B': range(10, 0, -2)})
>>> df
   A   B
0  1  10
1  2   8
2  3   6
3  4   4
4  5   2
>>> df.eval('A + B')
0    11
1    10
2     9
3     8
4     7
dtype: int64

Assignment is allowed though by default the original DataFrame is not modified.

>>> df.eval('C = A + B')
   A   B   C
0  1  10  11
1  2   8  10
2  3   6   9
3  4   4   8
4  5   2   7
>>> df
   A   B
0  1  10
1  2   8
2  3   6
3  4   4
4  5   2

Use inplace=True to modify the original DataFrame.

>>> df.eval('C = A + B', inplace=True)
>>> df
   A   B   C
0  1  10  11
1  2   8  10
2  3   6   9
3  4   4   8
4  5   2   7

Multiple columns can be assigned to using multi-line expressions:

>>> df.eval(
...     '''
... C = A + B
... D = A - B
... '''
... )
   A   B   C  D
0  1  10  11 -9
1  2   8  10 -6
2  3   6   9 -3
3  4   4   8  0
4  5   2   7  3
ewm(com=None, span=None, halflife=None, alpha=None, min_periods=0, adjust=True, ignore_na=False, axis=0, times=None)

Provide exponential weighted (EW) functions.

Available EW functions: mean(), var(), std(), corr(), cov().

Exactly one parameter: com, span, halflife, or alpha must be provided.

Parameters
  • com (float, optional) – Specify decay in terms of center of mass, \(\alpha = 1 / (1 + com)\), for \(com \geq 0\).

  • span (float, optional) – Specify decay in terms of span, \(\alpha = 2 / (span + 1)\), for \(span \geq 1\).

  • halflife (float, str, timedelta, optional) –

    Specify decay in terms of half-life, \(\alpha = 1 - \exp\left(-\ln(2) / halflife\right)\), for \(halflife > 0\).

    If times is specified, the time unit (str or timedelta) over which an observation decays to half its value. Only applicable to mean() and halflife value will not apply to the other functions.

    New in version 1.1.0.

  • alpha (float, optional) – Specify smoothing factor \(\alpha\) directly, \(0 < \alpha \leq 1\).

  • min_periods (int, default 0) – Minimum number of observations in window required to have a value (otherwise result is NA).

  • adjust (bool, default True) –

    Divide by decaying adjustment factor in beginning periods to account for imbalance in relative weightings (viewing EWMA as a moving average).

    • When adjust=True (default), the EW function is calculated using weights \(w_i = (1 - \alpha)^i\). For example, the EW moving average of the series [\(x_0, x_1, ..., x_t\)] would be:

    \[y_t = \frac{x_t + (1 - \alpha)x_{t-1} + (1 - \alpha)^2 x_{t-2} + ... + (1 - \alpha)^t x_0}{1 + (1 - \alpha) + (1 - \alpha)^2 + ... + (1 - \alpha)^t}\]
    • When adjust=False, the exponentially weighted function is calculated recursively:

    \[\begin{split}\begin{split} y_0 &= x_0\\ y_t &= (1 - \alpha) y_{t-1} + \alpha x_t, \end{split}\end{split}\]

  • ignore_na (bool, default False) –

    Ignore missing values when calculating weights; specify True to reproduce pre-0.15.0 behavior.

    • When ignore_na=False (default), weights are based on absolute positions. For example, the weights of \(x_0\) and \(x_2\) used in calculating the final weighted average of [\(x_0\), None, \(x_2\)] are \((1-\alpha)^2\) and \(1\) if adjust=True, and \((1-\alpha)^2\) and \(\alpha\) if adjust=False.

    • When ignore_na=True (reproducing pre-0.15.0 behavior), weights are based on relative positions. For example, the weights of \(x_0\) and \(x_2\) used in calculating the final weighted average of [\(x_0\), None, \(x_2\)] are \(1-\alpha\) and \(1\) if adjust=True, and \(1-\alpha\) and \(\alpha\) if adjust=False.

  • axis ({0, 1}, default 0) – The axis to use. The value 0 identifies the rows, and 1 identifies the columns.

  • times (str, np.ndarray, Series, default None) –

    New in version 1.1.0.

    Times corresponding to the observations. Must be monotonically increasing and datetime64[ns] dtype.

    If str, the name of the column in the DataFrame representing the times.

    If 1-D array like, a sequence with the same shape as the observations.

    Only applicable to mean().

Returns

A Window sub-classed for the particular operation.

Return type

DataFrame

See also

rolling()

Provides rolling window calculations.

expanding()

Provides expanding transformations.

Notes

More details can be found at: Exponentially weighted windows.

Examples

>>> df = pd.DataFrame({'B': [0, 1, 2, np.nan, 4]})
>>> df
     B
0  0.0
1  1.0
2  2.0
3  NaN
4  4.0
>>> df.ewm(com=0.5).mean()
          B
0  0.000000
1  0.750000
2  1.615385
3  1.615385
4  3.670213

Specifying times with a timedelta halflife when computing mean.

>>> times = ['2020-01-01', '2020-01-03', '2020-01-10', '2020-01-15', '2020-01-17']
>>> df.ewm(halflife='4 days', times=pd.DatetimeIndex(times)).mean()
          B
0  0.000000
1  0.585786
2  1.523889
3  1.523889
4  3.233686
expanding(min_periods=1, center=None, axis=0)

Provide expanding transformations.

Parameters
  • min_periods (int, default 1) – Minimum number of observations in window required to have a value (otherwise result is NA).

  • center (bool, default False) – Set the labels at the center of the window.

  • axis (int or str, default 0) –

Returns

Return type

a Window sub-classed for the particular operation

See also

rolling()

Provides rolling window calculations.

ewm()

Provides exponential weighted functions.

Notes

By default, the result is set to the right edge of the window. This can be changed to the center of the window by setting center=True.

Examples

>>> df = pd.DataFrame({"B": [0, 1, 2, np.nan, 4]})
>>> df
     B
0  0.0
1  1.0
2  2.0
3  NaN
4  4.0
>>> df.expanding(2).sum()
     B
0  NaN
1  1.0
2  3.0
3  3.0
4  7.0
explode(column: Union[str, Tuple], ignore_index: bool = False) → pandas.core.frame.DataFrame

Transform each element of a list-like to a row, replicating index values.

New in version 0.25.0.

Parameters
  • column (str or tuple) – Column to explode.

  • ignore_index (bool, default False) –

    If True, the resulting index will be labeled 0, 1, …, n - 1.

    New in version 1.1.0.

Returns

Exploded lists to rows of the subset columns; index will be duplicated for these rows.

Return type

DataFrame

Raises

ValueError : – if columns of the frame are not unique.

See also

DataFrame.unstack()

Pivot a level of the (necessarily hierarchical) index labels.

DataFrame.melt()

Unpivot a DataFrame from wide format to long format.

Series.explode()

Explode a DataFrame from list-like columns to long format.

Notes

This routine will explode list-likes including lists, tuples, Series, and np.ndarray. The result dtype of the subset rows will be object. Scalars will be returned unchanged. Empty list-likes will result in a np.nan for that row.

Examples

>>> df = pd.DataFrame({'A': [[1, 2, 3], 'foo', [], [3, 4]], 'B': 1})
>>> df
           A  B
0  [1, 2, 3]  1
1        foo  1
2         []  1
3     [3, 4]  1
>>> df.explode('A')
     A  B
0    1  1
0    2  1
0    3  1
1  foo  1
2  NaN  1
3    3  1
3    4  1
ffill(axis=None, inplace: bool = False, limit=None, downcast=None) → Optional[FrameOrSeries]

Synonym for DataFrame.fillna() with method='ffill'.

Returns

Object with missing values filled or None if inplace=True.

Return type

{klass} or None

fillna(value=None, method=None, axis=None, inplace=False, limit=None, downcast=None) → Optional[pandas.core.frame.DataFrame]

Fill NA/NaN values using the specified method.

Parameters
  • value (scalar, dict, Series, or DataFrame) – Value to use to fill holes (e.g. 0), alternately a dict/Series/DataFrame of values specifying which value to use for each index (for a Series) or column (for a DataFrame). Values not in the dict/Series/DataFrame will not be filled. This value cannot be a list.

  • method ({'backfill', 'bfill', 'pad', 'ffill', None}, default None) – Method to use for filling holes in reindexed Series pad / ffill: propagate last valid observation forward to next valid backfill / bfill: use next valid observation to fill gap.

  • axis ({0 or 'index', 1 or 'columns'}) – Axis along which to fill missing values.

  • inplace (bool, default False) – If True, fill in-place. Note: this will modify any other views on this object (e.g., a no-copy slice for a column in a DataFrame).

  • limit (int, default None) – If method is specified, this is the maximum number of consecutive NaN values to forward/backward fill. In other words, if there is a gap with more than this number of consecutive NaNs, it will only be partially filled. If method is not specified, this is the maximum number of entries along the entire axis where NaNs will be filled. Must be greater than 0 if not None.

  • downcast (dict, default is None) – A dict of item->dtype of what to downcast if possible, or the string ‘infer’ which will try to downcast to an appropriate equal type (e.g. float64 to int64 if possible).

Returns

Object with missing values filled or None if inplace=True.

Return type

DataFrame or None

See also

interpolate()

Fill NaN values using interpolation.

reindex()

Conform object to new index.

asfreq()

Convert TimeSeries to specified frequency.

Examples

>>> df = pd.DataFrame([[np.nan, 2, np.nan, 0],
...                    [3, 4, np.nan, 1],
...                    [np.nan, np.nan, np.nan, 5],
...                    [np.nan, 3, np.nan, 4]],
...                   columns=list('ABCD'))
>>> df
     A    B   C  D
0  NaN  2.0 NaN  0
1  3.0  4.0 NaN  1
2  NaN  NaN NaN  5
3  NaN  3.0 NaN  4

Replace all NaN elements with 0s.

>>> df.fillna(0)
    A   B   C   D
0   0.0 2.0 0.0 0
1   3.0 4.0 0.0 1
2   0.0 0.0 0.0 5
3   0.0 3.0 0.0 4

We can also propagate non-null values forward or backward.

>>> df.fillna(method='ffill')
    A   B   C   D
0   NaN 2.0 NaN 0
1   3.0 4.0 NaN 1
2   3.0 4.0 NaN 5
3   3.0 3.0 NaN 4

Replace all NaN elements in column ‘A’, ‘B’, ‘C’, and ‘D’, with 0, 1, 2, and 3 respectively.

>>> values = {'A': 0, 'B': 1, 'C': 2, 'D': 3}
>>> df.fillna(value=values)
    A   B   C   D
0   0.0 2.0 2.0 0
1   3.0 4.0 2.0 1
2   0.0 1.0 2.0 5
3   0.0 3.0 2.0 4

Only replace the first NaN element.

>>> df.fillna(value=values, limit=1)
    A   B   C   D
0   0.0 2.0 2.0 0
1   3.0 4.0 NaN 1
2   NaN 1.0 NaN 5
3   NaN 3.0 NaN 4
filter(items=None, like: Optional[str] = None, regex: Optional[str] = None, axis=None) → FrameOrSeries

Subset the dataframe rows or columns according to the specified index labels.

Note that this routine does not filter a dataframe on its contents. The filter is applied to the labels of the index.

Parameters
  • items (list-like) – Keep labels from axis which are in items.

  • like (str) – Keep labels from axis for which “like in label == True”.

  • regex (str (regular expression)) – Keep labels from axis for which re.search(regex, label) == True.

  • axis ({0 or ‘index’, 1 or ‘columns’, None}, default None) – The axis to filter on, expressed either as an index (int) or axis name (str). By default this is the info axis, ‘index’ for Series, ‘columns’ for DataFrame.

Returns

Return type

same type as input object

See also

DataFrame.loc()

Access a group of rows and columns by label(s) or a boolean array.

Notes

The items, like, and regex parameters are enforced to be mutually exclusive.

axis defaults to the info axis that is used when indexing with [].

Examples

>>> df = pd.DataFrame(np.array(([1, 2, 3], [4, 5, 6])),
...                   index=['mouse', 'rabbit'],
...                   columns=['one', 'two', 'three'])
>>> df
        one  two  three
mouse     1    2      3
rabbit    4    5      6
>>> # select columns by name
>>> df.filter(items=['one', 'three'])
         one  three
mouse     1      3
rabbit    4      6
>>> # select columns by regular expression
>>> df.filter(regex='e$', axis=1)
         one  three
mouse     1      3
rabbit    4      6
>>> # select rows containing 'bbi'
>>> df.filter(like='bbi', axis=0)
         one  two  three
rabbit    4    5      6
first(offset) → FrameOrSeries

Select initial periods of time series data based on a date offset.

When having a DataFrame with dates as index, this function can select the first few rows based on a date offset.

Parameters

offset (str, DateOffset or dateutil.relativedelta) – The offset length of the data that will be selected. For instance, ‘1M’ will display all the rows having their index within the first month.

Returns

A subset of the caller.

Return type

Series or DataFrame

Raises

TypeError – If the index is not a DatetimeIndex

See also

last()

Select final periods of time series based on a date offset.

at_time()

Select values at a particular time of the day.

between_time()

Select values between particular times of the day.

Examples

>>> i = pd.date_range('2018-04-09', periods=4, freq='2D')
>>> ts = pd.DataFrame({'A': [1, 2, 3, 4]}, index=i)
>>> ts
            A
2018-04-09  1
2018-04-11  2
2018-04-13  3
2018-04-15  4

Get the rows for the first 3 days:

>>> ts.first('3D')
            A
2018-04-09  1
2018-04-11  2

Notice the data for 3 first calendar days were returned, not the first 3 days observed in the dataset, and therefore data for 2018-04-13 was not returned.

first_valid_index()

Return index for first non-NA/null value.

Returns

scalar

Return type

type of index

Notes

If all elements are non-NA/null, returns None. Also returns None for empty Series/DataFrame.

floordiv(other, axis='columns', level=None, fill_value=None)

Get Integer division of dataframe and other, element-wise (binary operator floordiv).

Equivalent to dataframe // other, but with support to substitute a fill_value for missing data in one of the inputs. With reverse version, rfloordiv.

Among flexible wrappers (add, sub, mul, div, mod, pow) to arithmetic operators: +, -, *, /, //, %, **.

Parameters
  • other (scalar, sequence, Series, or DataFrame) – Any single or multiple element data structure, or list-like object.

  • axis ({0 or 'index', 1 or 'columns'}) – Whether to compare by the index (0 or ‘index’) or columns (1 or ‘columns’). For Series input, axis to match Series index on.

  • level (int or label) – Broadcast across a level, matching Index values on the passed MultiIndex level.

  • fill_value (float or None, default None) – Fill existing missing (NaN) values, and any new element needed for successful DataFrame alignment, with this value before computation. If data in both corresponding DataFrame locations is missing the result will be missing.

Returns

Result of the arithmetic operation.

Return type

DataFrame

See also

DataFrame.add()

Add DataFrames.

DataFrame.sub()

Subtract DataFrames.

DataFrame.mul()

Multiply DataFrames.

DataFrame.div()

Divide DataFrames (float division).

DataFrame.truediv()

Divide DataFrames (float division).

DataFrame.floordiv()

Divide DataFrames (integer division).

DataFrame.mod()

Calculate modulo (remainder after division).

DataFrame.pow()

Calculate exponential power.

Notes

Mismatched indices will be unioned together.

Examples

>>> df = pd.DataFrame({'angles': [0, 3, 4],
...                    'degrees': [360, 180, 360]},
...                   index=['circle', 'triangle', 'rectangle'])
>>> df
           angles  degrees
circle          0      360
triangle        3      180
rectangle       4      360

Add a scalar with operator version which return the same results.

>>> df + 1
           angles  degrees
circle          1      361
triangle        4      181
rectangle       5      361
>>> df.add(1)
           angles  degrees
circle          1      361
triangle        4      181
rectangle       5      361

Divide by constant with reverse version.

>>> df.div(10)
           angles  degrees
circle        0.0     36.0
triangle      0.3     18.0
rectangle     0.4     36.0
>>> df.rdiv(10)
             angles   degrees
circle          inf  0.027778
triangle   3.333333  0.055556
rectangle  2.500000  0.027778

Subtract a list and Series by axis with operator version.

>>> df - [1, 2]
           angles  degrees
circle         -1      358
triangle        2      178
rectangle       3      358
>>> df.sub([1, 2], axis='columns')
           angles  degrees
circle         -1      358
triangle        2      178
rectangle       3      358
>>> df.sub(pd.Series([1, 1, 1], index=['circle', 'triangle', 'rectangle']),
...        axis='index')
           angles  degrees
circle         -1      359
triangle        2      179
rectangle       3      359

Multiply a DataFrame of different shape with operator version.

>>> other = pd.DataFrame({'angles': [0, 3, 4]},
...                      index=['circle', 'triangle', 'rectangle'])
>>> other
           angles
circle          0
triangle        3
rectangle       4
>>> df * other
           angles  degrees
circle          0      NaN
triangle        9      NaN
rectangle      16      NaN
>>> df.mul(other, fill_value=0)
           angles  degrees
circle          0      0.0
triangle        9      0.0
rectangle      16      0.0

Divide by a MultiIndex by level.

>>> df_multindex = pd.DataFrame({'angles': [0, 3, 4, 4, 5, 6],
...                              'degrees': [360, 180, 360, 360, 540, 720]},
...                             index=[['A', 'A', 'A', 'B', 'B', 'B'],
...                                    ['circle', 'triangle', 'rectangle',
...                                     'square', 'pentagon', 'hexagon']])
>>> df_multindex
             angles  degrees
A circle          0      360
  triangle        3      180
  rectangle       4      360
B square          4      360
  pentagon        5      540
  hexagon         6      720
>>> df.div(df_multindex, level=1, fill_value=0)
             angles  degrees
A circle        NaN      1.0
  triangle      1.0      1.0
  rectangle     1.0      1.0
B square        0.0      0.0
  pentagon      0.0      0.0
  hexagon       0.0      0.0
classmethod from_dict(data, orient='columns', dtype=None, columns=None) → pandas.core.frame.DataFrame

Construct DataFrame from dict of array-like or dicts.

Creates DataFrame object from dictionary by columns or by index allowing dtype specification.

Parameters
  • data (dict) – Of the form {field : array-like} or {field : dict}.

  • orient ({'columns', 'index'}, default 'columns') – The “orientation” of the data. If the keys of the passed dict should be the columns of the resulting DataFrame, pass ‘columns’ (default). Otherwise if the keys should be rows, pass ‘index’.

  • dtype (dtype, default None) – Data type to force, otherwise infer.

  • columns (list, default None) –

    Column labels to use when orient='index'. Raises a ValueError if used with orient='columns'.

    New in version 0.23.0.

Returns

Return type

DataFrame

See also

DataFrame.from_records()

DataFrame from structured ndarray, sequence of tuples or dicts, or DataFrame.

DataFrame()

DataFrame object creation using constructor.

Examples

By default the keys of the dict become the DataFrame columns:

>>> data = {'col_1': [3, 2, 1, 0], 'col_2': ['a', 'b', 'c', 'd']}
>>> pd.DataFrame.from_dict(data)
   col_1 col_2
0      3     a
1      2     b
2      1     c
3      0     d

Specify orient='index' to create the DataFrame using dictionary keys as rows:

>>> data = {'row_1': [3, 2, 1, 0], 'row_2': ['a', 'b', 'c', 'd']}
>>> pd.DataFrame.from_dict(data, orient='index')
       0  1  2  3
row_1  3  2  1  0
row_2  a  b  c  d

When using the ‘index’ orientation, the column names can be specified manually:

>>> pd.DataFrame.from_dict(data, orient='index',
...                        columns=['A', 'B', 'C', 'D'])
       A  B  C  D
row_1  3  2  1  0
row_2  a  b  c  d
classmethod from_records(data, index=None, exclude=None, columns=None, coerce_float=False, nrows=None) → pandas.core.frame.DataFrame

Convert structured or record ndarray to DataFrame.

Creates a DataFrame object from a structured ndarray, sequence of tuples or dicts, or DataFrame.

Parameters
  • data (structured ndarray, sequence of tuples or dicts, or DataFrame) – Structured input data.

  • index (str, list of fields, array-like) – Field of array to use as the index, alternately a specific set of input labels to use.

  • exclude (sequence, default None) – Columns or fields to exclude.

  • columns (sequence, default None) – Column names to use. If the passed data do not have names associated with them, this argument provides names for the columns. Otherwise this argument indicates the order of the columns in the result (any names not found in the data will become all-NA columns).

  • coerce_float (bool, default False) – Attempt to convert values of non-string, non-numeric objects (like decimal.Decimal) to floating point, useful for SQL result sets.

  • nrows (int, default None) – Number of rows to read if data is an iterator.

Returns

Return type

DataFrame

See also

DataFrame.from_dict()

DataFrame from dict of array-like or dicts.

DataFrame()

DataFrame object creation using constructor.

Examples

Data can be provided as a structured ndarray:

>>> data = np.array([(3, 'a'), (2, 'b'), (1, 'c'), (0, 'd')],
...                 dtype=[('col_1', 'i4'), ('col_2', 'U1')])
>>> pd.DataFrame.from_records(data)
   col_1 col_2
0      3     a
1      2     b
2      1     c
3      0     d

Data can be provided as a list of dicts:

>>> data = [{'col_1': 3, 'col_2': 'a'},
...         {'col_1': 2, 'col_2': 'b'},
...         {'col_1': 1, 'col_2': 'c'},
...         {'col_1': 0, 'col_2': 'd'}]
>>> pd.DataFrame.from_records(data)
   col_1 col_2
0      3     a
1      2     b
2      1     c
3      0     d

Data can be provided as a list of tuples with corresponding columns:

>>> data = [(3, 'a'), (2, 'b'), (1, 'c'), (0, 'd')]
>>> pd.DataFrame.from_records(data, columns=['col_1', 'col_2'])
   col_1 col_2
0      3     a
1      2     b
2      1     c
3      0     d
ge(other, axis='columns', level=None)

Get Greater than or equal to of dataframe and other, element-wise (binary operator ge).

Among flexible wrappers (eq, ne, le, lt, ge, gt) to comparison operators.

Equivalent to ==, =!, <=, <, >=, > with support to choose axis (rows or columns) and level for comparison.

Parameters
  • other (scalar, sequence, Series, or DataFrame) – Any single or multiple element data structure, or list-like object.

  • axis ({0 or 'index', 1 or 'columns'}, default 'columns') – Whether to compare by the index (0 or ‘index’) or columns (1 or ‘columns’).

  • level (int or label) – Broadcast across a level, matching Index values on the passed MultiIndex level.

Returns

Result of the comparison.

Return type

DataFrame of bool

See also

DataFrame.eq()

Compare DataFrames for equality elementwise.

DataFrame.ne()

Compare DataFrames for inequality elementwise.

DataFrame.le()

Compare DataFrames for less than inequality or equality elementwise.

DataFrame.lt()

Compare DataFrames for strictly less than inequality elementwise.

DataFrame.ge()

Compare DataFrames for greater than inequality or equality elementwise.

DataFrame.gt()

Compare DataFrames for strictly greater than inequality elementwise.

Notes

Mismatched indices will be unioned together. NaN values are considered different (i.e. NaN != NaN).

Examples

>>> df = pd.DataFrame({'cost': [250, 150, 100],
...                    'revenue': [100, 250, 300]},
...                   index=['A', 'B', 'C'])
>>> df
   cost  revenue
A   250      100
B   150      250
C   100      300

Comparison with a scalar, using either the operator or method:

>>> df == 100
    cost  revenue
A  False     True
B  False    False
C   True    False
>>> df.eq(100)
    cost  revenue
A  False     True
B  False    False
C   True    False

When other is a Series, the columns of a DataFrame are aligned with the index of other and broadcast:

>>> df != pd.Series([100, 250], index=["cost", "revenue"])
    cost  revenue
A   True     True
B   True    False
C  False     True

Use the method to control the broadcast axis:

>>> df.ne(pd.Series([100, 300], index=["A", "D"]), axis='index')
   cost  revenue
A  True    False
B  True     True
C  True     True
D  True     True

When comparing to an arbitrary sequence, the number of columns must match the number elements in other:

>>> df == [250, 100]
    cost  revenue
A   True     True
B  False    False
C  False    False

Use the method to control the axis:

>>> df.eq([250, 250, 100], axis='index')
    cost  revenue
A   True    False
B  False     True
C   True    False

Compare to a DataFrame of different shape.

>>> other = pd.DataFrame({'revenue': [300, 250, 100, 150]},
...                      index=['A', 'B', 'C', 'D'])
>>> other
   revenue
A      300
B      250
C      100
D      150
>>> df.gt(other)
    cost  revenue
A  False    False
B  False    False
C  False     True
D  False    False

Compare to a MultiIndex by level.

>>> df_multindex = pd.DataFrame({'cost': [250, 150, 100, 150, 300, 220],
...                              'revenue': [100, 250, 300, 200, 175, 225]},
...                             index=[['Q1', 'Q1', 'Q1', 'Q2', 'Q2', 'Q2'],
...                                    ['A', 'B', 'C', 'A', 'B', 'C']])
>>> df_multindex
      cost  revenue
Q1 A   250      100
   B   150      250
   C   100      300
Q2 A   150      200
   B   300      175
   C   220      225
>>> df.le(df_multindex, level=1)
       cost  revenue
Q1 A   True     True
   B   True     True
   C   True     True
Q2 A  False     True
   B   True    False
   C   True    False
get(key, default=None)

Get item from object for given key (ex: DataFrame column).

Returns default value if not found.

Parameters

key (object) –

Returns

value

Return type

same type as items contained in object

groupby(by=None, axis=0, level=None, as_index: bool = True, sort: bool = True, group_keys: bool = True, squeeze: bool = <object object>, observed: bool = False, dropna: bool = True) → DataFrameGroupBy

Group DataFrame using a mapper or by a Series of columns.

A groupby operation involves some combination of splitting the object, applying a function, and combining the results. This can be used to group large amounts of data and compute operations on these groups.

Parameters
  • by (mapping, function, label, or list of labels) – Used to determine the groups for the groupby. If by is a function, it’s called on each value of the object’s index. If a dict or Series is passed, the Series or dict VALUES will be used to determine the groups (the Series’ values are first aligned; see .align() method). If an ndarray is passed, the values are used as-is determine the groups. A label or list of labels may be passed to group by the columns in self. Notice that a tuple is interpreted as a (single) key.

  • axis ({0 or 'index', 1 or 'columns'}, default 0) – Split along rows (0) or columns (1).

  • level (int, level name, or sequence of such, default None) – If the axis is a MultiIndex (hierarchical), group by a particular level or levels.

  • as_index (bool, default True) – For aggregated output, return object with group labels as the index. Only relevant for DataFrame input. as_index=False is effectively “SQL-style” grouped output.

  • sort (bool, default True) – Sort group keys. Get better performance by turning this off. Note this does not influence the order of observations within each group. Groupby preserves the order of rows within each group.

  • group_keys (bool, default True) – When calling apply, add group keys to index to identify pieces.

  • squeeze (bool, default False) –

    Reduce the dimensionality of the return type if possible, otherwise return a consistent type.

    Deprecated since version 1.1.0.

  • observed (bool, default False) –

    This only applies if any of the groupers are Categoricals. If True: only show observed values for categorical groupers. If False: show all values for categorical groupers.

    New in version 0.23.0.

  • dropna (bool, default True) –

    If True, and if group keys contain NA values, NA values together with row/column will be dropped. If False, NA values will also be treated as the key in groups

    New in version 1.1.0.

Returns

Returns a groupby object that contains information about the groups.

Return type

DataFrameGroupBy

See also

resample()

Convenience method for frequency conversion and resampling of time series.

Notes

See the user guide for more.

Examples

>>> df = pd.DataFrame({'Animal': ['Falcon', 'Falcon',
...                               'Parrot', 'Parrot'],
...                    'Max Speed': [380., 370., 24., 26.]})
>>> df
   Animal  Max Speed
0  Falcon      380.0
1  Falcon      370.0
2  Parrot       24.0
3  Parrot       26.0
>>> df.groupby(['Animal']).mean()
        Max Speed
Animal
Falcon      375.0
Parrot       25.0

Hierarchical Indexes

We can groupby different levels of a hierarchical index using the level parameter:

>>> arrays = [['Falcon', 'Falcon', 'Parrot', 'Parrot'],
...           ['Captive', 'Wild', 'Captive', 'Wild']]
>>> index = pd.MultiIndex.from_arrays(arrays, names=('Animal', 'Type'))
>>> df = pd.DataFrame({'Max Speed': [390., 350., 30., 20.]},
...                   index=index)
>>> df
                Max Speed
Animal Type
Falcon Captive      390.0
       Wild         350.0
Parrot Captive       30.0
       Wild          20.0
>>> df.groupby(level=0).mean()
        Max Speed
Animal
Falcon      370.0
Parrot       25.0
>>> df.groupby(level="Type").mean()
         Max Speed
Type
Captive      210.0
Wild         185.0

We can also choose to include NA in group keys or not by setting dropna parameter, the default setting is True:

>>> l = [[1, 2, 3], [1, None, 4], [2, 1, 3], [1, 2, 2]]
>>> df = pd.DataFrame(l, columns=["a", "b", "c"])
>>> df.groupby(by=["b"]).sum()
    a   c
b
1.0 2   3
2.0 2   5
>>> df.groupby(by=["b"], dropna=False).sum()
    a   c
b
1.0 2   3
2.0 2   5
NaN 1   4
>>> l = [["a", 12, 12], [None, 12.3, 33.], ["b", 12.3, 123], ["a", 1, 1]]
>>> df = pd.DataFrame(l, columns=["a", "b", "c"])
>>> df.groupby(by="a").sum()
    b     c
a
a   13.0   13.0
b   12.3  123.0
>>> df.groupby(by="a", dropna=False).sum()
    b     c
a
a   13.0   13.0
b   12.3  123.0
NaN 12.3   33.0
gt(other, axis='columns', level=None)

Get Greater than of dataframe and other, element-wise (binary operator gt).

Among flexible wrappers (eq, ne, le, lt, ge, gt) to comparison operators.

Equivalent to ==, =!, <=, <, >=, > with support to choose axis (rows or columns) and level for comparison.

Parameters
  • other (scalar, sequence, Series, or DataFrame) – Any single or multiple element data structure, or list-like object.

  • axis ({0 or 'index', 1 or 'columns'}, default 'columns') – Whether to compare by the index (0 or ‘index’) or columns (1 or ‘columns’).

  • level (int or label) – Broadcast across a level, matching Index values on the passed MultiIndex level.

Returns

Result of the comparison.

Return type

DataFrame of bool

See also

DataFrame.eq()

Compare DataFrames for equality elementwise.

DataFrame.ne()

Compare DataFrames for inequality elementwise.

DataFrame.le()

Compare DataFrames for less than inequality or equality elementwise.

DataFrame.lt()

Compare DataFrames for strictly less than inequality elementwise.

DataFrame.ge()

Compare DataFrames for greater than inequality or equality elementwise.

DataFrame.gt()

Compare DataFrames for strictly greater than inequality elementwise.

Notes

Mismatched indices will be unioned together. NaN values are considered different (i.e. NaN != NaN).

Examples

>>> df = pd.DataFrame({'cost': [250, 150, 100],
...                    'revenue': [100, 250, 300]},
...                   index=['A', 'B', 'C'])
>>> df
   cost  revenue
A   250      100
B   150      250
C   100      300

Comparison with a scalar, using either the operator or method:

>>> df == 100
    cost  revenue
A  False     True
B  False    False
C   True    False
>>> df.eq(100)
    cost  revenue
A  False     True
B  False    False
C   True    False

When other is a Series, the columns of a DataFrame are aligned with the index of other and broadcast:

>>> df != pd.Series([100, 250], index=["cost", "revenue"])
    cost  revenue
A   True     True
B   True    False
C  False     True

Use the method to control the broadcast axis:

>>> df.ne(pd.Series([100, 300], index=["A", "D"]), axis='index')
   cost  revenue
A  True    False
B  True     True
C  True     True
D  True     True

When comparing to an arbitrary sequence, the number of columns must match the number elements in other:

>>> df == [250, 100]
    cost  revenue
A   True     True
B  False    False
C  False    False

Use the method to control the axis:

>>> df.eq([250, 250, 100], axis='index')
    cost  revenue
A   True    False
B  False     True
C   True    False

Compare to a DataFrame of different shape.

>>> other = pd.DataFrame({'revenue': [300, 250, 100, 150]},
...                      index=['A', 'B', 'C', 'D'])
>>> other
   revenue
A      300
B      250
C      100
D      150
>>> df.gt(other)
    cost  revenue
A  False    False
B  False    False
C  False     True
D  False    False

Compare to a MultiIndex by level.

>>> df_multindex = pd.DataFrame({'cost': [250, 150, 100, 150, 300, 220],
...                              'revenue': [100, 250, 300, 200, 175, 225]},
...                             index=[['Q1', 'Q1', 'Q1', 'Q2', 'Q2', 'Q2'],
...                                    ['A', 'B', 'C', 'A', 'B', 'C']])
>>> df_multindex
      cost  revenue
Q1 A   250      100
   B   150      250
   C   100      300
Q2 A   150      200
   B   300      175
   C   220      225
>>> df.le(df_multindex, level=1)
       cost  revenue
Q1 A   True     True
   B   True     True
   C   True     True
Q2 A  False     True
   B   True    False
   C   True    False
head(n: int = 5) → FrameOrSeries

Return the first n rows.

This function returns the first n rows for the object based on position. It is useful for quickly testing if your object has the right type of data in it.

For negative values of n, this function returns all rows except the last n rows, equivalent to df[:-n].

Parameters

n (int, default 5) – Number of rows to select.

Returns

The first n rows of the caller object.

Return type

same type as caller

See also

DataFrame.tail()

Returns the last n rows.

Examples

>>> df = pd.DataFrame({'animal': ['alligator', 'bee', 'falcon', 'lion',
...                    'monkey', 'parrot', 'shark', 'whale', 'zebra']})
>>> df
      animal
0  alligator
1        bee
2     falcon
3       lion
4     monkey
5     parrot
6      shark
7      whale
8      zebra

Viewing the first 5 lines

>>> df.head()
      animal
0  alligator
1        bee
2     falcon
3       lion
4     monkey

Viewing the first n lines (three in this case)

>>> df.head(3)
      animal
0  alligator
1        bee
2     falcon

For negative values of n

>>> df.head(-3)
      animal
0  alligator
1        bee
2     falcon
3       lion
4     monkey
5     parrot
hist(column: Union[Hashable, None, Sequence[Optional[Hashable]]] = None, by=None, grid: bool = True, xlabelsize: Optional[int] = None, xrot: Optional[float] = None, ylabelsize: Optional[int] = None, yrot: Optional[float] = None, ax=None, sharex: bool = False, sharey: bool = False, figsize: Optional[Tuple[int, int]] = None, layout: Optional[Tuple[int, int]] = None, bins: Union[int, Sequence[int]] = 10, backend: Optional[str] = None, legend: bool = False, **kwargs)

Make a histogram of the DataFrame’s.

A histogram is a representation of the distribution of data. This function calls matplotlib.pyplot.hist(), on each series in the DataFrame, resulting in one histogram per column.

Parameters
  • data (DataFrame) – The pandas object holding the data.

  • column (str or sequence) – If passed, will be used to limit data to a subset of columns.

  • by (object, optional) – If passed, then used to form histograms for separate groups.

  • grid (bool, default True) – Whether to show axis grid lines.

  • xlabelsize (int, default None) – If specified changes the x-axis label size.

  • xrot (float, default None) – Rotation of x axis labels. For example, a value of 90 displays the x labels rotated 90 degrees clockwise.

  • ylabelsize (int, default None) – If specified changes the y-axis label size.

  • yrot (float, default None) – Rotation of y axis labels. For example, a value of 90 displays the y labels rotated 90 degrees clockwise.

  • ax (Matplotlib axes object, default None) – The axes to plot the histogram on.

  • sharex (bool, default True if ax is None else False) – In case subplots=True, share x axis and set some x axis labels to invisible; defaults to True if ax is None otherwise False if an ax is passed in. Note that passing in both an ax and sharex=True will alter all x axis labels for all subplots in a figure.

  • sharey (bool, default False) – In case subplots=True, share y axis and set some y axis labels to invisible.

  • figsize (tuple) – The size in inches of the figure to create. Uses the value in matplotlib.rcParams by default.

  • layout (tuple, optional) – Tuple of (rows, columns) for the layout of the histograms.

  • bins (int or sequence, default 10) – Number of histogram bins to be used. If an integer is given, bins + 1 bin edges are calculated and returned. If bins is a sequence, gives bin edges, including left edge of first bin and right edge of last bin. In this case, bins is returned unmodified.

  • backend (str, default None) –

    Backend to use instead of the backend specified in the option plotting.backend. For instance, ‘matplotlib’. Alternatively, to specify the plotting.backend for the whole session, set pd.options.plotting.backend.

    New in version 1.0.0.

  • legend (bool, default False) –

    Whether to show the legend.

    New in version 1.1.0.

  • **kwargs – All other plotting keyword arguments to be passed to matplotlib.pyplot.hist().

Returns

Return type

matplotlib.AxesSubplot or numpy.ndarray of them

See also

matplotlib.pyplot.hist()

Plot a histogram using matplotlib.

Examples

This example draws a histogram based on the length and width of some animals, displayed in three bins

property iat

Access a single value for a row/column pair by integer position.

Similar to iloc, in that both provide integer-based lookups. Use iat if you only need to get or set a single value in a DataFrame or Series.

Raises

IndexError – When integer position is out of bounds.

See also

DataFrame.at

Access a single value for a row/column label pair.

DataFrame.loc

Access a group of rows and columns by label(s).

DataFrame.iloc

Access a group of rows and columns by integer position(s).

Examples

>>> df = pd.DataFrame([[0, 2, 3], [0, 4, 1], [10, 20, 30]],
...                   columns=['A', 'B', 'C'])
>>> df
    A   B   C
0   0   2   3
1   0   4   1
2  10  20  30

Get value at specified row/column pair

>>> df.iat[1, 2]
1

Set value at specified row/column pair

>>> df.iat[1, 2] = 10
>>> df.iat[1, 2]
10

Get value within a series

>>> df.loc[0].iat[1]
2
idxmax(axis=0, skipna=True) → pandas.core.series.Series

Return index of first occurrence of maximum over requested axis.

NA/null values are excluded.

Parameters
  • axis ({0 or 'index', 1 or 'columns'}, default 0) – The axis to use. 0 or ‘index’ for row-wise, 1 or ‘columns’ for column-wise.

  • skipna (bool, default True) – Exclude NA/null values. If an entire row/column is NA, the result will be NA.

Returns

Indexes of maxima along the specified axis.

Return type

Series

Raises

ValueError

  • If the row/column is empty

See also

Series.idxmax()

Return index of the maximum element.

Notes

This method is the DataFrame version of ndarray.argmax.

Examples

Consider a dataset containing food consumption in Argentina.

>>> df = pd.DataFrame({'consumption': [10.51, 103.11, 55.48],
...                    'co2_emissions': [37.2, 19.66, 1712]},
...                    index=['Pork', 'Wheat Products', 'Beef'])
>>> df
                consumption  co2_emissions
Pork                  10.51         37.20
Wheat Products       103.11         19.66
Beef                  55.48       1712.00

By default, it returns the index for the maximum value in each column.

>>> df.idxmax()
consumption     Wheat Products
co2_emissions             Beef
dtype: object

To return the index for the maximum value in each row, use axis="columns".

>>> df.idxmax(axis="columns")
Pork              co2_emissions
Wheat Products     consumption
Beef              co2_emissions
dtype: object
idxmin(axis=0, skipna=True) → pandas.core.series.Series

Return index of first occurrence of minimum over requested axis.

NA/null values are excluded.

Parameters
  • axis ({0 or 'index', 1 or 'columns'}, default 0) – The axis to use. 0 or ‘index’ for row-wise, 1 or ‘columns’ for column-wise.

  • skipna (bool, default True) – Exclude NA/null values. If an entire row/column is NA, the result will be NA.

Returns

Indexes of minima along the specified axis.

Return type

Series

Raises

ValueError

  • If the row/column is empty

See also

Series.idxmin()

Return index of the minimum element.

Notes

This method is the DataFrame version of ndarray.argmin.

Examples

Consider a dataset containing food consumption in Argentina.

>>> df = pd.DataFrame({'consumption': [10.51, 103.11, 55.48],
...                    'co2_emissions': [37.2, 19.66, 1712]},
...                    index=['Pork', 'Wheat Products', 'Beef'])
>>> df
                consumption  co2_emissions
Pork                  10.51         37.20
Wheat Products       103.11         19.66
Beef                  55.48       1712.00

By default, it returns the index for the minimum value in each column.

>>> df.idxmin()
consumption                Pork
co2_emissions    Wheat Products
dtype: object

To return the index for the minimum value in each row, use axis="columns".

>>> df.idxmin(axis="columns")
Pork                consumption
Wheat Products    co2_emissions
Beef                consumption
dtype: object
property iloc

Purely integer-location based indexing for selection by position.

.iloc[] is primarily integer position based (from 0 to length-1 of the axis), but may also be used with a boolean array.

Allowed inputs are:

  • An integer, e.g. 5.

  • A list or array of integers, e.g. [4, 3, 0].

  • A slice object with ints, e.g. 1:7.

  • A boolean array.

  • A callable function with one argument (the calling Series or DataFrame) and that returns valid output for indexing (one of the above). This is useful in method chains, when you don’t have a reference to the calling object, but would like to base your selection on some value.

.iloc will raise IndexError if a requested indexer is out-of-bounds, except slice indexers which allow out-of-bounds indexing (this conforms with python/numpy slice semantics).

See more at Selection by Position.

See also

DataFrame.iat

Fast integer location scalar accessor.

DataFrame.loc

Purely label-location based indexer for selection by label.

Series.iloc

Purely integer-location based indexing for selection by position.

Examples

>>> mydict = [{'a': 1, 'b': 2, 'c': 3, 'd': 4},
...           {'a': 100, 'b': 200, 'c': 300, 'd': 400},
...           {'a': 1000, 'b': 2000, 'c': 3000, 'd': 4000 }]
>>> df = pd.DataFrame(mydict)
>>> df
      a     b     c     d
0     1     2     3     4
1   100   200   300   400
2  1000  2000  3000  4000

Indexing just the rows

With a scalar integer.

>>> type(df.iloc[0])
<class 'pandas.core.series.Series'>
>>> df.iloc[0]
a    1
b    2
c    3
d    4
Name: 0, dtype: int64

With a list of integers.

>>> df.iloc[[0]]
   a  b  c  d
0  1  2  3  4
>>> type(df.iloc[[0]])
<class 'pandas.core.frame.DataFrame'>
>>> df.iloc[[0, 1]]
     a    b    c    d
0    1    2    3    4
1  100  200  300  400

With a slice object.

>>> df.iloc[:3]
      a     b     c     d
0     1     2     3     4
1   100   200   300   400
2  1000  2000  3000  4000

With a boolean mask the same length as the index.

>>> df.iloc[[True, False, True]]
      a     b     c     d
0     1     2     3     4
2  1000  2000  3000  4000

With a callable, useful in method chains. The x passed to the lambda is the DataFrame being sliced. This selects the rows whose index label even.

>>> df.iloc[lambda x: x.index % 2 == 0]
      a     b     c     d
0     1     2     3     4
2  1000  2000  3000  4000

Indexing both axes

You can mix the indexer types for the index and columns. Use : to select the entire axis.

With scalar integers.

>>> df.iloc[0, 1]
2

With lists of integers.

>>> df.iloc[[0, 2], [1, 3]]
      b     d
0     2     4
2  2000  4000

With slice objects.

>>> df.iloc[1:3, 0:3]
      a     b     c
1   100   200   300
2  1000  2000  3000

With a boolean array whose length matches the columns.

>>> df.iloc[:, [True, False, True, False]]
      a     c
0     1     3
1   100   300
2  1000  3000

With a callable function that expects the Series or DataFrame.

>>> df.iloc[:, lambda df: [0, 2]]
      a     c
0     1     3
1   100   300
2  1000  3000
index

The index (row labels) of the DataFrame.

infer_objects() → FrameOrSeries

Attempt to infer better dtypes for object columns.

Attempts soft conversion of object-dtyped columns, leaving non-object and unconvertible columns unchanged. The inference rules are the same as during normal Series/DataFrame construction.

Returns

converted

Return type

same type as input object

See also

to_datetime()

Convert argument to datetime.

to_timedelta()

Convert argument to timedelta.

to_numeric()

Convert argument to numeric type.

convert_dtypes()

Convert argument to best possible dtype.

Examples

>>> df = pd.DataFrame({"A": ["a", 1, 2, 3]})
>>> df = df.iloc[1:]
>>> df
   A
1  1
2  2
3  3
>>> df.dtypes
A    object
dtype: object
>>> df.infer_objects().dtypes
A    int64
dtype: object
info(verbose: Optional[bool] = None, buf: Optional[IO[str]] = None, max_cols: Optional[int] = None, memory_usage: Union[bool, str, None] = None, null_counts: Optional[bool] = None) → None

Print a concise summary of a DataFrame.

This method prints information about a DataFrame including the index dtype and columns, non-null values and memory usage.

Parameters
  • data (DataFrame) – DataFrame to print information about.

  • verbose (bool, optional) – Whether to print the full summary. By default, the setting in pandas.options.display.max_info_columns is followed.

  • buf (writable buffer, defaults to sys.stdout) – Where to send the output. By default, the output is printed to sys.stdout. Pass a writable buffer if you need to further process the output.

  • max_cols (int, optional) – When to switch from the verbose to the truncated output. If the DataFrame has more than max_cols columns, the truncated output is used. By default, the setting in pandas.options.display.max_info_columns is used.

  • memory_usage (bool, str, optional) –

    Specifies whether total memory usage of the DataFrame elements (including the index) should be displayed. By default, this follows the pandas.options.display.memory_usage setting.

    True always show memory usage. False never shows memory usage. A value of ‘deep’ is equivalent to “True with deep introspection”. Memory usage is shown in human-readable units (base-2 representation). Without deep introspection a memory estimation is made based in column dtype and number of rows assuming values consume the same memory amount for corresponding dtypes. With deep memory introspection, a real memory usage calculation is performed at the cost of computational resources.

  • null_counts (bool, optional) – Whether to show the non-null counts. By default, this is shown only if the DataFrame is smaller than pandas.options.display.max_info_rows and pandas.options.display.max_info_columns. A value of True always shows the counts, and False never shows the counts.

Returns

This method prints a summary of a DataFrame and returns None.

Return type

None

See also

DataFrame.describe()

Generate descriptive statistics of DataFrame columns.

DataFrame.memory_usage()

Memory usage of DataFrame columns.

Examples

>>> int_values = [1, 2, 3, 4, 5]
>>> text_values = ['alpha', 'beta', 'gamma', 'delta', 'epsilon']
>>> float_values = [0.0, 0.25, 0.5, 0.75, 1.0]
>>> df = pd.DataFrame({"int_col": int_values, "text_col": text_values,
...                   "float_col": float_values})
>>> df
    int_col text_col  float_col
0        1    alpha       0.00
1        2     beta       0.25
2        3    gamma       0.50
3        4    delta       0.75
4        5  epsilon       1.00

Prints information of all columns:

>>> df.info(verbose=True)
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 5 entries, 0 to 4
Data columns (total 3 columns):
 #   Column     Non-Null Count  Dtype
---  ------     --------------  -----
 0   int_col    5 non-null      int64
 1   text_col   5 non-null      object
 2   float_col  5 non-null      float64
dtypes: float64(1), int64(1), object(1)
memory usage: 248.0+ bytes

Prints a summary of columns count and its dtypes but not per column information:

>>> df.info(verbose=False)
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 5 entries, 0 to 4
Columns: 3 entries, int_col to float_col
dtypes: float64(1), int64(1), object(1)
memory usage: 248.0+ bytes

Pipe output of DataFrame.info to buffer instead of sys.stdout, get buffer content and writes to a text file:

>>> import io
>>> buffer = io.StringIO()
>>> df.info(buf=buffer)
>>> s = buffer.getvalue()
>>> with open("df_info.txt", "w",
...           encoding="utf-8") as f:  
...     f.write(s)
260

The memory_usage parameter allows deep introspection mode, specially useful for big DataFrames and fine-tune memory optimization:

>>> random_strings_array = np.random.choice(['a', 'b', 'c'], 10 ** 6)
>>> df = pd.DataFrame({
...     'column_1': np.random.choice(['a', 'b', 'c'], 10 ** 6),
...     'column_2': np.random.choice(['a', 'b', 'c'], 10 ** 6),
...     'column_3': np.random.choice(['a', 'b', 'c'], 10 ** 6)
... })
>>> df.info()
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 1000000 entries, 0 to 999999
Data columns (total 3 columns):
 #   Column    Non-Null Count    Dtype
---  ------    --------------    -----
 0   column_1  1000000 non-null  object
 1   column_2  1000000 non-null  object
 2   column_3  1000000 non-null  object
dtypes: object(3)
memory usage: 22.9+ MB
>>> df.info(memory_usage='deep')
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 1000000 entries, 0 to 999999
Data columns (total 3 columns):
 #   Column    Non-Null Count    Dtype
---  ------    --------------    -----
 0   column_1  1000000 non-null  object
 1   column_2  1000000 non-null  object
 2   column_3  1000000 non-null  object
dtypes: object(3)
memory usage: 188.8 MB
insert(loc, column, value, allow_duplicates=False) → None

Insert column into DataFrame at specified location.

Raises a ValueError if column is already contained in the DataFrame, unless allow_duplicates is set to True.

Parameters
  • loc (int) – Insertion index. Must verify 0 <= loc <= len(columns).

  • column (str, number, or hashable object) – Label of the inserted column.

  • value (int, Series, or array-like) –

  • allow_duplicates (bool, optional) –

interpolate(method: str = 'linear', axis: Union[str, int] = 0, limit: Optional[int] = None, inplace: bool = False, limit_direction: Optional[str] = None, limit_area: Optional[str] = None, downcast: Optional[str] = None, **kwargs) → Optional[FrameOrSeries]

Please note that only method='linear' is supported for DataFrame/Series with a MultiIndex.

Parameters
  • method (str, default 'linear') –

    Interpolation technique to use. One of:

    • ’linear’: Ignore the index and treat the values as equally spaced. This is the only method supported on MultiIndexes.

    • ’time’: Works on daily and higher resolution data to interpolate given length of interval.

    • ’index’, ‘values’: use the actual numerical values of the index.

    • ’pad’: Fill in NaNs using existing values.

    • ’nearest’, ‘zero’, ‘slinear’, ‘quadratic’, ‘cubic’, ‘spline’, ‘barycentric’, ‘polynomial’: Passed to scipy.interpolate.interp1d. These methods use the numerical values of the index. Both ‘polynomial’ and ‘spline’ require that you also specify an order (int), e.g. df.interpolate(method='polynomial', order=5).

    • ’krogh’, ‘piecewise_polynomial’, ‘spline’, ‘pchip’, ‘akima’, ‘cubicspline’: Wrappers around the SciPy interpolation methods of similar names. See Notes.

    • ’from_derivatives’: Refers to scipy.interpolate.BPoly.from_derivatives which replaces ‘piecewise_polynomial’ interpolation method in scipy 0.18.

  • axis ({{0 or 'index', 1 or 'columns', None}}, default None) – Axis to interpolate along.

  • limit (int, optional) – Maximum number of consecutive NaNs to fill. Must be greater than 0.

  • inplace (bool, default False) – Update the data in place if possible.

  • limit_direction ({{'forward', 'backward', 'both'}}, Optional) –

    Consecutive NaNs will be filled in this direction.

    If limit is specified:
    • If ‘method’ is ‘pad’ or ‘ffill’, ‘limit_direction’ must be ‘forward’.

    • If ‘method’ is ‘backfill’ or ‘bfill’, ‘limit_direction’ must be ‘backwards’.

    If ‘limit’ is not specified:
    • If ‘method’ is ‘backfill’ or ‘bfill’, the default is ‘backward’

    • else the default is ‘forward’

    Changed in version 1.1.0: raises ValueError if limit_direction is ‘forward’ or ‘both’ and method is ‘backfill’ or ‘bfill’. raises ValueError if limit_direction is ‘backward’ or ‘both’ and method is ‘pad’ or ‘ffill’.

  • limit_area ({{None, ‘inside’, ‘outside’}}, default None) –

    If limit is specified, consecutive NaNs will be filled with this restriction.

    • None: No fill restriction.

    • ’inside’: Only fill NaNs surrounded by valid values (interpolate).

    • ’outside’: Only fill NaNs outside valid values (extrapolate).

    New in version 0.23.0.

  • downcast (optional, 'infer' or None, defaults to None) – Downcast dtypes if possible.

  • **kwargs – Keyword arguments to pass on to the interpolating function.

Returns

Returns the same object type as the caller, interpolated at some or all NaN values.

Return type

Series or DataFrame

See also

fillna()

Fill missing values using different methods.

scipy.interpolate.Akima1DInterpolator()

Piecewise cubic polynomials (Akima interpolator).

scipy.interpolate.BPoly.from_derivatives()

Piecewise polynomial in the Bernstein basis.

scipy.interpolate.interp1d()

Interpolate a 1-D function.

scipy.interpolate.KroghInterpolator()

Interpolate polynomial (Krogh interpolator).

scipy.interpolate.PchipInterpolator()

PCHIP 1-d monotonic cubic interpolation.

scipy.interpolate.CubicSpline()

Cubic spline data interpolator.

Notes

The ‘krogh’, ‘piecewise_polynomial’, ‘spline’, ‘pchip’ and ‘akima’ methods are wrappers around the respective SciPy implementations of similar names. These use the actual numerical values of the index. For more information on their behavior, see the SciPy documentation and SciPy tutorial.

Examples

Filling in NaN in a Series via linear interpolation.

>>> s = pd.Series([0, 1, np.nan, 3])
>>> s
0    0.0
1    1.0
2    NaN
3    3.0
dtype: float64
>>> s.interpolate()
0    0.0
1    1.0
2    2.0
3    3.0
dtype: float64

Filling in NaN in a Series by padding, but filling at most two consecutive NaN at a time.

>>> s = pd.Series([np.nan, "single_one", np.nan,
...                "fill_two_more", np.nan, np.nan, np.nan,
...                4.71, np.nan])
>>> s
0              NaN
1       single_one
2              NaN
3    fill_two_more
4              NaN
5              NaN
6              NaN
7             4.71
8              NaN
dtype: object
>>> s.interpolate(method='pad', limit=2)
0              NaN
1       single_one
2       single_one
3    fill_two_more
4    fill_two_more
5    fill_two_more
6              NaN
7             4.71
8             4.71
dtype: object

Filling in NaN in a Series via polynomial interpolation or splines: Both ‘polynomial’ and ‘spline’ methods require that you also specify an order (int).

>>> s = pd.Series([0, 2, np.nan, 8])
>>> s.interpolate(method='polynomial', order=2)
0    0.000000
1    2.000000
2    4.666667
3    8.000000
dtype: float64

Fill the DataFrame forward (that is, going down) along each column using linear interpolation.

Note how the last entry in column ‘a’ is interpolated differently, because there is no entry after it to use for interpolation. Note how the first entry in column ‘b’ remains NaN, because there is no entry before it to use for interpolation.

>>> df = pd.DataFrame([(0.0, np.nan, -1.0, 1.0),
...                    (np.nan, 2.0, np.nan, np.nan),
...                    (2.0, 3.0, np.nan, 9.0),
...                    (np.nan, 4.0, -4.0, 16.0)],
...                   columns=list('abcd'))
>>> df
     a    b    c     d
0  0.0  NaN -1.0   1.0
1  NaN  2.0  NaN   NaN
2  2.0  3.0  NaN   9.0
3  NaN  4.0 -4.0  16.0
>>> df.interpolate(method='linear', limit_direction='forward', axis=0)
     a    b    c     d
0  0.0  NaN -1.0   1.0
1  1.0  2.0 -2.0   5.0
2  2.0  3.0 -3.0   9.0
3  2.0  4.0 -4.0  16.0

Using polynomial interpolation.

>>> df['d'].interpolate(method='polynomial', order=2)
0     1.0
1     4.0
2     9.0
3    16.0
Name: d, dtype: float64
isin(values) → pandas.core.frame.DataFrame

Whether each element in the DataFrame is contained in values.

Parameters

values (iterable, Series, DataFrame or dict) – The result will only be true at a location if all the labels match. If values is a Series, that’s the index. If values is a dict, the keys must be the column names, which must match. If values is a DataFrame, then both the index and column labels must match.

Returns

DataFrame of booleans showing whether each element in the DataFrame is contained in values.

Return type

DataFrame

See also

DataFrame.eq()

Equality test for DataFrame.

Series.isin()

Equivalent method on Series.

Series.str.contains()

Test if pattern or regex is contained within a string of a Series or Index.

Examples

>>> df = pd.DataFrame({'num_legs': [2, 4], 'num_wings': [2, 0]},
...                   index=['falcon', 'dog'])
>>> df
        num_legs  num_wings
falcon         2          2
dog            4          0

When values is a list check whether every value in the DataFrame is present in the list (which animals have 0 or 2 legs or wings)

>>> df.isin([0, 2])
        num_legs  num_wings
falcon      True       True
dog        False       True

When values is a dict, we can pass values to check for each column separately:

>>> df.isin({'num_wings': [0, 3]})
        num_legs  num_wings
falcon     False      False
dog        False       True

When values is a Series or DataFrame the index and column must match. Note that ‘falcon’ does not match based on the number of legs in df2.

>>> other = pd.DataFrame({'num_legs': [8, 2], 'num_wings': [0, 2]},
...                      index=['spider', 'falcon'])
>>> df.isin(other)
        num_legs  num_wings
falcon      True       True
dog        False      False
isna() → pandas.core.frame.DataFrame

Detect missing values.

Return a boolean same-sized object indicating if the values are NA. NA values, such as None or numpy.NaN, gets mapped to True values. Everything else gets mapped to False values. Characters such as empty strings '' or numpy.inf are not considered NA values (unless you set pandas.options.mode.use_inf_as_na = True).

Returns

Mask of bool values for each element in DataFrame that indicates whether an element is not an NA value.

Return type

DataFrame

See also

DataFrame.isnull()

Alias of isna.

DataFrame.notna()

Boolean inverse of isna.

DataFrame.dropna()

Omit axes labels with missing values.

isna()

Top-level isna.

Examples

Show which entries in a DataFrame are NA.

>>> df = pd.DataFrame({'age': [5, 6, np.NaN],
...                    'born': [pd.NaT, pd.Timestamp('1939-05-27'),
...                             pd.Timestamp('1940-04-25')],
...                    'name': ['Alfred', 'Batman', ''],
...                    'toy': [None, 'Batmobile', 'Joker']})
>>> df
   age       born    name        toy
0  5.0        NaT  Alfred       None
1  6.0 1939-05-27  Batman  Batmobile
2  NaN 1940-04-25              Joker
>>> df.isna()
     age   born   name    toy
0  False   True  False   True
1  False  False  False  False
2   True  False  False  False

Show which entries in a Series are NA.

>>> ser = pd.Series([5, 6, np.NaN])
>>> ser
0    5.0
1    6.0
2    NaN
dtype: float64
>>> ser.isna()
0    False
1    False
2     True
dtype: bool
isnull() → pandas.core.frame.DataFrame

Detect missing values.

Return a boolean same-sized object indicating if the values are NA. NA values, such as None or numpy.NaN, gets mapped to True values. Everything else gets mapped to False values. Characters such as empty strings '' or numpy.inf are not considered NA values (unless you set pandas.options.mode.use_inf_as_na = True).

Returns

Mask of bool values for each element in DataFrame that indicates whether an element is not an NA value.

Return type

DataFrame

See also

DataFrame.isnull()

Alias of isna.

DataFrame.notna()

Boolean inverse of isna.

DataFrame.dropna()

Omit axes labels with missing values.

isna()

Top-level isna.

Examples

Show which entries in a DataFrame are NA.

>>> df = pd.DataFrame({'age': [5, 6, np.NaN],
...                    'born': [pd.NaT, pd.Timestamp('1939-05-27'),
...                             pd.Timestamp('1940-04-25')],
...                    'name': ['Alfred', 'Batman', ''],
...                    'toy': [None, 'Batmobile', 'Joker']})
>>> df
   age       born    name        toy
0  5.0        NaT  Alfred       None
1  6.0 1939-05-27  Batman  Batmobile
2  NaN 1940-04-25              Joker
>>> df.isna()
     age   born   name    toy
0  False   True  False   True
1  False  False  False  False
2   True  False  False  False

Show which entries in a Series are NA.

>>> ser = pd.Series([5, 6, np.NaN])
>>> ser
0    5.0
1    6.0
2    NaN
dtype: float64
>>> ser.isna()
0    False
1    False
2     True
dtype: bool
items() → Iterable[Tuple[Optional[Hashable], pandas.core.series.Series]]

Iterate over (column name, Series) pairs.

Iterates over the DataFrame columns, returning a tuple with the column name and the content as a Series.

Yields
  • label (object) – The column names for the DataFrame being iterated over.

  • content (Series) – The column entries belonging to each label, as a Series.

See also

DataFrame.iterrows()

Iterate over DataFrame rows as (index, Series) pairs.

DataFrame.itertuples()

Iterate over DataFrame rows as namedtuples of the values.

Examples

>>> df = pd.DataFrame({'species': ['bear', 'bear', 'marsupial'],
...                   'population': [1864, 22000, 80000]},
...                   index=['panda', 'polar', 'koala'])
>>> df
        species   population
panda   bear      1864
polar   bear      22000
koala   marsupial 80000
>>> for label, content in df.items():
...     print(f'label: {label}')
...     print(f'content: {content}', sep='\n')
...
label: species
content:
panda         bear
polar         bear
koala    marsupial
Name: species, dtype: object
label: population
content:
panda     1864
polar    22000
koala    80000
Name: population, dtype: int64
iteritems() → Iterable[Tuple[Optional[Hashable], pandas.core.series.Series]]

Iterate over (column name, Series) pairs.

Iterates over the DataFrame columns, returning a tuple with the column name and the content as a Series.

Yields
  • label (object) – The column names for the DataFrame being iterated over.

  • content (Series) – The column entries belonging to each label, as a Series.

See also

DataFrame.iterrows()

Iterate over DataFrame rows as (index, Series) pairs.

DataFrame.itertuples()

Iterate over DataFrame rows as namedtuples of the values.

Examples

>>> df = pd.DataFrame({'species': ['bear', 'bear', 'marsupial'],
...                   'population': [1864, 22000, 80000]},
...                   index=['panda', 'polar', 'koala'])
>>> df
        species   population
panda   bear      1864
polar   bear      22000
koala   marsupial 80000
>>> for label, content in df.items():
...     print(f'label: {label}')
...     print(f'content: {content}', sep='\n')
...
label: species
content:
panda         bear
polar         bear
koala    marsupial
Name: species, dtype: object
label: population
content:
panda     1864
polar    22000
koala    80000
Name: population, dtype: int64
iterrows() → Iterable[Tuple[Optional[Hashable], pandas.core.series.Series]]

Iterate over DataFrame rows as (index, Series) pairs.

Yields
  • index (label or tuple of label) – The index of the row. A tuple for a MultiIndex.

  • data (Series) – The data of the row as a Series.

  • it (generator) – A generator that iterates over the rows of the frame.

See also

DataFrame.itertuples()

Iterate over DataFrame rows as namedtuples of the values.

DataFrame.items()

Iterate over (column name, Series) pairs.

Notes

  1. Because iterrows returns a Series for each row, it does not preserve dtypes across the rows (dtypes are preserved across columns for DataFrames). For example,

    >>> df = pd.DataFrame([[1, 1.5]], columns=['int', 'float'])
    >>> row = next(df.iterrows())[1]
    >>> row
    int      1.0
    float    1.5
    Name: 0, dtype: float64
    >>> print(row['int'].dtype)
    float64
    >>> print(df['int'].dtype)
    int64
    

    To preserve dtypes while iterating over the rows, it is better to use itertuples() which returns namedtuples of the values and which is generally faster than iterrows.

  2. You should never modify something you are iterating over. This is not guaranteed to work in all cases. Depending on the data types, the iterator returns a copy and not a view, and writing to it will have no effect.

itertuples(index=True, name='Pandas')

Iterate over DataFrame rows as namedtuples.

Parameters
  • index (bool, default True) – If True, return the index as the first element of the tuple.

  • name (str or None, default "Pandas") – The name of the returned namedtuples or None to return regular tuples.

Returns

An object to iterate over namedtuples for each row in the DataFrame with the first field possibly being the index and following fields being the column values.

Return type

iterator

See also

DataFrame.iterrows()

Iterate over DataFrame rows as (index, Series) pairs.

DataFrame.items()

Iterate over (column name, Series) pairs.

Notes

The column names will be renamed to positional names if they are invalid Python identifiers, repeated, or start with an underscore. On python versions < 3.7 regular tuples are returned for DataFrames with a large number of columns (>254).

Examples

>>> df = pd.DataFrame({'num_legs': [4, 2], 'num_wings': [0, 2]},
...                   index=['dog', 'hawk'])
>>> df
      num_legs  num_wings
dog          4          0
hawk         2          2
>>> for row in df.itertuples():
...     print(row)
...
Pandas(Index='dog', num_legs=4, num_wings=0)
Pandas(Index='hawk', num_legs=2, num_wings=2)

By setting the index parameter to False we can remove the index as the first element of the tuple:

>>> for row in df.itertuples(index=False):
...     print(row)
...
Pandas(num_legs=4, num_wings=0)
Pandas(num_legs=2, num_wings=2)

With the name parameter set we set a custom name for the yielded namedtuples:

>>> for row in df.itertuples(name='Animal'):
...     print(row)
...
Animal(Index='dog', num_legs=4, num_wings=0)
Animal(Index='hawk', num_legs=2, num_wings=2)
join(other, on=None, how='left', lsuffix='', rsuffix='', sort=False) → pandas.core.frame.DataFrame

Join columns of another DataFrame.

Join columns with other DataFrame either on index or on a key column. Efficiently join multiple DataFrame objects by index at once by passing a list.

Parameters
  • other (DataFrame, Series, or list of DataFrame) – Index should be similar to one of the columns in this one. If a Series is passed, its name attribute must be set, and that will be used as the column name in the resulting joined DataFrame.

  • on (str, list of str, or array-like, optional) – Column or index level name(s) in the caller to join on the index in other, otherwise joins index-on-index. If multiple values given, the other DataFrame must have a MultiIndex. Can pass an array as the join key if it is not already contained in the calling DataFrame. Like an Excel VLOOKUP operation.

  • how ({'left', 'right', 'outer', 'inner'}, default 'left') –

    How to handle the operation of the two objects.

    • left: use calling frame’s index (or column if on is specified)

    • right: use other’s index.

    • outer: form union of calling frame’s index (or column if on is specified) with other’s index, and sort it. lexicographically.

    • inner: form intersection of calling frame’s index (or column if on is specified) with other’s index, preserving the order of the calling’s one.

  • lsuffix (str, default '') – Suffix to use from left frame’s overlapping columns.

  • rsuffix (str, default '') – Suffix to use from right frame’s overlapping columns.

  • sort (bool, default False) – Order result DataFrame lexicographically by the join key. If False, the order of the join key depends on the join type (how keyword).

Returns

A dataframe containing columns from both the caller and other.

Return type

DataFrame

See also

DataFrame.merge()

For column(s)-on-columns(s) operations.

Notes

Parameters on, lsuffix, and rsuffix are not supported when passing a list of DataFrame objects.

Support for specifying index levels as the on parameter was added in version 0.23.0.

Examples

>>> df = pd.DataFrame({'key': ['K0', 'K1', 'K2', 'K3', 'K4', 'K5'],
...                    'A': ['A0', 'A1', 'A2', 'A3', 'A4', 'A5']})
>>> df
  key   A
0  K0  A0
1  K1  A1
2  K2  A2
3  K3  A3
4  K4  A4
5  K5  A5
>>> other = pd.DataFrame({'key': ['K0', 'K1', 'K2'],
...                       'B': ['B0', 'B1', 'B2']})
>>> other
  key   B
0  K0  B0
1  K1  B1
2  K2  B2

Join DataFrames using their indexes.

>>> df.join(other, lsuffix='_caller', rsuffix='_other')
  key_caller   A key_other    B
0         K0  A0        K0   B0
1         K1  A1        K1   B1
2         K2  A2        K2   B2
3         K3  A3       NaN  NaN
4         K4  A4       NaN  NaN
5         K5  A5       NaN  NaN

If we want to join using the key columns, we need to set key to be the index in both df and other. The joined DataFrame will have key as its index.

>>> df.set_index('key').join(other.set_index('key'))
      A    B
key
K0   A0   B0
K1   A1   B1
K2   A2   B2
K3   A3  NaN
K4   A4  NaN
K5   A5  NaN

Another option to join using the key columns is to use the on parameter. DataFrame.join always uses other’s index but we can use any column in df. This method preserves the original DataFrame’s index in the result.

>>> df.join(other.set_index('key'), on='key')
  key   A    B
0  K0  A0   B0
1  K1  A1   B1
2  K2  A2   B2
3  K3  A3  NaN
4  K4  A4  NaN
5  K5  A5  NaN
keys()

Get the ‘info axis’ (see Indexing for more).

This is index for Series, columns for DataFrame.

Returns

Info axis.

Return type

Index

kurt(axis=None, skipna=None, level=None, numeric_only=None, **kwargs)

Return unbiased kurtosis over requested axis.

Kurtosis obtained using Fisher’s definition of kurtosis (kurtosis of normal == 0.0). Normalized by N-1.

Parameters
  • axis ({index (0), columns (1)}) – Axis for the function to be applied on.

  • skipna (bool, default True) – Exclude NA/null values when computing the result.

  • level (int or level name, default None) – If the axis is a MultiIndex (hierarchical), count along a particular level, collapsing into a Series.

  • numeric_only (bool, default None) – Include only float, int, boolean columns. If None, will attempt to use everything, then use only numeric data. Not implemented for Series.

  • **kwargs – Additional keyword arguments to be passed to the function.

Returns

Return type

Series or DataFrame (if level specified)

kurtosis(axis=None, skipna=None, level=None, numeric_only=None, **kwargs)

Return unbiased kurtosis over requested axis.

Kurtosis obtained using Fisher’s definition of kurtosis (kurtosis of normal == 0.0). Normalized by N-1.

Parameters
  • axis ({index (0), columns (1)}) – Axis for the function to be applied on.

  • skipna (bool, default True) – Exclude NA/null values when computing the result.

  • level (int or level name, default None) – If the axis is a MultiIndex (hierarchical), count along a particular level, collapsing into a Series.

  • numeric_only (bool, default None) – Include only float, int, boolean columns. If None, will attempt to use everything, then use only numeric data. Not implemented for Series.

  • **kwargs – Additional keyword arguments to be passed to the function.

Returns

Return type

Series or DataFrame (if level specified)

last(offset) → FrameOrSeries

Select final periods of time series data based on a date offset.

When having a DataFrame with dates as index, this function can select the last few rows based on a date offset.

Parameters

offset (str, DateOffset, dateutil.relativedelta) – The offset length of the data that will be selected. For instance, ‘3D’ will display all the rows having their index within the last 3 days.

Returns

A subset of the caller.

Return type

Series or DataFrame

Raises

TypeError – If the index is not a DatetimeIndex

See also

first()

Select initial periods of time series based on a date offset.

at_time()

Select values at a particular time of the day.

between_time()

Select values between particular times of the day.

Examples

>>> i = pd.date_range('2018-04-09', periods=4, freq='2D')
>>> ts = pd.DataFrame({'A': [1, 2, 3, 4]}, index=i)
>>> ts
            A
2018-04-09  1
2018-04-11  2
2018-04-13  3
2018-04-15  4

Get the rows for the last 3 days:

>>> ts.last('3D')
            A
2018-04-13  3
2018-04-15  4

Notice the data for 3 last calendar days were returned, not the last 3 observed days in the dataset, and therefore data for 2018-04-11 was not returned.

last_valid_index()

Return index for last non-NA/null value.

Returns

scalar

Return type

type of index

Notes

If all elements are non-NA/null, returns None. Also returns None for empty Series/DataFrame.

le(other, axis='columns', level=None)

Get Less than or equal to of dataframe and other, element-wise (binary operator le).

Among flexible wrappers (eq, ne, le, lt, ge, gt) to comparison operators.

Equivalent to ==, =!, <=, <, >=, > with support to choose axis (rows or columns) and level for comparison.

Parameters
  • other (scalar, sequence, Series, or DataFrame) – Any single or multiple element data structure, or list-like object.

  • axis ({0 or 'index', 1 or 'columns'}, default 'columns') – Whether to compare by the index (0 or ‘index’) or columns (1 or ‘columns’).

  • level (int or label) – Broadcast across a level, matching Index values on the passed MultiIndex level.

Returns

Result of the comparison.

Return type

DataFrame of bool

See also

DataFrame.eq()

Compare DataFrames for equality elementwise.

DataFrame.ne()

Compare DataFrames for inequality elementwise.

DataFrame.le()

Compare DataFrames for less than inequality or equality elementwise.

DataFrame.lt()

Compare DataFrames for strictly less than inequality elementwise.

DataFrame.ge()

Compare DataFrames for greater than inequality or equality elementwise.

DataFrame.gt()

Compare DataFrames for strictly greater than inequality elementwise.

Notes

Mismatched indices will be unioned together. NaN values are considered different (i.e. NaN != NaN).

Examples

>>> df = pd.DataFrame({'cost': [250, 150, 100],
...                    'revenue': [100, 250, 300]},
...                   index=['A', 'B', 'C'])
>>> df
   cost  revenue
A   250      100
B   150      250
C   100      300

Comparison with a scalar, using either the operator or method:

>>> df == 100
    cost  revenue
A  False     True
B  False    False
C   True    False
>>> df.eq(100)
    cost  revenue
A  False     True
B  False    False
C   True    False

When other is a Series, the columns of a DataFrame are aligned with the index of other and broadcast:

>>> df != pd.Series([100, 250], index=["cost", "revenue"])
    cost  revenue
A   True     True
B   True    False
C  False     True

Use the method to control the broadcast axis:

>>> df.ne(pd.Series([100, 300], index=["A", "D"]), axis='index')
   cost  revenue
A  True    False
B  True     True
C  True     True
D  True     True

When comparing to an arbitrary sequence, the number of columns must match the number elements in other:

>>> df == [250, 100]
    cost  revenue
A   True     True
B  False    False
C  False    False

Use the method to control the axis:

>>> df.eq([250, 250, 100], axis='index')
    cost  revenue
A   True    False
B  False     True
C   True    False

Compare to a DataFrame of different shape.

>>> other = pd.DataFrame({'revenue': [300, 250, 100, 150]},
...                      index=['A', 'B', 'C', 'D'])
>>> other
   revenue
A      300
B      250
C      100
D      150
>>> df.gt(other)
    cost  revenue
A  False    False
B  False    False
C  False     True
D  False    False

Compare to a MultiIndex by level.

>>> df_multindex = pd.DataFrame({'cost': [250, 150, 100, 150, 300, 220],
...                              'revenue': [100, 250, 300, 200, 175, 225]},
...                             index=[['Q1', 'Q1', 'Q1', 'Q2', 'Q2', 'Q2'],
...                                    ['A', 'B', 'C', 'A', 'B', 'C']])
>>> df_multindex
      cost  revenue
Q1 A   250      100
   B   150      250
   C   100      300
Q2 A   150      200
   B   300      175
   C   220      225
>>> df.le(df_multindex, level=1)
       cost  revenue
Q1 A   True     True
   B   True     True
   C   True     True
Q2 A  False     True
   B   True    False
   C   True    False
property loc

Access a group of rows and columns by label(s) or a boolean array.

.loc[] is primarily label based, but may also be used with a boolean array.

Allowed inputs are:

  • A single label, e.g. 5 or 'a', (note that 5 is interpreted as a label of the index, and never as an integer position along the index).

  • A list or array of labels, e.g. ['a', 'b', 'c'].

  • A slice object with labels, e.g. 'a':'f'.

    Warning

    Note that contrary to usual python slices, both the start and the stop are included

  • A boolean array of the same length as the axis being sliced, e.g. [True, False, True].

  • A callable function with one argument (the calling Series or DataFrame) and that returns valid output for indexing (one of the above)

See more at Selection by Label

Raises

KeyError – If any items are not found.

See also

DataFrame.at

Access a single value for a row/column label pair.

DataFrame.iloc

Access group of rows and columns by integer position(s).

DataFrame.xs

Returns a cross-section (row(s) or column(s)) from the Series/DataFrame.

Series.loc

Access group of values using labels.

Examples

Getting values

>>> df = pd.DataFrame([[1, 2], [4, 5], [7, 8]],
...      index=['cobra', 'viper', 'sidewinder'],
...      columns=['max_speed', 'shield'])
>>> df
            max_speed  shield
cobra               1       2
viper               4       5
sidewinder          7       8

Single label. Note this returns the row as a Series.

>>> df.loc['viper']
max_speed    4
shield       5
Name: viper, dtype: int64

List of labels. Note using [[]] returns a DataFrame.

>>> df.loc[['viper', 'sidewinder']]
            max_speed  shield
viper               4       5
sidewinder          7       8

Single label for row and column

>>> df.loc['cobra', 'shield']
2

Slice with labels for row and single label for column. As mentioned above, note that both the start and stop of the slice are included.

>>> df.loc['cobra':'viper', 'max_speed']
cobra    1
viper    4
Name: max_speed, dtype: int64

Boolean list with the same length as the row axis

>>> df.loc[[False, False, True]]
            max_speed  shield
sidewinder          7       8

Conditional that returns a boolean Series

>>> df.loc[df['shield'] > 6]
            max_speed  shield
sidewinder          7       8

Conditional that returns a boolean Series with column labels specified

>>> df.loc[df['shield'] > 6, ['max_speed']]
            max_speed
sidewinder          7

Callable that returns a boolean Series

>>> df.loc[lambda df: df['shield'] == 8]
            max_speed  shield
sidewinder          7       8

Setting values

Set value for all items matching the list of labels

>>> df.loc[['viper', 'sidewinder'], ['shield']] = 50
>>> df
            max_speed  shield
cobra               1       2
viper               4      50
sidewinder          7      50

Set value for an entire row

>>> df.loc['cobra'] = 10
>>> df
            max_speed  shield
cobra              10      10
viper               4      50
sidewinder          7      50

Set value for an entire column

>>> df.loc[:, 'max_speed'] = 30
>>> df
            max_speed  shield
cobra              30      10
viper              30      50
sidewinder         30      50

Set value for rows matching callable condition

>>> df.loc[df['shield'] > 35] = 0
>>> df
            max_speed  shield
cobra              30      10
viper               0       0
sidewinder          0       0

Getting values on a DataFrame with an index that has integer labels

Another example using integers for the index

>>> df = pd.DataFrame([[1, 2], [4, 5], [7, 8]],
...      index=[7, 8, 9], columns=['max_speed', 'shield'])
>>> df
   max_speed  shield
7          1       2
8          4       5
9          7       8

Slice with integer labels for rows. As mentioned above, note that both the start and stop of the slice are included.

>>> df.loc[7:9]
   max_speed  shield
7          1       2
8          4       5
9          7       8

Getting values with a MultiIndex

A number of examples using a DataFrame with a MultiIndex

>>> tuples = [
...    ('cobra', 'mark i'), ('cobra', 'mark ii'),
...    ('sidewinder', 'mark i'), ('sidewinder', 'mark ii'),
...    ('viper', 'mark ii'), ('viper', 'mark iii')
... ]
>>> index = pd.MultiIndex.from_tuples(tuples)
>>> values = [[12, 2], [0, 4], [10, 20],
...         [1, 4], [7, 1], [16, 36]]
>>> df = pd.DataFrame(values, columns=['max_speed', 'shield'], index=index)
>>> df
                     max_speed  shield
cobra      mark i           12       2
           mark ii           0       4
sidewinder mark i           10      20
           mark ii           1       4
viper      mark ii           7       1
           mark iii         16      36

Single label. Note this returns a DataFrame with a single index.

>>> df.loc['cobra']
         max_speed  shield
mark i          12       2
mark ii          0       4

Single index tuple. Note this returns a Series.

>>> df.loc[('cobra', 'mark ii')]
max_speed    0
shield       4
Name: (cobra, mark ii), dtype: int64

Single label for row and column. Similar to passing in a tuple, this returns a Series.

>>> df.loc['cobra', 'mark i']
max_speed    12
shield        2
Name: (cobra, mark i), dtype: int64

Single tuple. Note using [[]] returns a DataFrame.

>>> df.loc[[('cobra', 'mark ii')]]
               max_speed  shield
cobra mark ii          0       4

Single tuple for the index with a single label for the column

>>> df.loc[('cobra', 'mark i'), 'shield']
2

Slice from index tuple to single label

>>> df.loc[('cobra', 'mark i'):'viper']
                     max_speed  shield
cobra      mark i           12       2
           mark ii           0       4
sidewinder mark i           10      20
           mark ii           1       4
viper      mark ii           7       1
           mark iii         16      36

Slice from index tuple to index tuple

>>> df.loc[('cobra', 'mark i'):('viper', 'mark ii')]
                    max_speed  shield
cobra      mark i          12       2
           mark ii          0       4
sidewinder mark i          10      20
           mark ii          1       4
viper      mark ii          7       1
lookup(row_labels, col_labels) → numpy.ndarray

Label-based “fancy indexing” function for DataFrame.

Given equal-length arrays of row and column labels, return an array of the values corresponding to each (row, col) pair.

Parameters
  • row_labels (sequence) – The row labels to use for lookup.

  • col_labels (sequence) – The column labels to use for lookup.

Returns

The found values.

Return type

numpy.ndarray

lt(other, axis='columns', level=None)

Get Less than of dataframe and other, element-wise (binary operator lt).

Among flexible wrappers (eq, ne, le, lt, ge, gt) to comparison operators.

Equivalent to ==, =!, <=, <, >=, > with support to choose axis (rows or columns) and level for comparison.

Parameters
  • other (scalar, sequence, Series, or DataFrame) – Any single or multiple element data structure, or list-like object.

  • axis ({0 or 'index', 1 or 'columns'}, default 'columns') – Whether to compare by the index (0 or ‘index’) or columns (1 or ‘columns’).

  • level (int or label) – Broadcast across a level, matching Index values on the passed MultiIndex level.

Returns

Result of the comparison.

Return type

DataFrame of bool

See also

DataFrame.eq()

Compare DataFrames for equality elementwise.

DataFrame.ne()

Compare DataFrames for inequality elementwise.

DataFrame.le()

Compare DataFrames for less than inequality or equality elementwise.

DataFrame.lt()

Compare DataFrames for strictly less than inequality elementwise.

DataFrame.ge()

Compare DataFrames for greater than inequality or equality elementwise.

DataFrame.gt()

Compare DataFrames for strictly greater than inequality elementwise.

Notes

Mismatched indices will be unioned together. NaN values are considered different (i.e. NaN != NaN).

Examples

>>> df = pd.DataFrame({'cost': [250, 150, 100],
...                    'revenue': [100, 250, 300]},
...                   index=['A', 'B', 'C'])
>>> df
   cost  revenue
A   250      100
B   150      250
C   100      300

Comparison with a scalar, using either the operator or method:

>>> df == 100
    cost  revenue
A  False     True
B  False    False
C   True    False
>>> df.eq(100)
    cost  revenue
A  False     True
B  False    False
C   True    False

When other is a Series, the columns of a DataFrame are aligned with the index of other and broadcast:

>>> df != pd.Series([100, 250], index=["cost", "revenue"])
    cost  revenue
A   True     True
B   True    False
C  False     True

Use the method to control the broadcast axis:

>>> df.ne(pd.Series([100, 300], index=["A", "D"]), axis='index')
   cost  revenue
A  True    False
B  True     True
C  True     True
D  True     True

When comparing to an arbitrary sequence, the number of columns must match the number elements in other:

>>> df == [250, 100]
    cost  revenue
A   True     True
B  False    False
C  False    False

Use the method to control the axis:

>>> df.eq([250, 250, 100], axis='index')
    cost  revenue
A   True    False
B  False     True
C   True    False

Compare to a DataFrame of different shape.

>>> other = pd.DataFrame({'revenue': [300, 250, 100, 150]},
...                      index=['A', 'B', 'C', 'D'])
>>> other
   revenue
A      300
B      250
C      100
D      150
>>> df.gt(other)
    cost  revenue
A  False    False
B  False    False
C  False     True
D  False    False

Compare to a MultiIndex by level.

>>> df_multindex = pd.DataFrame({'cost': [250, 150, 100, 150, 300, 220],
...                              'revenue': [100, 250, 300, 200, 175, 225]},
...                             index=[['Q1', 'Q1', 'Q1', 'Q2', 'Q2', 'Q2'],
...                                    ['A', 'B', 'C', 'A', 'B', 'C']])
>>> df_multindex
      cost  revenue
Q1 A   250      100
   B   150      250
   C   100      300
Q2 A   150      200
   B   300      175
   C   220      225
>>> df.le(df_multindex, level=1)
       cost  revenue
Q1 A   True     True
   B   True     True
   C   True     True
Q2 A  False     True
   B   True    False
   C   True    False
mad(axis=None, skipna=None, level=None)

Return the mean absolute deviation of the values for the requested axis.

Parameters
  • axis ({index (0), columns (1)}) – Axis for the function to be applied on.

  • skipna (bool, default None) – Exclude NA/null values when computing the result.

  • level (int or level name, default None) – If the axis is a MultiIndex (hierarchical), count along a particular level, collapsing into a Series.

Returns

Return type

Series or DataFrame (if level specified)

mask(cond, other=nan, inplace=False, axis=None, level=None, errors='raise', try_cast=False)

Replace values where the condition is True.

Parameters
  • cond (bool Series/DataFrame, array-like, or callable) – Where cond is False, keep the original value. Where True, replace with corresponding value from other. If cond is callable, it is computed on the Series/DataFrame and should return boolean Series/DataFrame or array. The callable must not change input Series/DataFrame (though pandas doesn’t check it).

  • other (scalar, Series/DataFrame, or callable) – Entries where cond is True are replaced with corresponding value from other. If other is callable, it is computed on the Series/DataFrame and should return scalar or Series/DataFrame. The callable must not change input Series/DataFrame (though pandas doesn’t check it).

  • inplace (bool, default False) – Whether to perform the operation in place on the data.

  • axis (int, default None) – Alignment axis if needed.

  • level (int, default None) – Alignment level if needed.

  • errors (str, {'raise', 'ignore'}, default 'raise') –

    Note that currently this parameter won’t affect the results and will always coerce to a suitable dtype.

    • ’raise’ : allow exceptions to be raised.

    • ’ignore’ : suppress exceptions. On error return original object.

  • try_cast (bool, default False) – Try to cast the result back to the input type (if possible).

Returns

Return type

Same type as caller

See also

DataFrame.where()

Return an object of same shape as self.

Notes

The mask method is an application of the if-then idiom. For each element in the calling DataFrame, if cond is False the element is used; otherwise the corresponding element from the DataFrame other is used.

The signature for DataFrame.where() differs from numpy.where(). Roughly df1.where(m, df2) is equivalent to np.where(m, df1, df2).

For further details and examples see the mask documentation in indexing.

Examples

>>> s = pd.Series(range(5))
>>> s.where(s > 0)
0    NaN
1    1.0
2    2.0
3    3.0
4    4.0
dtype: float64
>>> s.mask(s > 0)
0    0.0
1    NaN
2    NaN
3    NaN
4    NaN
dtype: float64
>>> s.where(s > 1, 10)
0    10
1    10
2    2
3    3
4    4
dtype: int64
>>> df = pd.DataFrame(np.arange(10).reshape(-1, 2), columns=['A', 'B'])
>>> df
   A  B
0  0  1
1  2  3
2  4  5
3  6  7
4  8  9
>>> m = df % 3 == 0
>>> df.where(m, -df)
   A  B
0  0 -1
1 -2  3
2 -4 -5
3  6 -7
4 -8  9
>>> df.where(m, -df) == np.where(m, df, -df)
      A     B
0  True  True
1  True  True
2  True  True
3  True  True
4  True  True
>>> df.where(m, -df) == df.mask(~m, -df)
      A     B
0  True  True
1  True  True
2  True  True
3  True  True
4  True  True
max(axis=None, skipna=None, level=None, numeric_only=None, **kwargs)

Return the maximum of the values for the requested axis.

If you want the index of the maximum, use idxmax. This isthe equivalent of the numpy.ndarray method argmax.

Parameters
  • axis ({index (0), columns (1)}) – Axis for the function to be applied on.

  • skipna (bool, default True) – Exclude NA/null values when computing the result.

  • level (int or level name, default None) – If the axis is a MultiIndex (hierarchical), count along a particular level, collapsing into a Series.

  • numeric_only (bool, default None) – Include only float, int, boolean columns. If None, will attempt to use everything, then use only numeric data. Not implemented for Series.

  • **kwargs – Additional keyword arguments to be passed to the function.

Returns

Return type

Series or DataFrame (if level specified)

See also

Series.sum()

Return the sum.

Series.min()

Return the minimum.

Series.max()

Return the maximum.

Series.idxmin()

Return the index of the minimum.

Series.idxmax()

Return the index of the maximum.

DataFrame.sum()

Return the sum over the requested axis.

DataFrame.min()

Return the minimum over the requested axis.

DataFrame.max()

Return the maximum over the requested axis.

DataFrame.idxmin()

Return the index of the minimum over the requested axis.

DataFrame.idxmax()

Return the index of the maximum over the requested axis.

Examples

>>> idx = pd.MultiIndex.from_arrays([
...     ['warm', 'warm', 'cold', 'cold'],
...     ['dog', 'falcon', 'fish', 'spider']],
...     names=['blooded', 'animal'])
>>> s = pd.Series([4, 2, 0, 8], name='legs', index=idx)
>>> s
blooded  animal
warm     dog       4
         falcon    2
cold     fish      0
         spider    8
Name: legs, dtype: int64
>>> s.max()
8

Max using level names, as well as indices.

>>> s.max(level='blooded')
blooded
warm    4
cold    8
Name: legs, dtype: int64
>>> s.max(level=0)
blooded
warm    4
cold    8
Name: legs, dtype: int64
mean(axis=None, skipna=None, level=None, numeric_only=None, **kwargs)

Return the mean of the values for the requested axis.

Parameters
  • axis ({index (0), columns (1)}) – Axis for the function to be applied on.

  • skipna (bool, default True) – Exclude NA/null values when computing the result.

  • level (int or level name, default None) – If the axis is a MultiIndex (hierarchical), count along a particular level, collapsing into a Series.

  • numeric_only (bool, default None) – Include only float, int, boolean columns. If None, will attempt to use everything, then use only numeric data. Not implemented for Series.

  • **kwargs – Additional keyword arguments to be passed to the function.

Returns

Return type

Series or DataFrame (if level specified)

median(axis=None, skipna=None, level=None, numeric_only=None, **kwargs)

Return the median of the values for the requested axis.

Parameters
  • axis ({index (0), columns (1)}) – Axis for the function to be applied on.

  • skipna (bool, default True) – Exclude NA/null values when computing the result.

  • level (int or level name, default None) – If the axis is a MultiIndex (hierarchical), count along a particular level, collapsing into a Series.

  • numeric_only (bool, default None) – Include only float, int, boolean columns. If None, will attempt to use everything, then use only numeric data. Not implemented for Series.

  • **kwargs – Additional keyword arguments to be passed to the function.

Returns

Return type

Series or DataFrame (if level specified)

melt(id_vars=None, value_vars=None, var_name=None, value_name='value', col_level=None, ignore_index=True) → pandas.core.frame.DataFrame

Unpivot a DataFrame from wide to long format, optionally leaving identifiers set.

This function is useful to massage a DataFrame into a format where one or more columns are identifier variables (id_vars), while all other columns, considered measured variables (value_vars), are “unpivoted” to the row axis, leaving just two non-identifier columns, ‘variable’ and ‘value’.

New in version 0.20.0.

Parameters
  • id_vars (tuple, list, or ndarray, optional) – Column(s) to use as identifier variables.

  • value_vars (tuple, list, or ndarray, optional) – Column(s) to unpivot. If not specified, uses all columns that are not set as id_vars.

  • var_name (scalar) – Name to use for the ‘variable’ column. If None it uses frame.columns.name or ‘variable’.

  • value_name (scalar, default 'value') – Name to use for the ‘value’ column.

  • col_level (int or str, optional) – If columns are a MultiIndex then use this level to melt.

  • ignore_index (bool, default True) –

    If True, original index is ignored. If False, the original index is retained. Index labels will be repeated as necessary.

    New in version 1.1.0.

Returns

Unpivoted DataFrame.

Return type

DataFrame

See also

melt()

Identical method.

pivot_table()

Create a spreadsheet-style pivot table as a DataFrame.

DataFrame.pivot()

Return reshaped DataFrame organized by given index / column values.

DataFrame.explode()

Explode a DataFrame from list-like columns to long format.

Examples

>>> df = pd.DataFrame({'A': {0: 'a', 1: 'b', 2: 'c'},
...                    'B': {0: 1, 1: 3, 2: 5},
...                    'C': {0: 2, 1: 4, 2: 6}})
>>> df
   A  B  C
0  a  1  2
1  b  3  4
2  c  5  6
>>> df.melt(id_vars=['A'], value_vars=['B'])
   A variable  value
0  a        B      1
1  b        B      3
2  c        B      5
>>> df.melt(id_vars=['A'], value_vars=['B', 'C'])
   A variable  value
0  a        B      1
1  b        B      3
2  c        B      5
3  a        C      2
4  b        C      4
5  c        C      6

The names of ‘variable’ and ‘value’ columns can be customized:

>>> df.melt(id_vars=['A'], value_vars=['B'],
...         var_name='myVarname', value_name='myValname')
   A myVarname  myValname
0  a         B          1
1  b         B          3
2  c         B          5

Original index values can be kept around:

>>> df.melt(id_vars=['A'], value_vars=['B', 'C'], ignore_index=False)
   A variable  value
0  a        B      1
1  b        B      3
2  c        B      5
0  a        C      2
1  b        C      4
2  c        C      6

If you have multi-index columns:

>>> df.columns = [list('ABC'), list('DEF')]
>>> df
   A  B  C
   D  E  F
0  a  1  2
1  b  3  4
2  c  5  6
>>> df.melt(col_level=0, id_vars=['A'], value_vars=['B'])
   A variable  value
0  a        B      1
1  b        B      3
2  c        B      5
>>> df.melt(id_vars=[('A', 'D')], value_vars=[('B', 'E')])
  (A, D) variable_0 variable_1  value
0      a          B          E      1
1      b          B          E      3
2      c          B          E      5
memory_usage(index=True, deep=False) → pandas.core.series.Series

Return the memory usage of each column in bytes.

The memory usage can optionally include the contribution of the index and elements of object dtype.

This value is displayed in DataFrame.info by default. This can be suppressed by setting pandas.options.display.memory_usage to False.

Parameters
  • index (bool, default True) – Specifies whether to include the memory usage of the DataFrame’s index in returned Series. If index=True, the memory usage of the index is the first item in the output.

  • deep (bool, default False) – If True, introspect the data deeply by interrogating object dtypes for system-level memory consumption, and include it in the returned values.

Returns

A Series whose index is the original column names and whose values is the memory usage of each column in bytes.

Return type

Series

See also

numpy.ndarray.nbytes()

Total bytes consumed by the elements of an ndarray.

Series.memory_usage()

Bytes consumed by a Series.

Categorical()

Memory-efficient array for string values with many repeated values.

DataFrame.info()

Concise summary of a DataFrame.

Examples

>>> dtypes = ['int64', 'float64', 'complex128', 'object', 'bool']
>>> data = dict([(t, np.ones(shape=5000).astype(t))
...              for t in dtypes])
>>> df = pd.DataFrame(data)
>>> df.head()
   int64  float64            complex128  object  bool
0      1      1.0    1.000000+0.000000j       1  True
1      1      1.0    1.000000+0.000000j       1  True
2      1      1.0    1.000000+0.000000j       1  True
3      1      1.0    1.000000+0.000000j       1  True
4      1      1.0    1.000000+0.000000j       1  True
>>> df.memory_usage()
Index           128
int64         40000
float64       40000
complex128    80000
object        40000
bool           5000
dtype: int64
>>> df.memory_usage(index=False)
int64         40000
float64       40000
complex128    80000
object        40000
bool           5000
dtype: int64

The memory footprint of object dtype columns is ignored by default:

>>> df.memory_usage(deep=True)
Index            128
int64          40000
float64        40000
complex128     80000
object        160000
bool            5000
dtype: int64

Use a Categorical for efficient storage of an object-dtype column with many repeated values.

>>> df['object'].astype('category').memory_usage(deep=True)
5216
merge(right, how='inner', on=None, left_on=None, right_on=None, left_index=False, right_index=False, sort=False, suffixes=('_x', '_y'), copy=True, indicator=False, validate=None) → pandas.core.frame.DataFrame

Merge DataFrame or named Series objects with a database-style join.

The join is done on columns or indexes. If joining columns on columns, the DataFrame indexes will be ignored. Otherwise if joining indexes on indexes or indexes on a column or columns, the index will be passed on.

Parameters
  • right (DataFrame or named Series) – Object to merge with.

  • how ({'left', 'right', 'outer', 'inner'}, default 'inner') –

    Type of merge to be performed.

    • left: use only keys from left frame, similar to a SQL left outer join; preserve key order.

    • right: use only keys from right frame, similar to a SQL right outer join; preserve key order.

    • outer: use union of keys from both frames, similar to a SQL full outer join; sort keys lexicographically.

    • inner: use intersection of keys from both frames, similar to a SQL inner join; preserve the order of the left keys.

  • on (label or list) – Column or index level names to join on. These must be found in both DataFrames. If on is None and not merging on indexes then this defaults to the intersection of the columns in both DataFrames.

  • left_on (label or list, or array-like) – Column or index level names to join on in the left DataFrame. Can also be an array or list of arrays of the length of the left DataFrame. These arrays are treated as if they are columns.

  • right_on (label or list, or array-like) – Column or index level names to join on in the right DataFrame. Can also be an array or list of arrays of the length of the right DataFrame. These arrays are treated as if they are columns.

  • left_index (bool, default False) – Use the index from the left DataFrame as the join key(s). If it is a MultiIndex, the number of keys in the other DataFrame (either the index or a number of columns) must match the number of levels.

  • right_index (bool, default False) – Use the index from the right DataFrame as the join key. Same caveats as left_index.

  • sort (bool, default False) – Sort the join keys lexicographically in the result DataFrame. If False, the order of the join keys depends on the join type (how keyword).

  • suffixes (list-like, default is ("_x", "_y")) – A length-2 sequence where each element is optionally a string indicating the suffix to add to overlapping column names in left and right respectively. Pass a value of None instead of a string to indicate that the column name from left or right should be left as-is, with no suffix. At least one of the values must not be None.

  • copy (bool, default True) – If False, avoid copy if possible.

  • indicator (bool or str, default False) – If True, adds a column to the output DataFrame called “_merge” with information on the source of each row. The column can be given a different name by providing a string argument. The column will have a Categorical type with the value of “left_only” for observations whose merge key only appears in the left DataFrame, “right_only” for observations whose merge key only appears in the right DataFrame, and “both” if the observation’s merge key is found in both DataFrames.

  • validate (str, optional) –

    If specified, checks if merge is of specified type.

    • ”one_to_one” or “1:1”: check if merge keys are unique in both left and right datasets.

    • ”one_to_many” or “1:m”: check if merge keys are unique in left dataset.

    • ”many_to_one” or “m:1”: check if merge keys are unique in right dataset.

    • ”many_to_many” or “m:m”: allowed, but does not result in checks.

Returns

A DataFrame of the two merged objects.

Return type

DataFrame

See also

merge_ordered()

Merge with optional filling/interpolation.

merge_asof()

Merge on nearest keys.

DataFrame.join()

Similar method using indices.

Notes

Support for specifying index levels as the on, left_on, and right_on parameters was added in version 0.23.0 Support for merging named Series objects was added in version 0.24.0

Examples

>>> df1 = pd.DataFrame({'lkey': ['foo', 'bar', 'baz', 'foo'],
...                     'value': [1, 2, 3, 5]})
>>> df2 = pd.DataFrame({'rkey': ['foo', 'bar', 'baz', 'foo'],
...                     'value': [5, 6, 7, 8]})
>>> df1
    lkey value
0   foo      1
1   bar      2
2   baz      3
3   foo      5
>>> df2
    rkey value
0   foo      5
1   bar      6
2   baz      7
3   foo      8

Merge df1 and df2 on the lkey and rkey columns. The value columns have the default suffixes, _x and _y, appended.

>>> df1.merge(df2, left_on='lkey', right_on='rkey')
  lkey  value_x rkey  value_y
0  foo        1  foo        5
1  foo        1  foo        8
2  foo        5  foo        5
3  foo        5  foo        8
4  bar        2  bar        6
5  baz        3  baz        7

Merge DataFrames df1 and df2 with specified left and right suffixes appended to any overlapping columns.

>>> df1.merge(df2, left_on='lkey', right_on='rkey',
...           suffixes=('_left', '_right'))
  lkey  value_left rkey  value_right
0  foo           1  foo            5
1  foo           1  foo            8
2  foo           5  foo            5
3  foo           5  foo            8
4  bar           2  bar            6
5  baz           3  baz            7

Merge DataFrames df1 and df2, but raise an exception if the DataFrames have any overlapping columns.

>>> df1.merge(df2, left_on='lkey', right_on='rkey', suffixes=(False, False))
Traceback (most recent call last):
...
ValueError: columns overlap but no suffix specified:
    Index(['value'], dtype='object')
min(axis=None, skipna=None, level=None, numeric_only=None, **kwargs)

Return the minimum of the values for the requested axis.

If you want the index of the minimum, use idxmin. This isthe equivalent of the numpy.ndarray method argmin.

Parameters
  • axis ({index (0), columns (1)}) – Axis for the function to be applied on.

  • skipna (bool, default True) – Exclude NA/null values when computing the result.

  • level (int or level name, default None) – If the axis is a MultiIndex (hierarchical), count along a particular level, collapsing into a Series.

  • numeric_only (bool, default None) – Include only float, int, boolean columns. If None, will attempt to use everything, then use only numeric data. Not implemented for Series.

  • **kwargs – Additional keyword arguments to be passed to the function.

Returns

Return type

Series or DataFrame (if level specified)

See also

Series.sum()

Return the sum.

Series.min()

Return the minimum.

Series.max()

Return the maximum.

Series.idxmin()

Return the index of the minimum.

Series.idxmax()

Return the index of the maximum.

DataFrame.sum()

Return the sum over the requested axis.

DataFrame.min()

Return the minimum over the requested axis.

DataFrame.max()

Return the maximum over the requested axis.

DataFrame.idxmin()

Return the index of the minimum over the requested axis.

DataFrame.idxmax()

Return the index of the maximum over the requested axis.

Examples

>>> idx = pd.MultiIndex.from_arrays([
...     ['warm', 'warm', 'cold', 'cold'],
...     ['dog', 'falcon', 'fish', 'spider']],
...     names=['blooded', 'animal'])
>>> s = pd.Series([4, 2, 0, 8], name='legs', index=idx)
>>> s
blooded  animal
warm     dog       4
         falcon    2
cold     fish      0
         spider    8
Name: legs, dtype: int64
>>> s.min()
0

Min using level names, as well as indices.

>>> s.min(level='blooded')
blooded
warm    2
cold    0
Name: legs, dtype: int64
>>> s.min(level=0)
blooded
warm    2
cold    0
Name: legs, dtype: int64
mod(other, axis='columns', level=None, fill_value=None)

Get Modulo of dataframe and other, element-wise (binary operator mod).

Equivalent to dataframe % other, but with support to substitute a fill_value for missing data in one of the inputs. With reverse version, rmod.

Among flexible wrappers (add, sub, mul, div, mod, pow) to arithmetic operators: +, -, *, /, //, %, **.

Parameters
  • other (scalar, sequence, Series, or DataFrame) – Any single or multiple element data structure, or list-like object.

  • axis ({0 or 'index', 1 or 'columns'}) – Whether to compare by the index (0 or ‘index’) or columns (1 or ‘columns’). For Series input, axis to match Series index on.

  • level (int or label) – Broadcast across a level, matching Index values on the passed MultiIndex level.

  • fill_value (float or None, default None) – Fill existing missing (NaN) values, and any new element needed for successful DataFrame alignment, with this value before computation. If data in both corresponding DataFrame locations is missing the result will be missing.

Returns

Result of the arithmetic operation.

Return type

DataFrame

See also

DataFrame.add()

Add DataFrames.

DataFrame.sub()

Subtract DataFrames.

DataFrame.mul()

Multiply DataFrames.

DataFrame.div()

Divide DataFrames (float division).

DataFrame.truediv()

Divide DataFrames (float division).

DataFrame.floordiv()

Divide DataFrames (integer division).

DataFrame.mod()

Calculate modulo (remainder after division).

DataFrame.pow()

Calculate exponential power.

Notes

Mismatched indices will be unioned together.

Examples

>>> df = pd.DataFrame({'angles': [0, 3, 4],
...                    'degrees': [360, 180, 360]},
...                   index=['circle', 'triangle', 'rectangle'])
>>> df
           angles  degrees
circle          0      360
triangle        3      180
rectangle       4      360

Add a scalar with operator version which return the same results.

>>> df + 1
           angles  degrees
circle          1      361
triangle        4      181
rectangle       5      361
>>> df.add(1)
           angles  degrees
circle          1      361
triangle        4      181
rectangle       5      361

Divide by constant with reverse version.

>>> df.div(10)
           angles  degrees
circle        0.0     36.0
triangle      0.3     18.0
rectangle     0.4     36.0
>>> df.rdiv(10)
             angles   degrees
circle          inf  0.027778
triangle   3.333333  0.055556
rectangle  2.500000  0.027778

Subtract a list and Series by axis with operator version.

>>> df - [1, 2]
           angles  degrees
circle         -1      358
triangle        2      178
rectangle       3      358
>>> df.sub([1, 2], axis='columns')
           angles  degrees
circle         -1      358
triangle        2      178
rectangle       3      358
>>> df.sub(pd.Series([1, 1, 1], index=['circle', 'triangle', 'rectangle']),
...        axis='index')
           angles  degrees
circle         -1      359
triangle        2      179
rectangle       3      359

Multiply a DataFrame of different shape with operator version.

>>> other = pd.DataFrame({'angles': [0, 3, 4]},
...                      index=['circle', 'triangle', 'rectangle'])
>>> other
           angles
circle          0
triangle        3
rectangle       4
>>> df * other
           angles  degrees
circle          0      NaN
triangle        9      NaN
rectangle      16      NaN
>>> df.mul(other, fill_value=0)
           angles  degrees
circle          0      0.0
triangle        9      0.0
rectangle      16      0.0

Divide by a MultiIndex by level.

>>> df_multindex = pd.DataFrame({'angles': [0, 3, 4, 4, 5, 6],
...                              'degrees': [360, 180, 360, 360, 540, 720]},
...                             index=[['A', 'A', 'A', 'B', 'B', 'B'],
...                                    ['circle', 'triangle', 'rectangle',
...                                     'square', 'pentagon', 'hexagon']])
>>> df_multindex
             angles  degrees
A circle          0      360
  triangle        3      180
  rectangle       4      360
B square          4      360
  pentagon        5      540
  hexagon         6      720
>>> df.div(df_multindex, level=1, fill_value=0)
             angles  degrees
A circle        NaN      1.0
  triangle      1.0      1.0
  rectangle     1.0      1.0
B square        0.0      0.0
  pentagon      0.0      0.0
  hexagon       0.0      0.0
mode(axis=0, numeric_only=False, dropna=True) → pandas.core.frame.DataFrame

Get the mode(s) of each element along the selected axis.

The mode of a set of values is the value that appears most often. It can be multiple values.

Parameters
  • axis ({0 or 'index', 1 or 'columns'}, default 0) –

    The axis to iterate over while searching for the mode:

    • 0 or ‘index’ : get mode of each column

    • 1 or ‘columns’ : get mode of each row.

  • numeric_only (bool, default False) – If True, only apply to numeric columns.

  • dropna (bool, default True) –

    Don’t consider counts of NaN/NaT.

    New in version 0.24.0.

Returns

The modes of each column or row.

Return type

DataFrame

See also

Series.mode()

Return the highest frequency value in a Series.

Series.value_counts()

Return the counts of values in a Series.

Examples

>>> df = pd.DataFrame([('bird', 2, 2),
...                    ('mammal', 4, np.nan),
...                    ('arthropod', 8, 0),
...                    ('bird', 2, np.nan)],
...                   index=('falcon', 'horse', 'spider', 'ostrich'),
...                   columns=('species', 'legs', 'wings'))
>>> df
           species  legs  wings
falcon        bird     2    2.0
horse       mammal     4    NaN
spider   arthropod     8    0.0
ostrich       bird     2    NaN

By default, missing values are not considered, and the mode of wings are both 0 and 2. The second row of species and legs contains NaN, because they have only one mode, but the DataFrame has two rows.

>>> df.mode()
  species  legs  wings
0    bird   2.0    0.0
1     NaN   NaN    2.0

Setting dropna=False NaN values are considered and they can be the mode (like for wings).

>>> df.mode(dropna=False)
  species  legs  wings
0    bird     2    NaN

Setting numeric_only=True, only the mode of numeric columns is computed, and columns of other types are ignored.

>>> df.mode(numeric_only=True)
   legs  wings
0   2.0    0.0
1   NaN    2.0

To compute the mode over columns and not rows, use the axis parameter:

>>> df.mode(axis='columns', numeric_only=True)
           0    1
falcon   2.0  NaN
horse    4.0  NaN
spider   0.0  8.0
ostrich  2.0  NaN
mul(other, axis='columns', level=None, fill_value=None)

Get Multiplication of dataframe and other, element-wise (binary operator mul).

Equivalent to dataframe * other, but with support to substitute a fill_value for missing data in one of the inputs. With reverse version, rmul.

Among flexible wrappers (add, sub, mul, div, mod, pow) to arithmetic operators: +, -, *, /, //, %, **.

Parameters
  • other (scalar, sequence, Series, or DataFrame) – Any single or multiple element data structure, or list-like object.

  • axis ({0 or 'index', 1 or 'columns'}) – Whether to compare by the index (0 or ‘index’) or columns (1 or ‘columns’). For Series input, axis to match Series index on.

  • level (int or label) – Broadcast across a level, matching Index values on the passed MultiIndex level.

  • fill_value (float or None, default None) – Fill existing missing (NaN) values, and any new element needed for successful DataFrame alignment, with this value before computation. If data in both corresponding DataFrame locations is missing the result will be missing.

Returns

Result of the arithmetic operation.

Return type

DataFrame

See also

DataFrame.add()

Add DataFrames.

DataFrame.sub()

Subtract DataFrames.

DataFrame.mul()

Multiply DataFrames.

DataFrame.div()

Divide DataFrames (float division).

DataFrame.truediv()

Divide DataFrames (float division).

DataFrame.floordiv()

Divide DataFrames (integer division).

DataFrame.mod()

Calculate modulo (remainder after division).

DataFrame.pow()

Calculate exponential power.

Notes

Mismatched indices will be unioned together.

Examples

>>> df = pd.DataFrame({'angles': [0, 3, 4],
...                    'degrees': [360, 180, 360]},
...                   index=['circle', 'triangle', 'rectangle'])
>>> df
           angles  degrees
circle          0      360
triangle        3      180
rectangle       4      360

Add a scalar with operator version which return the same results.

>>> df + 1
           angles  degrees
circle          1      361
triangle        4      181
rectangle       5      361
>>> df.add(1)
           angles  degrees
circle          1      361
triangle        4      181
rectangle       5      361

Divide by constant with reverse version.

>>> df.div(10)
           angles  degrees
circle        0.0     36.0
triangle      0.3     18.0
rectangle     0.4     36.0
>>> df.rdiv(10)
             angles   degrees
circle          inf  0.027778
triangle   3.333333  0.055556
rectangle  2.500000  0.027778

Subtract a list and Series by axis with operator version.

>>> df - [1, 2]
           angles  degrees
circle         -1      358
triangle        2      178
rectangle       3      358
>>> df.sub([1, 2], axis='columns')
           angles  degrees
circle         -1      358
triangle        2      178
rectangle       3      358
>>> df.sub(pd.Series([1, 1, 1], index=['circle', 'triangle', 'rectangle']),
...        axis='index')
           angles  degrees
circle         -1      359
triangle        2      179
rectangle       3      359

Multiply a DataFrame of different shape with operator version.

>>> other = pd.DataFrame({'angles': [0, 3, 4]},
...                      index=['circle', 'triangle', 'rectangle'])
>>> other
           angles
circle          0
triangle        3
rectangle       4
>>> df * other
           angles  degrees
circle          0      NaN
triangle        9      NaN
rectangle      16      NaN
>>> df.mul(other, fill_value=0)
           angles  degrees
circle          0      0.0
triangle        9      0.0
rectangle      16      0.0

Divide by a MultiIndex by level.

>>> df_multindex = pd.DataFrame({'angles': [0, 3, 4, 4, 5, 6],
...                              'degrees': [360, 180, 360, 360, 540, 720]},
...                             index=[['A', 'A', 'A', 'B', 'B', 'B'],
...                                    ['circle', 'triangle', 'rectangle',
...                                     'square', 'pentagon', 'hexagon']])
>>> df_multindex
             angles  degrees
A circle          0      360
  triangle        3      180
  rectangle       4      360
B square          4      360
  pentagon        5      540
  hexagon         6      720
>>> df.div(df_multindex, level=1, fill_value=0)
             angles  degrees
A circle        NaN      1.0
  triangle      1.0      1.0
  rectangle     1.0      1.0
B square        0.0      0.0
  pentagon      0.0      0.0
  hexagon       0.0      0.0
multiply(other, axis='columns', level=None, fill_value=None)

Get Multiplication of dataframe and other, element-wise (binary operator mul).

Equivalent to dataframe * other, but with support to substitute a fill_value for missing data in one of the inputs. With reverse version, rmul.

Among flexible wrappers (add, sub, mul, div, mod, pow) to arithmetic operators: +, -, *, /, //, %, **.

Parameters
  • other (scalar, sequence, Series, or DataFrame) – Any single or multiple element data structure, or list-like object.

  • axis ({0 or 'index', 1 or 'columns'}) – Whether to compare by the index (0 or ‘index’) or columns (1 or ‘columns’). For Series input, axis to match Series index on.

  • level (int or label) – Broadcast across a level, matching Index values on the passed MultiIndex level.

  • fill_value (float or None, default None) – Fill existing missing (NaN) values, and any new element needed for successful DataFrame alignment, with this value before computation. If data in both corresponding DataFrame locations is missing the result will be missing.

Returns

Result of the arithmetic operation.

Return type

DataFrame

See also

DataFrame.add()

Add DataFrames.

DataFrame.sub()

Subtract DataFrames.

DataFrame.mul()

Multiply DataFrames.

DataFrame.div()

Divide DataFrames (float division).

DataFrame.truediv()

Divide DataFrames (float division).

DataFrame.floordiv()

Divide DataFrames (integer division).

DataFrame.mod()

Calculate modulo (remainder after division).

DataFrame.pow()

Calculate exponential power.

Notes

Mismatched indices will be unioned together.

Examples

>>> df = pd.DataFrame({'angles': [0, 3, 4],
...                    'degrees': [360, 180, 360]},
...                   index=['circle', 'triangle', 'rectangle'])
>>> df
           angles  degrees
circle          0      360
triangle        3      180
rectangle       4      360

Add a scalar with operator version which return the same results.

>>> df + 1
           angles  degrees
circle          1      361
triangle        4      181
rectangle       5      361
>>> df.add(1)
           angles  degrees
circle          1      361
triangle        4      181
rectangle       5      361

Divide by constant with reverse version.

>>> df.div(10)
           angles  degrees
circle        0.0     36.0
triangle      0.3     18.0
rectangle     0.4     36.0
>>> df.rdiv(10)
             angles   degrees
circle          inf  0.027778
triangle   3.333333  0.055556
rectangle  2.500000  0.027778

Subtract a list and Series by axis with operator version.

>>> df - [1, 2]
           angles  degrees
circle         -1      358
triangle        2      178
rectangle       3      358
>>> df.sub([1, 2], axis='columns')
           angles  degrees
circle         -1      358
triangle        2      178
rectangle       3      358
>>> df.sub(pd.Series([1, 1, 1], index=['circle', 'triangle', 'rectangle']),
...        axis='index')
           angles  degrees
circle         -1      359
triangle        2      179
rectangle       3      359

Multiply a DataFrame of different shape with operator version.

>>> other = pd.DataFrame({'angles': [0, 3, 4]},
...                      index=['circle', 'triangle', 'rectangle'])
>>> other
           angles
circle          0
triangle        3
rectangle       4
>>> df * other
           angles  degrees
circle          0      NaN
triangle        9      NaN
rectangle      16      NaN
>>> df.mul(other, fill_value=0)
           angles  degrees
circle          0      0.0
triangle        9      0.0
rectangle      16      0.0

Divide by a MultiIndex by level.

>>> df_multindex = pd.DataFrame({'angles': [0, 3, 4, 4, 5, 6],
...                              'degrees': [360, 180, 360, 360, 540, 720]},
...                             index=[['A', 'A', 'A', 'B', 'B', 'B'],
...                                    ['circle', 'triangle', 'rectangle',
...                                     'square', 'pentagon', 'hexagon']])
>>> df_multindex
             angles  degrees
A circle          0      360
  triangle        3      180
  rectangle       4      360
B square          4      360
  pentagon        5      540
  hexagon         6      720
>>> df.div(df_multindex, level=1, fill_value=0)
             angles  degrees
A circle        NaN      1.0
  triangle      1.0      1.0
  rectangle     1.0      1.0
B square        0.0      0.0
  pentagon      0.0      0.0
  hexagon       0.0      0.0
property ndim

Return an int representing the number of axes / array dimensions.

Return 1 if Series. Otherwise return 2 if DataFrame.

See also

ndarray.ndim

Number of array dimensions.

Examples

>>> s = pd.Series({'a': 1, 'b': 2, 'c': 3})
>>> s.ndim
1
>>> df = pd.DataFrame({'col1': [1, 2], 'col2': [3, 4]})
>>> df.ndim
2
ne(other, axis='columns', level=None)

Get Not equal to of dataframe and other, element-wise (binary operator ne).

Among flexible wrappers (eq, ne, le, lt, ge, gt) to comparison operators.

Equivalent to ==, =!, <=, <, >=, > with support to choose axis (rows or columns) and level for comparison.

Parameters
  • other (scalar, sequence, Series, or DataFrame) – Any single or multiple element data structure, or list-like object.

  • axis ({0 or 'index', 1 or 'columns'}, default 'columns') – Whether to compare by the index (0 or ‘index’) or columns (1 or ‘columns’).

  • level (int or label) – Broadcast across a level, matching Index values on the passed MultiIndex level.

Returns

Result of the comparison.

Return type

DataFrame of bool

See also

DataFrame.eq()

Compare DataFrames for equality elementwise.

DataFrame.ne()

Compare DataFrames for inequality elementwise.

DataFrame.le()

Compare DataFrames for less than inequality or equality elementwise.

DataFrame.lt()

Compare DataFrames for strictly less than inequality elementwise.

DataFrame.ge()

Compare DataFrames for greater than inequality or equality elementwise.

DataFrame.gt()

Compare DataFrames for strictly greater than inequality elementwise.

Notes

Mismatched indices will be unioned together. NaN values are considered different (i.e. NaN != NaN).

Examples

>>> df = pd.DataFrame({'cost': [250, 150, 100],
...                    'revenue': [100, 250, 300]},
...                   index=['A', 'B', 'C'])
>>> df
   cost  revenue
A   250      100
B   150      250
C   100      300

Comparison with a scalar, using either the operator or method:

>>> df == 100
    cost  revenue
A  False     True
B  False    False
C   True    False
>>> df.eq(100)
    cost  revenue
A  False     True
B  False    False
C   True    False

When other is a Series, the columns of a DataFrame are aligned with the index of other and broadcast:

>>> df != pd.Series([100, 250], index=["cost", "revenue"])
    cost  revenue
A   True     True
B   True    False
C  False     True

Use the method to control the broadcast axis:

>>> df.ne(pd.Series([100, 300], index=["A", "D"]), axis='index')
   cost  revenue
A  True    False
B  True     True
C  True     True
D  True     True

When comparing to an arbitrary sequence, the number of columns must match the number elements in other:

>>> df == [250, 100]
    cost  revenue
A   True     True
B  False    False
C  False    False

Use the method to control the axis:

>>> df.eq([250, 250, 100], axis='index')
    cost  revenue
A   True    False
B  False     True
C   True    False

Compare to a DataFrame of different shape.

>>> other = pd.DataFrame({'revenue': [300, 250, 100, 150]},
...                      index=['A', 'B', 'C', 'D'])
>>> other
   revenue
A      300
B      250
C      100
D      150
>>> df.gt(other)
    cost  revenue
A  False    False
B  False    False
C  False     True
D  False    False

Compare to a MultiIndex by level.

>>> df_multindex = pd.DataFrame({'cost': [250, 150, 100, 150, 300, 220],
...                              'revenue': [100, 250, 300, 200, 175, 225]},
...                             index=[['Q1', 'Q1', 'Q1', 'Q2', 'Q2', 'Q2'],
...                                    ['A', 'B', 'C', 'A', 'B', 'C']])
>>> df_multindex
      cost  revenue
Q1 A   250      100
   B   150      250
   C   100      300
Q2 A   150      200
   B   300      175
   C   220      225
>>> df.le(df_multindex, level=1)
       cost  revenue
Q1 A   True     True
   B   True     True
   C   True     True
Q2 A  False     True
   B   True    False
   C   True    False
nlargest(n, columns, keep='first') → pandas.core.frame.DataFrame

Return the first n rows ordered by columns in descending order.

Return the first n rows with the largest values in columns, in descending order. The columns that are not specified are returned as well, but not used for ordering.

This method is equivalent to df.sort_values(columns, ascending=False).head(n), but more performant.

Parameters
  • n (int) – Number of rows to return.

  • columns (label or list of labels) – Column label(s) to order by.

  • keep ({'first', 'last', 'all'}, default 'first') –

    Where there are duplicate values:

    • first : prioritize the first occurrence(s)

    • last : prioritize the last occurrence(s)

    • alldo not drop any duplicates, even it means

      selecting more than n items.

    New in version 0.24.0.

Returns

The first n rows ordered by the given columns in descending order.

Return type

DataFrame

See also

DataFrame.nsmallest()

Return the first n rows ordered by columns in ascending order.

DataFrame.sort_values()

Sort DataFrame by the values.

DataFrame.head()

Return the first n rows without re-ordering.

Notes

This function cannot be used with all column types. For example, when specifying columns with object or category dtypes, TypeError is raised.

Examples

>>> df = pd.DataFrame({'population': [59000000, 65000000, 434000,
...                                   434000, 434000, 337000, 11300,
...                                   11300, 11300],
...                    'GDP': [1937894, 2583560 , 12011, 4520, 12128,
...                            17036, 182, 38, 311],
...                    'alpha-2': ["IT", "FR", "MT", "MV", "BN",
...                                "IS", "NR", "TV", "AI"]},
...                   index=["Italy", "France", "Malta",
...                          "Maldives", "Brunei", "Iceland",
...                          "Nauru", "Tuvalu", "Anguilla"])
>>> df
          population      GDP alpha-2
Italy       59000000  1937894      IT
France      65000000  2583560      FR
Malta         434000    12011      MT
Maldives      434000     4520      MV
Brunei        434000    12128      BN
Iceland       337000    17036      IS
Nauru          11300      182      NR
Tuvalu         11300       38      TV
Anguilla       11300      311      AI

In the following example, we will use nlargest to select the three rows having the largest values in column “population”.

>>> df.nlargest(3, 'population')
        population      GDP alpha-2
France    65000000  2583560      FR
Italy     59000000  1937894      IT
Malta       434000    12011      MT

When using keep='last', ties are resolved in reverse order:

>>> df.nlargest(3, 'population', keep='last')
        population      GDP alpha-2
France    65000000  2583560      FR
Italy     59000000  1937894      IT
Brunei      434000    12128      BN

When using keep='all', all duplicate items are maintained:

>>> df.nlargest(3, 'population', keep='all')
          population      GDP alpha-2
France      65000000  2583560      FR
Italy       59000000  1937894      IT
Malta         434000    12011      MT
Maldives      434000     4520      MV
Brunei        434000    12128      BN

To order by the largest values in column “population” and then “GDP”, we can specify multiple columns like in the next example.

>>> df.nlargest(3, ['population', 'GDP'])
        population      GDP alpha-2
France    65000000  2583560      FR
Italy     59000000  1937894      IT
Brunei      434000    12128      BN
notna() → pandas.core.frame.DataFrame

Detect existing (non-missing) values.

Return a boolean same-sized object indicating if the values are not NA. Non-missing values get mapped to True. Characters such as empty strings '' or numpy.inf are not considered NA values (unless you set pandas.options.mode.use_inf_as_na = True). NA values, such as None or numpy.NaN, get mapped to False values.

Returns

Mask of bool values for each element in DataFrame that indicates whether an element is not an NA value.

Return type

DataFrame

See also

DataFrame.notnull()

Alias of notna.

DataFrame.isna()

Boolean inverse of notna.

DataFrame.dropna()

Omit axes labels with missing values.

notna()

Top-level notna.

Examples

Show which entries in a DataFrame are not NA.

>>> df = pd.DataFrame({'age': [5, 6, np.NaN],
...                    'born': [pd.NaT, pd.Timestamp('1939-05-27'),
...                             pd.Timestamp('1940-04-25')],
...                    'name': ['Alfred', 'Batman', ''],
...                    'toy': [None, 'Batmobile', 'Joker']})
>>> df
   age       born    name        toy
0  5.0        NaT  Alfred       None
1  6.0 1939-05-27  Batman  Batmobile
2  NaN 1940-04-25              Joker
>>> df.notna()
     age   born  name    toy
0   True  False  True  False
1   True   True  True   True
2  False   True  True   True

Show which entries in a Series are not NA.

>>> ser = pd.Series([5, 6, np.NaN])
>>> ser
0    5.0
1    6.0
2    NaN
dtype: float64
>>> ser.notna()
0     True
1     True
2    False
dtype: bool
notnull() → pandas.core.frame.DataFrame

Detect existing (non-missing) values.

Return a boolean same-sized object indicating if the values are not NA. Non-missing values get mapped to True. Characters such as empty strings '' or numpy.inf are not considered NA values (unless you set pandas.options.mode.use_inf_as_na = True). NA values, such as None or numpy.NaN, get mapped to False values.

Returns

Mask of bool values for each element in DataFrame that indicates whether an element is not an NA value.

Return type

DataFrame

See also

DataFrame.notnull()

Alias of notna.

DataFrame.isna()

Boolean inverse of notna.

DataFrame.dropna()

Omit axes labels with missing values.

notna()

Top-level notna.

Examples

Show which entries in a DataFrame are not NA.

>>> df = pd.DataFrame({'age': [5, 6, np.NaN],
...                    'born': [pd.NaT, pd.Timestamp('1939-05-27'),
...                             pd.Timestamp('1940-04-25')],
...                    'name': ['Alfred', 'Batman', ''],
...                    'toy': [None, 'Batmobile', 'Joker']})
>>> df
   age       born    name        toy
0  5.0        NaT  Alfred       None
1  6.0 1939-05-27  Batman  Batmobile
2  NaN 1940-04-25              Joker
>>> df.notna()
     age   born  name    toy
0   True  False  True  False
1   True   True  True   True
2  False   True  True   True

Show which entries in a Series are not NA.

>>> ser = pd.Series([5, 6, np.NaN])
>>> ser
0    5.0
1    6.0
2    NaN
dtype: float64
>>> ser.notna()
0     True
1     True
2    False
dtype: bool
nsmallest(n, columns, keep='first') → pandas.core.frame.DataFrame

Return the first n rows ordered by columns in ascending order.

Return the first n rows with the smallest values in columns, in ascending order. The columns that are not specified are returned as well, but not used for ordering.

This method is equivalent to df.sort_values(columns, ascending=True).head(n), but more performant.

Parameters
  • n (int) – Number of items to retrieve.

  • columns (list or str) – Column name or names to order by.

  • keep ({'first', 'last', 'all'}, default 'first') –

    Where there are duplicate values:

    • first : take the first occurrence.

    • last : take the last occurrence.

    • all : do not drop any duplicates, even it means selecting more than n items.

    New in version 0.24.0.

Returns

Return type

DataFrame

See also

DataFrame.nlargest()

Return the first n rows ordered by columns in descending order.

DataFrame.sort_values()

Sort DataFrame by the values.

DataFrame.head()

Return the first n rows without re-ordering.

Examples

>>> df = pd.DataFrame({'population': [59000000, 65000000, 434000,
...                                   434000, 434000, 337000, 337000,
...                                   11300, 11300],
...                    'GDP': [1937894, 2583560 , 12011, 4520, 12128,
...                            17036, 182, 38, 311],
...                    'alpha-2': ["IT", "FR", "MT", "MV", "BN",
...                                "IS", "NR", "TV", "AI"]},
...                   index=["Italy", "France", "Malta",
...                          "Maldives", "Brunei", "Iceland",
...                          "Nauru", "Tuvalu", "Anguilla"])
>>> df
          population      GDP alpha-2
Italy       59000000  1937894      IT
France      65000000  2583560      FR
Malta         434000    12011      MT
Maldives      434000     4520      MV
Brunei        434000    12128      BN
Iceland       337000    17036      IS
Nauru         337000      182      NR
Tuvalu         11300       38      TV
Anguilla       11300      311      AI

In the following example, we will use nsmallest to select the three rows having the smallest values in column “population”.

>>> df.nsmallest(3, 'population')
          population    GDP alpha-2
Tuvalu         11300     38      TV
Anguilla       11300    311      AI
Iceland       337000  17036          IS

When using keep='last', ties are resolved in reverse order:

>>> df.nsmallest(3, 'population', keep='last')
          population  GDP alpha-2
Anguilla       11300  311      AI
Tuvalu         11300   38      TV
Nauru         337000  182      NR

When using keep='all', all duplicate items are maintained:

>>> df.nsmallest(3, 'population', keep='all')
          population    GDP alpha-2
Tuvalu         11300     38      TV
Anguilla       11300    311      AI
Iceland       337000  17036      IS
Nauru         337000    182      NR

To order by the smallest values in column “population” and then “GDP”, we can specify multiple columns like in the next example.

>>> df.nsmallest(3, ['population', 'GDP'])
          population  GDP alpha-2
Tuvalu         11300   38      TV
Anguilla       11300  311      AI
Nauru         337000  182      NR
nunique(axis=0, dropna=True) → pandas.core.series.Series

Count distinct observations over requested axis.

Return Series with number of distinct observations. Can ignore NaN values.

Parameters
  • axis ({0 or 'index', 1 or 'columns'}, default 0) – The axis to use. 0 or ‘index’ for row-wise, 1 or ‘columns’ for column-wise.

  • dropna (bool, default True) – Don’t include NaN in the counts.

Returns

Return type

Series

See also

Series.nunique()

Method nunique for Series.

DataFrame.count()

Count non-NA cells for each column or row.

Examples

>>> df = pd.DataFrame({'A': [1, 2, 3], 'B': [1, 1, 1]})
>>> df.nunique()
A    3
B    1
dtype: int64
>>> df.nunique(axis=1)
0    1
1    2
2    2
dtype: int64
pad(axis=None, inplace: bool = False, limit=None, downcast=None) → Optional[FrameOrSeries]

Synonym for DataFrame.fillna() with method='ffill'.

Returns

Object with missing values filled or None if inplace=True.

Return type

{klass} or None

pct_change(periods=1, fill_method='pad', limit=None, freq=None, **kwargs) → FrameOrSeries

Percentage change between the current and a prior element.

Computes the percentage change from the immediately previous row by default. This is useful in comparing the percentage of change in a time series of elements.

Parameters
  • periods (int, default 1) – Periods to shift for forming percent change.

  • fill_method (str, default 'pad') – How to handle NAs before computing percent changes.

  • limit (int, default None) – The number of consecutive NAs to fill before stopping.

  • freq (DateOffset, timedelta, or str, optional) – Increment to use from time series API (e.g. ‘M’ or BDay()).

  • **kwargs – Additional keyword arguments are passed into DataFrame.shift or Series.shift.

Returns

chg – The same type as the calling object.

Return type

Series or DataFrame

See also

Series.diff()

Compute the difference of two elements in a Series.

DataFrame.diff()

Compute the difference of two elements in a DataFrame.

Series.shift()

Shift the index by some number of periods.

DataFrame.shift()

Shift the index by some number of periods.

Examples

Series

>>> s = pd.Series([90, 91, 85])
>>> s
0    90
1    91
2    85
dtype: int64
>>> s.pct_change()
0         NaN
1    0.011111
2   -0.065934
dtype: float64
>>> s.pct_change(periods=2)
0         NaN
1         NaN
2   -0.055556
dtype: float64

See the percentage change in a Series where filling NAs with last valid observation forward to next valid.

>>> s = pd.Series([90, 91, None, 85])
>>> s
0    90.0
1    91.0
2     NaN
3    85.0
dtype: float64
>>> s.pct_change(fill_method='ffill')
0         NaN
1    0.011111
2    0.000000
3   -0.065934
dtype: float64

DataFrame

Percentage change in French franc, Deutsche Mark, and Italian lira from 1980-01-01 to 1980-03-01.

>>> df = pd.DataFrame({
...     'FR': [4.0405, 4.0963, 4.3149],
...     'GR': [1.7246, 1.7482, 1.8519],
...     'IT': [804.74, 810.01, 860.13]},
...     index=['1980-01-01', '1980-02-01', '1980-03-01'])
>>> df
                FR      GR      IT
1980-01-01  4.0405  1.7246  804.74
1980-02-01  4.0963  1.7482  810.01
1980-03-01  4.3149  1.8519  860.13
>>> df.pct_change()
                  FR        GR        IT
1980-01-01       NaN       NaN       NaN
1980-02-01  0.013810  0.013684  0.006549
1980-03-01  0.053365  0.059318  0.061876

Percentage of change in GOOG and APPL stock volume. Shows computing the percentage change between columns.

>>> df = pd.DataFrame({
...     '2016': [1769950, 30586265],
...     '2015': [1500923, 40912316],
...     '2014': [1371819, 41403351]},
...     index=['GOOG', 'APPL'])
>>> df
          2016      2015      2014
GOOG   1769950   1500923   1371819
APPL  30586265  40912316  41403351
>>> df.pct_change(axis='columns')
      2016      2015      2014
GOOG   NaN -0.151997 -0.086016
APPL   NaN  0.337604  0.012002
pipe(func, *args, **kwargs)

Apply func(self, *args, **kwargs).

Parameters
  • func (function) – Function to apply to the Series/DataFrame. args, and kwargs are passed into func. Alternatively a (callable, data_keyword) tuple where data_keyword is a string indicating the keyword of callable that expects the Series/DataFrame.

  • args (iterable, optional) – Positional arguments passed into func.

  • kwargs (mapping, optional) – A dictionary of keyword arguments passed into func.

Returns

object

Return type

the return type of func.

See also

DataFrame.apply()

Apply a function along input axis of DataFrame.

DataFrame.applymap()

Apply a function elementwise on a whole DataFrame.

Series.map()

Apply a mapping correspondence on a Series.

Notes

Use .pipe when chaining together functions that expect Series, DataFrames or GroupBy objects. Instead of writing

>>> func(g(h(df), arg1=a), arg2=b, arg3=c)  

You can write

>>> (df.pipe(h)
...    .pipe(g, arg1=a)
...    .pipe(func, arg2=b, arg3=c)
... )  

If you have a function that takes the data as (say) the second argument, pass a tuple indicating which keyword expects the data. For example, suppose f takes its data as arg2:

>>> (df.pipe(h)
...    .pipe(g, arg1=a)
...    .pipe((func, 'arg2'), arg1=a, arg3=c)
...  )  
pivot(index=None, columns=None, values=None) → pandas.core.frame.DataFrame

Return reshaped DataFrame organized by given index / column values.

Reshape data (produce a “pivot” table) based on column values. Uses unique values from specified index / columns to form axes of the resulting DataFrame. This function does not support data aggregation, multiple values will result in a MultiIndex in the columns. See the User Guide for more on reshaping.

Parameters
  • index (str or object or a list of str, optional) –

    Column to use to make new frame’s index. If None, uses existing index.

    Changed in version 1.1.0: Also accept list of index names.

  • columns (str or object or a list of str) –

    Column to use to make new frame’s columns.

    Changed in version 1.1.0: Also accept list of columns names.

  • values (str, object or a list of the previous, optional) –

    Column(s) to use for populating new frame’s values. If not specified, all remaining columns will be used and the result will have hierarchically indexed columns.

    Changed in version 0.23.0: Also accept list of column names.

Returns

Returns reshaped DataFrame.

Return type

DataFrame

Raises

ValueError: – When there are any index, columns combinations with multiple values. DataFrame.pivot_table when you need to aggregate.

See also

DataFrame.pivot_table()

Generalization of pivot that can handle duplicate values for one index/column pair.

DataFrame.unstack()

Pivot based on the index values instead of a column.

Notes

For finer-tuned control, see hierarchical indexing documentation along with the related stack/unstack methods.

Examples

>>> df = pd.DataFrame({'foo': ['one', 'one', 'one', 'two', 'two',
...                            'two'],
...                    'bar': ['A', 'B', 'C', 'A', 'B', 'C'],
...                    'baz': [1, 2, 3, 4, 5, 6],
...                    'zoo': ['x', 'y', 'z', 'q', 'w', 't']})
>>> df
    foo   bar  baz  zoo
0   one   A    1    x
1   one   B    2    y
2   one   C    3    z
3   two   A    4    q
4   two   B    5    w
5   two   C    6    t
>>> df.pivot(index='foo', columns='bar', values='baz')
bar  A   B   C
foo
one  1   2   3
two  4   5   6
>>> df.pivot(index='foo', columns='bar')['baz']
bar  A   B   C
foo
one  1   2   3
two  4   5   6
>>> df.pivot(index='foo', columns='bar', values=['baz', 'zoo'])
      baz       zoo
bar   A  B  C   A  B  C
foo
one   1  2  3   x  y  z
two   4  5  6   q  w  t

You could also assign a list of column names or a list of index names.

>>> df = pd.DataFrame({
...        "lev1": [1, 1, 1, 2, 2, 2],
...        "lev2": [1, 1, 2, 1, 1, 2],
...        "lev3": [1, 2, 1, 2, 1, 2],
...        "lev4": [1, 2, 3, 4, 5, 6],
...        "values": [0, 1, 2, 3, 4, 5]})
>>> df
    lev1 lev2 lev3 lev4 values
0   1    1    1    1    0
1   1    1    2    2    1
2   1    2    1    3    2
3   2    1    2    4    3
4   2    1    1    5    4
5   2    2    2    6    5
>>> df.pivot(index="lev1", columns=["lev2", "lev3"],values="values")
lev2    1         2
lev3    1    2    1    2
lev1
1     0.0  1.0  2.0  NaN
2     4.0  3.0  NaN  5.0
>>> df.pivot(index=["lev1", "lev2"], columns=["lev3"],values="values")
      lev3    1    2
lev1  lev2
   1     1  0.0  1.0
         2  2.0  NaN
   2     1  4.0  3.0
         2  NaN  5.0

A ValueError is raised if there are any duplicates.

>>> df = pd.DataFrame({"foo": ['one', 'one', 'two', 'two'],
...                    "bar": ['A', 'A', 'B', 'C'],
...                    "baz": [1, 2, 3, 4]})
>>> df
   foo bar  baz
0  one   A    1
1  one   A    2
2  two   B    3
3  two   C    4

Notice that the first two rows are the same for our index and columns arguments.

>>> df.pivot(index='foo', columns='bar', values='baz')
Traceback (most recent call last):
   ...
ValueError: Index contains duplicate entries, cannot reshape
pivot_table(values=None, index=None, columns=None, aggfunc='mean', fill_value=None, margins=False, dropna=True, margins_name='All', observed=False) → pandas.core.frame.DataFrame

Create a spreadsheet-style pivot table as a DataFrame.

The levels in the pivot table will be stored in MultiIndex objects (hierarchical indexes) on the index and columns of the result DataFrame.

Parameters
  • values (column to aggregate, optional) –

  • index (column, Grouper, array, or list of the previous) – If an array is passed, it must be the same length as the data. The list can contain any of the other types (except list). Keys to group by on the pivot table index. If an array is passed, it is being used as the same manner as column values.

  • columns (column, Grouper, array, or list of the previous) – If an array is passed, it must be the same length as the data. The list can contain any of the other types (except list). Keys to group by on the pivot table column. If an array is passed, it is being used as the same manner as column values.

  • aggfunc (function, list of functions, dict, default numpy.mean) – If list of functions passed, the resulting pivot table will have hierarchical columns whose top level are the function names (inferred from the function objects themselves) If dict is passed, the key is column to aggregate and value is function or list of functions.

  • fill_value (scalar, default None) – Value to replace missing values with (in the resulting pivot table, after aggregation).

  • margins (bool, default False) – Add all row / columns (e.g. for subtotal / grand totals).

  • dropna (bool, default True) – Do not include columns whose entries are all NaN.

  • margins_name (str, default 'All') – Name of the row / column that will contain the totals when margins is True.

  • observed (bool, default False) –

    This only applies if any of the groupers are Categoricals. If True: only show observed values for categorical groupers. If False: show all values for categorical groupers.

    Changed in version 0.25.0.

Returns

An Excel style pivot table.

Return type

DataFrame

See also

DataFrame.pivot()

Pivot without aggregation that can handle non-numeric data.

Examples

>>> df = pd.DataFrame({"A": ["foo", "foo", "foo", "foo", "foo",
...                          "bar", "bar", "bar", "bar"],
...                    "B": ["one", "one", "one", "two", "two",
...                          "one", "one", "two", "two"],
...                    "C": ["small", "large", "large", "small",
...                          "small", "large", "small", "small",
...                          "large"],
...                    "D": [1, 2, 2, 3, 3, 4, 5, 6, 7],
...                    "E": [2, 4, 5, 5, 6, 6, 8, 9, 9]})
>>> df
     A    B      C  D  E
0  foo  one  small  1  2
1  foo  one  large  2  4
2  foo  one  large  2  5
3  foo  two  small  3  5
4  foo  two  small  3  6
5  bar  one  large  4  6
6  bar  one  small  5  8
7  bar  two  small  6  9
8  bar  two  large  7  9

This first example aggregates values by taking the sum.

>>> table = pd.pivot_table(df, values='D', index=['A', 'B'],
...                     columns=['C'], aggfunc=np.sum)
>>> table
C        large  small
A   B
bar one    4.0    5.0
    two    7.0    6.0
foo one    4.0    1.0
    two    NaN    6.0

We can also fill missing values using the fill_value parameter.

>>> table = pd.pivot_table(df, values='D', index=['A', 'B'],
...                     columns=['C'], aggfunc=np.sum, fill_value=0)
>>> table
C        large  small
A   B
bar one      4      5
    two      7      6
foo one      4      1
    two      0      6

The next example aggregates by taking the mean across multiple columns.

>>> table = pd.pivot_table(df, values=['D', 'E'], index=['A', 'C'],
...                     aggfunc={'D': np.mean,
...                              'E': np.mean})
>>> table
                D         E
A   C
bar large  5.500000  7.500000
    small  5.500000  8.500000
foo large  2.000000  4.500000
    small  2.333333  4.333333

We can also calculate multiple types of aggregations for any given value column.

>>> table = pd.pivot_table(df, values=['D', 'E'], index=['A', 'C'],
...                     aggfunc={'D': np.mean,
...                              'E': [min, max, np.mean]})
>>> table
                D    E
            mean  max      mean  min
A   C
bar large  5.500000  9.0  7.500000  6.0
    small  5.500000  9.0  8.500000  8.0
foo large  2.000000  5.0  4.500000  4.0
    small  2.333333  6.0  4.333333  2.0
plot

alias of pandas.plotting._core.PlotAccessor

pop(item: Optional[Hashable]) → pandas.core.series.Series

Return item and drop from frame. Raise KeyError if not found.

Parameters

item (label) – Label of column to be popped.

Returns

Return type

Series

Examples

>>> df = pd.DataFrame([('falcon', 'bird', 389.0),
...                    ('parrot', 'bird', 24.0),
...                    ('lion', 'mammal', 80.5),
...                    ('monkey', 'mammal', np.nan)],
...                   columns=('name', 'class', 'max_speed'))
>>> df
     name   class  max_speed
0  falcon    bird      389.0
1  parrot    bird       24.0
2    lion  mammal       80.5
3  monkey  mammal        NaN
>>> df.pop('class')
0      bird
1      bird
2    mammal
3    mammal
Name: class, dtype: object
>>> df
     name  max_speed
0  falcon      389.0
1  parrot       24.0
2    lion       80.5
3  monkey        NaN
pow(other, axis='columns', level=None, fill_value=None)

Get Exponential power of dataframe and other, element-wise (binary operator pow).

Equivalent to dataframe ** other, but with support to substitute a fill_value for missing data in one of the inputs. With reverse version, rpow.

Among flexible wrappers (add, sub, mul, div, mod, pow) to arithmetic operators: +, -, *, /, //, %, **.

Parameters
  • other (scalar, sequence, Series, or DataFrame) – Any single or multiple element data structure, or list-like object.

  • axis ({0 or 'index', 1 or 'columns'}) – Whether to compare by the index (0 or ‘index’) or columns (1 or ‘columns’). For Series input, axis to match Series index on.

  • level (int or label) – Broadcast across a level, matching Index values on the passed MultiIndex level.

  • fill_value (float or None, default None) – Fill existing missing (NaN) values, and any new element needed for successful DataFrame alignment, with this value before computation. If data in both corresponding DataFrame locations is missing the result will be missing.

Returns

Result of the arithmetic operation.

Return type

DataFrame

See also

DataFrame.add()

Add DataFrames.

DataFrame.sub()

Subtract DataFrames.

DataFrame.mul()

Multiply DataFrames.

DataFrame.div()

Divide DataFrames (float division).

DataFrame.truediv()

Divide DataFrames (float division).

DataFrame.floordiv()

Divide DataFrames (integer division).

DataFrame.mod()

Calculate modulo (remainder after division).

DataFrame.pow()

Calculate exponential power.

Notes

Mismatched indices will be unioned together.

Examples

>>> df = pd.DataFrame({'angles': [0, 3, 4],
...                    'degrees': [360, 180, 360]},
...                   index=['circle', 'triangle', 'rectangle'])
>>> df
           angles  degrees
circle          0      360
triangle        3      180
rectangle       4      360

Add a scalar with operator version which return the same results.

>>> df + 1
           angles  degrees
circle          1      361
triangle        4      181
rectangle       5      361
>>> df.add(1)
           angles  degrees
circle          1      361
triangle        4      181
rectangle       5      361

Divide by constant with reverse version.

>>> df.div(10)
           angles  degrees
circle        0.0     36.0
triangle      0.3     18.0
rectangle     0.4     36.0
>>> df.rdiv(10)
             angles   degrees
circle          inf  0.027778
triangle   3.333333  0.055556
rectangle  2.500000  0.027778

Subtract a list and Series by axis with operator version.

>>> df - [1, 2]
           angles  degrees
circle         -1      358
triangle        2      178
rectangle       3      358
>>> df.sub([1, 2], axis='columns')
           angles  degrees
circle         -1      358
triangle        2      178
rectangle       3      358
>>> df.sub(pd.Series([1, 1, 1], index=['circle', 'triangle', 'rectangle']),
...        axis='index')
           angles  degrees
circle         -1      359
triangle        2      179
rectangle       3      359

Multiply a DataFrame of different shape with operator version.

>>> other = pd.DataFrame({'angles': [0, 3, 4]},
...                      index=['circle', 'triangle', 'rectangle'])
>>> other
           angles
circle          0
triangle        3
rectangle       4
>>> df * other
           angles  degrees
circle          0      NaN
triangle        9      NaN
rectangle      16      NaN
>>> df.mul(other, fill_value=0)
           angles  degrees
circle          0      0.0
triangle        9      0.0
rectangle      16      0.0

Divide by a MultiIndex by level.

>>> df_multindex = pd.DataFrame({'angles': [0, 3, 4, 4, 5, 6],
...                              'degrees': [360, 180, 360, 360, 540, 720]},
...                             index=[['A', 'A', 'A', 'B', 'B', 'B'],
...                                    ['circle', 'triangle', 'rectangle',
...                                     'square', 'pentagon', 'hexagon']])
>>> df_multindex
             angles  degrees
A circle          0      360
  triangle        3      180
  rectangle       4      360
B square          4      360
  pentagon        5      540
  hexagon         6      720
>>> df.div(df_multindex, level=1, fill_value=0)
             angles  degrees
A circle        NaN      1.0
  triangle      1.0      1.0
  rectangle     1.0      1.0
B square        0.0      0.0
  pentagon      0.0      0.0
  hexagon       0.0      0.0
prod(axis=None, skipna=None, level=None, numeric_only=None, min_count=0, **kwargs)

Return the product of the values for the requested axis.

Parameters
  • axis ({index (0), columns (1)}) – Axis for the function to be applied on.

  • skipna (bool, default True) – Exclude NA/null values when computing the result.

  • level (int or level name, default None) – If the axis is a MultiIndex (hierarchical), count along a particular level, collapsing into a Series.

  • numeric_only (bool, default None) – Include only float, int, boolean columns. If None, will attempt to use everything, then use only numeric data. Not implemented for Series.

  • min_count (int, default 0) –

    The required number of valid values to perform the operation. If fewer than min_count non-NA values are present the result will be NA.

    New in version 0.22.0: Added with the default being 0. This means the sum of an all-NA or empty Series is 0, and the product of an all-NA or empty Series is 1.

  • **kwargs – Additional keyword arguments to be passed to the function.

Returns

Return type

Series or DataFrame (if level specified)

Examples

By default, the product of an empty or all-NA Series is 1

>>> pd.Series([]).prod()
1.0

This can be controlled with the min_count parameter

>>> pd.Series([]).prod(min_count=1)
nan

Thanks to the skipna parameter, min_count handles all-NA and empty series identically.

>>> pd.Series([np.nan]).prod()
1.0
>>> pd.Series([np.nan]).prod(min_count=1)
nan
product(axis=None, skipna=None, level=None, numeric_only=None, min_count=0, **kwargs)

Return the product of the values for the requested axis.

Parameters
  • axis ({index (0), columns (1)}) – Axis for the function to be applied on.

  • skipna (bool, default True) – Exclude NA/null values when computing the result.

  • level (int or level name, default None) – If the axis is a MultiIndex (hierarchical), count along a particular level, collapsing into a Series.

  • numeric_only (bool, default None) – Include only float, int, boolean columns. If None, will attempt to use everything, then use only numeric data. Not implemented for Series.

  • min_count (int, default 0) –

    The required number of valid values to perform the operation. If fewer than min_count non-NA values are present the result will be NA.

    New in version 0.22.0: Added with the default being 0. This means the sum of an all-NA or empty Series is 0, and the product of an all-NA or empty Series is 1.

  • **kwargs – Additional keyword arguments to be passed to the function.

Returns

Return type

Series or DataFrame (if level specified)

Examples

By default, the product of an empty or all-NA Series is 1

>>> pd.Series([]).prod()
1.0

This can be controlled with the min_count parameter

>>> pd.Series([]).prod(min_count=1)
nan

Thanks to the skipna parameter, min_count handles all-NA and empty series identically.

>>> pd.Series([np.nan]).prod()
1.0
>>> pd.Series([np.nan]).prod(min_count=1)
nan
quantile(q=0.5, axis=0, numeric_only=True, interpolation='linear')

Return values at the given quantile over requested axis.

Parameters
  • q (float or array-like, default 0.5 (50% quantile)) – Value between 0 <= q <= 1, the quantile(s) to compute.

  • axis ({0, 1, 'index', 'columns'}, default 0) – Equals 0 or ‘index’ for row-wise, 1 or ‘columns’ for column-wise.

  • numeric_only (bool, default True) – If False, the quantile of datetime and timedelta data will be computed as well.

  • interpolation ({'linear', 'lower', 'higher', 'midpoint', 'nearest'}) –

    This optional parameter specifies the interpolation method to use, when the desired quantile lies between two data points i and j:

    • linear: i + (j - i) * fraction, where fraction is the fractional part of the index surrounded by i and j.

    • lower: i.

    • higher: j.

    • nearest: i or j whichever is nearest.

    • midpoint: (i + j) / 2.

Returns

If q is an array, a DataFrame will be returned where the

index is q, the columns are the columns of self, and the values are the quantiles.

If q is a float, a Series will be returned where the

index is the columns of self and the values are the quantiles.

Return type

Series or DataFrame

See also

core.window.Rolling.quantile()

Rolling quantile.

numpy.percentile()

Numpy function to compute the percentile.

Examples

>>> df = pd.DataFrame(np.array([[1, 1], [2, 10], [3, 100], [4, 100]]),
...                   columns=['a', 'b'])
>>> df.quantile(.1)
a    1.3
b    3.7
Name: 0.1, dtype: float64
>>> df.quantile([.1, .5])
       a     b
0.1  1.3   3.7
0.5  2.5  55.0

Specifying numeric_only=False will also compute the quantile of datetime and timedelta data.

>>> df = pd.DataFrame({'A': [1, 2],
...                    'B': [pd.Timestamp('2010'),
...                          pd.Timestamp('2011')],
...                    'C': [pd.Timedelta('1 days'),
...                          pd.Timedelta('2 days')]})
>>> df.quantile(0.5, numeric_only=False)
A                    1.5
B    2010-07-02 12:00:00
C        1 days 12:00:00
Name: 0.5, dtype: object
query(expr, inplace=False, **kwargs)

Query the columns of a DataFrame with a boolean expression.

Parameters
  • expr (str) –

    The query string to evaluate.

    You can refer to variables in the environment by prefixing them with an ‘@’ character like @a + b.

    You can refer to column names that contain spaces or operators by surrounding them in backticks. This way you can also escape names that start with a digit, or those that are a Python keyword. Basically when it is not valid Python identifier. See notes down for more details.

    For example, if one of your columns is called a a and you want to sum it with b, your query should be `a a` + b.

    New in version 0.25.0: Backtick quoting introduced.

    New in version 1.0.0: Expanding functionality of backtick quoting for more than only spaces.

  • inplace (bool) – Whether the query should modify the data in place or return a modified copy.

  • **kwargs – See the documentation for eval() for complete details on the keyword arguments accepted by DataFrame.query().

Returns

DataFrame resulting from the provided query expression.

Return type

DataFrame

See also

eval()

Evaluate a string describing operations on DataFrame columns.

DataFrame.eval()

Evaluate a string describing operations on DataFrame columns.

Notes

The result of the evaluation of this expression is first passed to DataFrame.loc and if that fails because of a multidimensional key (e.g., a DataFrame) then the result will be passed to DataFrame.__getitem__().

This method uses the top-level eval() function to evaluate the passed query.

The query() method uses a slightly modified Python syntax by default. For example, the & and | (bitwise) operators have the precedence of their boolean cousins, and and or. This is syntactically valid Python, however the semantics are different.

You can change the semantics of the expression by passing the keyword argument parser='python'. This enforces the same semantics as evaluation in Python space. Likewise, you can pass engine='python' to evaluate an expression using Python itself as a backend. This is not recommended as it is inefficient compared to using numexpr as the engine.

The DataFrame.index and DataFrame.columns attributes of the DataFrame instance are placed in the query namespace by default, which allows you to treat both the index and columns of the frame as a column in the frame. The identifier index is used for the frame index; you can also use the name of the index to identify it in a query. Please note that Python keywords may not be used as identifiers.

For further details and examples see the query documentation in indexing.

Backtick quoted variables

Backtick quoted variables are parsed as literal Python code and are converted internally to a Python valid identifier. This can lead to the following problems.

During parsing a number of disallowed characters inside the backtick quoted string are replaced by strings that are allowed as a Python identifier. These characters include all operators in Python, the space character, the question mark, the exclamation mark, the dollar sign, and the euro sign. For other characters that fall outside the ASCII range (U+0001..U+007F) and those that are not further specified in PEP 3131, the query parser will raise an error. This excludes whitespace different than the space character, but also the hashtag (as it is used for comments) and the backtick itself (backtick can also not be escaped).

In a special case, quotes that make a pair around a backtick can confuse the parser. For example, `it's` > `that's` will raise an error, as it forms a quoted string ('s > `that') with a backtick inside.

See also the Python documentation about lexical analysis (https://docs.python.org/3/reference/lexical_analysis.html) in combination with the source code in pandas.core.computation.parsing.

Examples

>>> df = pd.DataFrame({'A': range(1, 6),
...                    'B': range(10, 0, -2),
...                    'C C': range(10, 5, -1)})
>>> df
   A   B  C C
0  1  10   10
1  2   8    9
2  3   6    8
3  4   4    7
4  5   2    6
>>> df.query('A > B')
   A  B  C C
4  5  2    6

The previous expression is equivalent to

>>> df[df.A > df.B]
   A  B  C C
4  5  2    6

For columns with spaces in their name, you can use backtick quoting.

>>> df.query('B == `C C`')
   A   B  C C
0  1  10   10

The previous expression is equivalent to

>>> df[df.B == df['C C']]
   A   B  C C
0  1  10   10
radd(other, axis='columns', level=None, fill_value=None)

Get Addition of dataframe and other, element-wise (binary operator radd).

Equivalent to other + dataframe, but with support to substitute a fill_value for missing data in one of the inputs. With reverse version, add.

Among flexible wrappers (add, sub, mul, div, mod, pow) to arithmetic operators: +, -, *, /, //, %, **.

Parameters
  • other (scalar, sequence, Series, or DataFrame) – Any single or multiple element data structure, or list-like object.

  • axis ({0 or 'index', 1 or 'columns'}) – Whether to compare by the index (0 or ‘index’) or columns (1 or ‘columns’). For Series input, axis to match Series index on.

  • level (int or label) – Broadcast across a level, matching Index values on the passed MultiIndex level.

  • fill_value (float or None, default None) – Fill existing missing (NaN) values, and any new element needed for successful DataFrame alignment, with this value before computation. If data in both corresponding DataFrame locations is missing the result will be missing.

Returns

Result of the arithmetic operation.

Return type

DataFrame

See also

DataFrame.add()

Add DataFrames.

DataFrame.sub()

Subtract DataFrames.

DataFrame.mul()

Multiply DataFrames.

DataFrame.div()

Divide DataFrames (float division).

DataFrame.truediv()

Divide DataFrames (float division).

DataFrame.floordiv()

Divide DataFrames (integer division).

DataFrame.mod()

Calculate modulo (remainder after division).

DataFrame.pow()

Calculate exponential power.

Notes

Mismatched indices will be unioned together.

Examples

>>> df = pd.DataFrame({'angles': [0, 3, 4],
...                    'degrees': [360, 180, 360]},
...                   index=['circle', 'triangle', 'rectangle'])
>>> df
           angles  degrees
circle          0      360
triangle        3      180
rectangle       4      360

Add a scalar with operator version which return the same results.

>>> df + 1
           angles  degrees
circle          1      361
triangle        4      181
rectangle       5      361
>>> df.add(1)
           angles  degrees
circle          1      361
triangle        4      181
rectangle       5      361

Divide by constant with reverse version.

>>> df.div(10)
           angles  degrees
circle        0.0     36.0
triangle      0.3     18.0
rectangle     0.4     36.0
>>> df.rdiv(10)
             angles   degrees
circle          inf  0.027778
triangle   3.333333  0.055556
rectangle  2.500000  0.027778

Subtract a list and Series by axis with operator version.

>>> df - [1, 2]
           angles  degrees
circle         -1      358
triangle        2      178
rectangle       3      358
>>> df.sub([1, 2], axis='columns')
           angles  degrees
circle         -1      358
triangle        2      178
rectangle       3      358
>>> df.sub(pd.Series([1, 1, 1], index=['circle', 'triangle', 'rectangle']),
...        axis='index')
           angles  degrees
circle         -1      359
triangle        2      179
rectangle       3      359

Multiply a DataFrame of different shape with operator version.

>>> other = pd.DataFrame({'angles': [0, 3, 4]},
...                      index=['circle', 'triangle', 'rectangle'])
>>> other
           angles
circle          0
triangle        3
rectangle       4
>>> df * other
           angles  degrees
circle          0      NaN
triangle        9      NaN
rectangle      16      NaN
>>> df.mul(other, fill_value=0)
           angles  degrees
circle          0      0.0
triangle        9      0.0
rectangle      16      0.0

Divide by a MultiIndex by level.

>>> df_multindex = pd.DataFrame({'angles': [0, 3, 4, 4, 5, 6],
...                              'degrees': [360, 180, 360, 360, 540, 720]},
...                             index=[['A', 'A', 'A', 'B', 'B', 'B'],
...                                    ['circle', 'triangle', 'rectangle',
...                                     'square', 'pentagon', 'hexagon']])
>>> df_multindex
             angles  degrees
A circle          0      360
  triangle        3      180
  rectangle       4      360
B square          4      360
  pentagon        5      540
  hexagon         6      720
>>> df.div(df_multindex, level=1, fill_value=0)
             angles  degrees
A circle        NaN      1.0
  triangle      1.0      1.0
  rectangle     1.0      1.0
B square        0.0      0.0
  pentagon      0.0      0.0
  hexagon       0.0      0.0
rank(axis=0, method: str = 'average', numeric_only: Optional[bool] = None, na_option: str = 'keep', ascending: bool = True, pct: bool = False) → FrameOrSeries

Compute numerical data ranks (1 through n) along axis.

By default, equal values are assigned a rank that is the average of the ranks of those values.

Parameters
  • axis ({0 or 'index', 1 or 'columns'}, default 0) – Index to direct ranking.

  • method ({'average', 'min', 'max', 'first', 'dense'}, default 'average') –

    How to rank the group of records that have the same value (i.e. ties):

    • average: average rank of the group

    • min: lowest rank in the group

    • max: highest rank in the group

    • first: ranks assigned in order they appear in the array

    • dense: like ‘min’, but rank always increases by 1 between groups.

  • numeric_only (bool, optional) – For DataFrame objects, rank only numeric columns if set to True.

  • na_option ({'keep', 'top', 'bottom'}, default 'keep') –

    How to rank NaN values:

    • keep: assign NaN rank to NaN values

    • top: assign smallest rank to NaN values if ascending

    • bottom: assign highest rank to NaN values if ascending.

  • ascending (bool, default True) – Whether or not the elements should be ranked in ascending order.

  • pct (bool, default False) – Whether or not to display the returned rankings in percentile form.

Returns

Return a Series or DataFrame with data ranks as values.

Return type

same type as caller

See also

core.groupby.GroupBy.rank()

Rank of values within each group.

Examples

>>> df = pd.DataFrame(data={'Animal': ['cat', 'penguin', 'dog',
...                                    'spider', 'snake'],
...                         'Number_legs': [4, 2, 4, 8, np.nan]})
>>> df
    Animal  Number_legs
0      cat          4.0
1  penguin          2.0
2      dog          4.0
3   spider          8.0
4    snake          NaN

The following example shows how the method behaves with the above parameters:

  • default_rank: this is the default behaviour obtained without using any parameter.

  • max_rank: setting method = 'max' the records that have the same values are ranked using the highest rank (e.g.: since ‘cat’ and ‘dog’ are both in the 2nd and 3rd position, rank 3 is assigned.)

  • NA_bottom: choosing na_option = 'bottom', if there are records with NaN values they are placed at the bottom of the ranking.

  • pct_rank: when setting pct = True, the ranking is expressed as percentile rank.

>>> df['default_rank'] = df['Number_legs'].rank()
>>> df['max_rank'] = df['Number_legs'].rank(method='max')
>>> df['NA_bottom'] = df['Number_legs'].rank(na_option='bottom')
>>> df['pct_rank'] = df['Number_legs'].rank(pct=True)
>>> df
    Animal  Number_legs  default_rank  max_rank  NA_bottom  pct_rank
0      cat          4.0           2.5       3.0        2.5     0.625
1  penguin          2.0           1.0       1.0        1.0     0.250
2      dog          4.0           2.5       3.0        2.5     0.625
3   spider          8.0           4.0       4.0        4.0     1.000
4    snake          NaN           NaN       NaN        5.0       NaN
rdiv(other, axis='columns', level=None, fill_value=None)

Get Floating division of dataframe and other, element-wise (binary operator rtruediv).

Equivalent to other / dataframe, but with support to substitute a fill_value for missing data in one of the inputs. With reverse version, truediv.

Among flexible wrappers (add, sub, mul, div, mod, pow) to arithmetic operators: +, -, *, /, //, %, **.

Parameters
  • other (scalar, sequence, Series, or DataFrame) – Any single or multiple element data structure, or list-like object.

  • axis ({0 or 'index', 1 or 'columns'}) – Whether to compare by the index (0 or ‘index’) or columns (1 or ‘columns’). For Series input, axis to match Series index on.

  • level (int or label) – Broadcast across a level, matching Index values on the passed MultiIndex level.

  • fill_value (float or None, default None) – Fill existing missing (NaN) values, and any new element needed for successful DataFrame alignment, with this value before computation. If data in both corresponding DataFrame locations is missing the result will be missing.

Returns

Result of the arithmetic operation.

Return type

DataFrame

See also

DataFrame.add()

Add DataFrames.

DataFrame.sub()

Subtract DataFrames.

DataFrame.mul()

Multiply DataFrames.

DataFrame.div()

Divide DataFrames (float division).

DataFrame.truediv()

Divide DataFrames (float division).

DataFrame.floordiv()

Divide DataFrames (integer division).

DataFrame.mod()

Calculate modulo (remainder after division).

DataFrame.pow()

Calculate exponential power.

Notes

Mismatched indices will be unioned together.

Examples

>>> df = pd.DataFrame({'angles': [0, 3, 4],
...                    'degrees': [360, 180, 360]},
...                   index=['circle', 'triangle', 'rectangle'])
>>> df
           angles  degrees
circle          0      360
triangle        3      180
rectangle       4      360

Add a scalar with operator version which return the same results.

>>> df + 1
           angles  degrees
circle          1      361
triangle        4      181
rectangle       5      361
>>> df.add(1)
           angles  degrees
circle          1      361
triangle        4      181
rectangle       5      361

Divide by constant with reverse version.

>>> df.div(10)
           angles  degrees
circle        0.0     36.0
triangle      0.3     18.0
rectangle     0.4     36.0
>>> df.rdiv(10)
             angles   degrees
circle          inf  0.027778
triangle   3.333333  0.055556
rectangle  2.500000  0.027778

Subtract a list and Series by axis with operator version.

>>> df - [1, 2]
           angles  degrees
circle         -1      358
triangle        2      178
rectangle       3      358
>>> df.sub([1, 2], axis='columns')
           angles  degrees
circle         -1      358
triangle        2      178
rectangle       3      358
>>> df.sub(pd.Series([1, 1, 1], index=['circle', 'triangle', 'rectangle']),
...        axis='index')
           angles  degrees
circle         -1      359
triangle        2      179
rectangle       3      359

Multiply a DataFrame of different shape with operator version.

>>> other = pd.DataFrame({'angles': [0, 3, 4]},
...                      index=['circle', 'triangle', 'rectangle'])
>>> other
           angles
circle          0
triangle        3
rectangle       4
>>> df * other
           angles  degrees
circle          0      NaN
triangle        9      NaN
rectangle      16      NaN
>>> df.mul(other, fill_value=0)
           angles  degrees
circle          0      0.0
triangle        9      0.0
rectangle      16      0.0

Divide by a MultiIndex by level.

>>> df_multindex = pd.DataFrame({'angles': [0, 3, 4, 4, 5, 6],
...                              'degrees': [360, 180, 360, 360, 540, 720]},
...                             index=[['A', 'A', 'A', 'B', 'B', 'B'],
...                                    ['circle', 'triangle', 'rectangle',
...                                     'square', 'pentagon', 'hexagon']])
>>> df_multindex
             angles  degrees
A circle          0      360
  triangle        3      180
  rectangle       4      360
B square          4      360
  pentagon        5      540
  hexagon         6      720
>>> df.div(df_multindex, level=1, fill_value=0)
             angles  degrees
A circle        NaN      1.0
  triangle      1.0      1.0
  rectangle     1.0      1.0
B square        0.0      0.0
  pentagon      0.0      0.0
  hexagon       0.0      0.0
reindex(labels=None, index=None, columns=None, axis=None, method=None, copy=True, level=None, fill_value=nan, limit=None, tolerance=None) → pandas.core.frame.DataFrame

Conform Series/DataFrame to new index with optional filling logic.

Places NA/NaN in locations having no value in the previous index. A new object is produced unless the new index is equivalent to the current one and copy=False.

Parameters
  • for axes (keywords) – New labels / index to conform to, should be specified using keywords. Preferably an Index object to avoid duplicating data.

  • method ({None, 'backfill'/'bfill', 'pad'/'ffill', 'nearest'}) –

    Method to use for filling holes in reindexed DataFrame. Please note: this is only applicable to DataFrames/Series with a monotonically increasing/decreasing index.

    • None (default): don’t fill gaps

    • pad / ffill: Propagate last valid observation forward to next valid.

    • backfill / bfill: Use next valid observation to fill gap.

    • nearest: Use nearest valid observations to fill gap.

  • copy (bool, default True) – Return a new object, even if the passed indexes are the same.

  • level (int or name) – Broadcast across a level, matching Index values on the passed MultiIndex level.

  • fill_value (scalar, default np.NaN) – Value to use for missing values. Defaults to NaN, but can be any “compatible” value.

  • limit (int, default None) – Maximum number of consecutive elements to forward or backward fill.

  • tolerance (optional) –

    Maximum distance between original and new labels for inexact matches. The values of the index at the matching locations most satisfy the equation abs(index[indexer] - target) <= tolerance.

    Tolerance may be a scalar value, which applies the same tolerance to all values, or list-like, which applies variable tolerance per element. List-like includes list, tuple, array, Series, and must be the same size as the index and its dtype must exactly match the index’s type.

Returns

Return type

Series/DataFrame with changed index.

See also

DataFrame.set_index()

Set row labels.

DataFrame.reset_index()

Remove row labels or move them to new columns.

DataFrame.reindex_like()

Change to same indices as other DataFrame.

Examples

DataFrame.reindex supports two calling conventions

  • (index=index_labels, columns=column_labels, ...)

  • (labels, axis={'index', 'columns'}, ...)

We highly recommend using keyword arguments to clarify your intent.

Create a dataframe with some fictional data.

>>> index = ['Firefox', 'Chrome', 'Safari', 'IE10', 'Konqueror']
>>> df = pd.DataFrame({'http_status': [200, 200, 404, 404, 301],
...                   'response_time': [0.04, 0.02, 0.07, 0.08, 1.0]},
...                   index=index)
>>> df
           http_status  response_time
Firefox            200           0.04
Chrome             200           0.02
Safari             404           0.07
IE10               404           0.08
Konqueror          301           1.00

Create a new index and reindex the dataframe. By default values in the new index that do not have corresponding records in the dataframe are assigned NaN.

>>> new_index = ['Safari', 'Iceweasel', 'Comodo Dragon', 'IE10',
...              'Chrome']
>>> df.reindex(new_index)
               http_status  response_time
Safari               404.0           0.07
Iceweasel              NaN            NaN
Comodo Dragon          NaN            NaN
IE10                 404.0           0.08
Chrome               200.0           0.02

We can fill in the missing values by passing a value to the keyword fill_value. Because the index is not monotonically increasing or decreasing, we cannot use arguments to the keyword method to fill the NaN values.

>>> df.reindex(new_index, fill_value=0)
               http_status  response_time
Safari                 404           0.07
Iceweasel                0           0.00
Comodo Dragon            0           0.00
IE10                   404           0.08
Chrome                 200           0.02
>>> df.reindex(new_index, fill_value='missing')
              http_status response_time
Safari                404          0.07
Iceweasel         missing       missing
Comodo Dragon     missing       missing
IE10                  404          0.08
Chrome                200          0.02

We can also reindex the columns.

>>> df.reindex(columns=['http_status', 'user_agent'])
           http_status  user_agent
Firefox            200         NaN
Chrome             200         NaN
Safari             404         NaN
IE10               404         NaN
Konqueror          301         NaN

Or we can use “axis-style” keyword arguments

>>> df.reindex(['http_status', 'user_agent'], axis="columns")
           http_status  user_agent
Firefox            200         NaN
Chrome             200         NaN
Safari             404         NaN
IE10               404         NaN
Konqueror          301         NaN

To further illustrate the filling functionality in reindex, we will create a dataframe with a monotonically increasing index (for example, a sequence of dates).

>>> date_index = pd.date_range('1/1/2010', periods=6, freq='D')
>>> df2 = pd.DataFrame({"prices": [100, 101, np.nan, 100, 89, 88]},
...                    index=date_index)
>>> df2
            prices
2010-01-01   100.0
2010-01-02   101.0
2010-01-03     NaN
2010-01-04   100.0
2010-01-05    89.0
2010-01-06    88.0

Suppose we decide to expand the dataframe to cover a wider date range.

>>> date_index2 = pd.date_range('12/29/2009', periods=10, freq='D')
>>> df2.reindex(date_index2)
            prices
2009-12-29     NaN
2009-12-30     NaN
2009-12-31     NaN
2010-01-01   100.0
2010-01-02   101.0
2010-01-03     NaN
2010-01-04   100.0
2010-01-05    89.0
2010-01-06    88.0
2010-01-07     NaN

The index entries that did not have a value in the original data frame (for example, ‘2009-12-29’) are by default filled with NaN. If desired, we can fill in the missing values using one of several options.

For example, to back-propagate the last valid value to fill the NaN values, pass bfill as an argument to the method keyword.

>>> df2.reindex(date_index2, method='bfill')
            prices
2009-12-29   100.0
2009-12-30   100.0
2009-12-31   100.0
2010-01-01   100.0
2010-01-02   101.0
2010-01-03     NaN
2010-01-04   100.0
2010-01-05    89.0
2010-01-06    88.0
2010-01-07     NaN

Please note that the NaN value present in the original dataframe (at index value 2010-01-03) will not be filled by any of the value propagation schemes. This is because filling while reindexing does not look at dataframe values, but only compares the original and desired indexes. If you do want to fill in the NaN values present in the original dataframe, use the fillna() method.

See the user guide for more.

reindex_like(other, method: Optional[str] = None, copy: bool = True, limit=None, tolerance=None) → FrameOrSeries

Return an object with matching indices as other object.

Conform the object to the same index on all axes. Optional filling logic, placing NaN in locations having no value in the previous index. A new object is produced unless the new index is equivalent to the current one and copy=False.

Parameters
  • other (Object of the same data type) – Its row and column indices are used to define the new indices of this object.

  • method ({None, 'backfill'/'bfill', 'pad'/'ffill', 'nearest'}) –

    Method to use for filling holes in reindexed DataFrame. Please note: this is only applicable to DataFrames/Series with a monotonically increasing/decreasing index.

    • None (default): don’t fill gaps

    • pad / ffill: propagate last valid observation forward to next valid

    • backfill / bfill: use next valid observation to fill gap

    • nearest: use nearest valid observations to fill gap.

  • copy (bool, default True) – Return a new object, even if the passed indexes are the same.

  • limit (int, default None) – Maximum number of consecutive labels to fill for inexact matches.

  • tolerance (optional) –

    Maximum distance between original and new labels for inexact matches. The values of the index at the matching locations most satisfy the equation abs(index[indexer] - target) <= tolerance.

    Tolerance may be a scalar value, which applies the same tolerance to all values, or list-like, which applies variable tolerance per element. List-like includes list, tuple, array, Series, and must be the same size as the index and its dtype must exactly match the index’s type.

Returns

Same type as caller, but with changed indices on each axis.

Return type

Series or DataFrame

See also

DataFrame.set_index()

Set row labels.

DataFrame.reset_index()

Remove row labels or move them to new columns.

DataFrame.reindex()

Change to new indices or expand indices.

Notes

Same as calling .reindex(index=other.index, columns=other.columns,...).

Examples

>>> df1 = pd.DataFrame([[24.3, 75.7, 'high'],
...                     [31, 87.8, 'high'],
...                     [22, 71.6, 'medium'],
...                     [35, 95, 'medium']],
...                    columns=['temp_celsius', 'temp_fahrenheit',
...                             'windspeed'],
...                    index=pd.date_range(start='2014-02-12',
...                                        end='2014-02-15', freq='D'))
>>> df1
            temp_celsius  temp_fahrenheit windspeed
2014-02-12          24.3             75.7      high
2014-02-13          31.0             87.8      high
2014-02-14          22.0             71.6    medium
2014-02-15          35.0             95.0    medium
>>> df2 = pd.DataFrame([[28, 'low'],
...                     [30, 'low'],
...                     [35.1, 'medium']],
...                    columns=['temp_celsius', 'windspeed'],
...                    index=pd.DatetimeIndex(['2014-02-12', '2014-02-13',
...                                            '2014-02-15']))
>>> df2
            temp_celsius windspeed
2014-02-12          28.0       low
2014-02-13          30.0       low
2014-02-15          35.1    medium
>>> df2.reindex_like(df1)
            temp_celsius  temp_fahrenheit windspeed
2014-02-12          28.0              NaN       low
2014-02-13          30.0              NaN       low
2014-02-14           NaN              NaN       NaN
2014-02-15          35.1              NaN    medium
rename(mapper=None, index=None, columns=None, axis=None, copy=True, inplace=False, level=None, errors='ignore') → Optional[pandas.core.frame.DataFrame]

Alter axes labels.

Function / dict values must be unique (1-to-1). Labels not contained in a dict / Series will be left as-is. Extra labels listed don’t throw an error.

See the user guide for more.

Parameters
  • mapper (dict-like or function) – Dict-like or functions transformations to apply to that axis’ values. Use either mapper and axis to specify the axis to target with mapper, or index and columns.

  • index (dict-like or function) – Alternative to specifying axis (mapper, axis=0 is equivalent to index=mapper).

  • columns (dict-like or function) – Alternative to specifying axis (mapper, axis=1 is equivalent to columns=mapper).

  • axis ({0 or 'index', 1 or 'columns'}, default 0) – Axis to target with mapper. Can be either the axis name (‘index’, ‘columns’) or number (0, 1). The default is ‘index’.

  • copy (bool, default True) – Also copy underlying data.

  • inplace (bool, default False) – Whether to return a new DataFrame. If True then value of copy is ignored.

  • level (int or level name, default None) – In case of a MultiIndex, only rename labels in the specified level.

  • errors ({'ignore', 'raise'}, default 'ignore') – If ‘raise’, raise a KeyError when a dict-like mapper, index, or columns contains labels that are not present in the Index being transformed. If ‘ignore’, existing keys will be renamed and extra keys will be ignored.

Returns

DataFrame with the renamed axis labels.

Return type

DataFrame

Raises

KeyError – If any of the labels is not found in the selected axis and “errors=’raise’”.

See also

DataFrame.rename_axis()

Set the name of the axis.

Examples

DataFrame.rename supports two calling conventions

  • (index=index_mapper, columns=columns_mapper, ...)

  • (mapper, axis={'index', 'columns'}, ...)

We highly recommend using keyword arguments to clarify your intent.

Rename columns using a mapping:

>>> df = pd.DataFrame({"A": [1, 2, 3], "B": [4, 5, 6]})
>>> df.rename(columns={"A": "a", "B": "c"})
   a  c
0  1  4
1  2  5
2  3  6

Rename index using a mapping:

>>> df.rename(index={0: "x", 1: "y", 2: "z"})
   A  B
x  1  4
y  2  5
z  3  6

Cast index labels to a different type:

>>> df.index
RangeIndex(start=0, stop=3, step=1)
>>> df.rename(index=str).index
Index(['0', '1', '2'], dtype='object')
>>> df.rename(columns={"A": "a", "B": "b", "C": "c"}, errors="raise")
Traceback (most recent call last):
KeyError: ['C'] not found in axis

Using axis-style parameters

>>> df.rename(str.lower, axis='columns')
   a  b
0  1  4
1  2  5
2  3  6
>>> df.rename({1: 2, 2: 4}, axis='index')
   A  B
0  1  4
2  2  5
4  3  6
rename_axis(mapper=None, index=None, columns=None, axis=None, copy=True, inplace=False)

Set the name of the axis for the index or columns.

Parameters
  • mapper (scalar, list-like, optional) – Value to set the axis name attribute.

  • columns (index,) –

    A scalar, list-like, dict-like or functions transformations to apply to that axis’ values. Note that the columns parameter is not allowed if the object is a Series. This parameter only apply for DataFrame type objects.

    Use either mapper and axis to specify the axis to target with mapper, or index and/or columns.

    Changed in version 0.24.0.

  • axis ({0 or 'index', 1 or 'columns'}, default 0) – The axis to rename.

  • copy (bool, default True) – Also copy underlying data.

  • inplace (bool, default False) – Modifies the object directly, instead of creating a new Series or DataFrame.

Returns

The same type as the caller or None if inplace is True.

Return type

Series, DataFrame, or None

See also

Series.rename()

Alter Series index labels or name.

DataFrame.rename()

Alter DataFrame index labels or name.

Index.rename()

Set new names on index.

Notes

DataFrame.rename_axis supports two calling conventions

  • (index=index_mapper, columns=columns_mapper, ...)

  • (mapper, axis={'index', 'columns'}, ...)

The first calling convention will only modify the names of the index and/or the names of the Index object that is the columns. In this case, the parameter copy is ignored.

The second calling convention will modify the names of the the corresponding index if mapper is a list or a scalar. However, if mapper is dict-like or a function, it will use the deprecated behavior of modifying the axis labels.

We highly recommend using keyword arguments to clarify your intent.

Examples

Series

>>> s = pd.Series(["dog", "cat", "monkey"])
>>> s
0       dog
1       cat
2    monkey
dtype: object
>>> s.rename_axis("animal")
animal
0    dog
1    cat
2    monkey
dtype: object

DataFrame

>>> df = pd.DataFrame({"num_legs": [4, 4, 2],
...                    "num_arms": [0, 0, 2]},
...                   ["dog", "cat", "monkey"])
>>> df
        num_legs  num_arms
dog            4         0
cat            4         0
monkey         2         2
>>> df = df.rename_axis("animal")
>>> df
        num_legs  num_arms
animal
dog            4         0
cat            4         0
monkey         2         2
>>> df = df.rename_axis("limbs", axis="columns")
>>> df
limbs   num_legs  num_arms
animal
dog            4         0
cat            4         0
monkey         2         2

MultiIndex

>>> df.index = pd.MultiIndex.from_product([['mammal'],
...                                        ['dog', 'cat', 'monkey']],
...                                       names=['type', 'name'])
>>> df
limbs          num_legs  num_arms
type   name
mammal dog            4         0
       cat            4         0
       monkey         2         2
>>> df.rename_axis(index={'type': 'class'})
limbs          num_legs  num_arms
class  name
mammal dog            4         0
       cat            4         0
       monkey         2         2
>>> df.rename_axis(columns=str.upper)
LIMBS          num_legs  num_arms
type   name
mammal dog            4         0
       cat            4         0
       monkey         2         2
reorder_levels(order, axis=0) → pandas.core.frame.DataFrame

Rearrange index levels using input order. May not drop or duplicate levels.

Parameters
  • order (list of int or list of str) – List representing new level order. Reference level by number (position) or by key (label).

  • axis ({0 or 'index', 1 or 'columns'}, default 0) – Where to reorder levels.

Returns

Return type

DataFrame

replace(to_replace=None, value=None, inplace=False, limit=None, regex=False, method='pad')

Replace values given in to_replace with value.

Values of the DataFrame are replaced with other values dynamically. This differs from updating with .loc or .iloc, which require you to specify a location to update with some value.

Parameters
  • to_replace (str, regex, list, dict, Series, int, float, or None) –

    How to find the values that will be replaced.

    • numeric, str or regex:

      • numeric: numeric values equal to to_replace will be replaced with value

      • str: string exactly matching to_replace will be replaced with value

      • regex: regexs matching to_replace will be replaced with value

    • list of str, regex, or numeric:

      • First, if to_replace and value are both lists, they must be the same length.

      • Second, if regex=True then all of the strings in both lists will be interpreted as regexs otherwise they will match directly. This doesn’t matter much for value since there are only a few possible substitution regexes you can use.

      • str, regex and numeric rules apply as above.

    • dict:

      • Dicts can be used to specify different replacement values for different existing values. For example, {'a': 'b', 'y': 'z'} replaces the value ‘a’ with ‘b’ and ‘y’ with ‘z’. To use a dict in this way the value parameter should be None.

      • For a DataFrame a dict can specify that different values should be replaced in different columns. For example, {'a': 1, 'b': 'z'} looks for the value 1 in column ‘a’ and the value ‘z’ in column ‘b’ and replaces these values with whatever is specified in value. The value parameter should not be None in this case. You can treat this as a special case of passing two lists except that you are specifying the column to search in.

      • For a DataFrame nested dictionaries, e.g., {'a': {'b': np.nan}}, are read as follows: look in column ‘a’ for the value ‘b’ and replace it with NaN. The value parameter should be None to use a nested dict in this way. You can nest regular expressions as well. Note that column names (the top-level dictionary keys in a nested dictionary) cannot be regular expressions.

    • None:

      • This means that the regex argument must be a string, compiled regular expression, or list, dict, ndarray or Series of such elements. If value is also None then this must be a nested dictionary or Series.

    See the examples section for examples of each of these.

  • value (scalar, dict, list, str, regex, default None) – Value to replace any values matching to_replace with. For a DataFrame a dict of values can be used to specify which value to use for each column (columns not in the dict will not be filled). Regular expressions, strings and lists or dicts of such objects are also allowed.

  • inplace (bool, default False) – If True, in place. Note: this will modify any other views on this object (e.g. a column from a DataFrame). Returns the caller if this is True.

  • limit (int, default None) – Maximum size gap to forward or backward fill.

  • regex (bool or same types as to_replace, default False) – Whether to interpret to_replace and/or value as regular expressions. If this is True then to_replace must be a string. Alternatively, this could be a regular expression or a list, dict, or array of regular expressions in which case to_replace must be None.

  • method ({‘pad’, ‘ffill’, ‘bfill’, None}) –

    The method to use when for replacement, when to_replace is a scalar, list or tuple and value is None.

    Changed in version 0.23.0: Added to DataFrame.

Returns

Object after replacement.

Return type

DataFrame

Raises
  • AssertionError

    • If regex is not a bool and to_replace is not None.

  • TypeError

    • If to_replace is not a scalar, array-like, dict, or None * If to_replace is a dict and value is not a list, dict, ndarray, or Series * If to_replace is None and regex is not compilable into a regular expression or is a list, dict, ndarray, or Series. * When replacing multiple bool or datetime64 objects and the arguments to to_replace does not match the type of the value being replaced

  • ValueError

    • If a list or an ndarray is passed to to_replace and value but they are not the same length.

See also

DataFrame.fillna()

Fill NA values.

DataFrame.where()

Replace values based on boolean condition.

Series.str.replace()

Simple string replacement.

Notes

  • Regex substitution is performed under the hood with re.sub. The rules for substitution for re.sub are the same.

  • Regular expressions will only substitute on strings, meaning you cannot provide, for example, a regular expression matching floating point numbers and expect the columns in your frame that have a numeric dtype to be matched. However, if those floating point numbers are strings, then you can do this.

  • This method has a lot of options. You are encouraged to experiment and play with this method to gain intuition about how it works.

  • When dict is used as the to_replace value, it is like key(s) in the dict are the to_replace part and value(s) in the dict are the value parameter.

Examples

Scalar `to_replace` and `value`

>>> s = pd.Series([0, 1, 2, 3, 4])
>>> s.replace(0, 5)
0    5
1    1
2    2
3    3
4    4
dtype: int64
>>> df = pd.DataFrame({'A': [0, 1, 2, 3, 4],
...                    'B': [5, 6, 7, 8, 9],
...                    'C': ['a', 'b', 'c', 'd', 'e']})
>>> df.replace(0, 5)
   A  B  C
0  5  5  a
1  1  6  b
2  2  7  c
3  3  8  d
4  4  9  e

List-like `to_replace`

>>> df.replace([0, 1, 2, 3], 4)
   A  B  C
0  4  5  a
1  4  6  b
2  4  7  c
3  4  8  d
4  4  9  e
>>> df.replace([0, 1, 2, 3], [4, 3, 2, 1])
   A  B  C
0  4  5  a
1  3  6  b
2  2  7  c
3  1  8  d
4  4  9  e
>>> s.replace([1, 2], method='bfill')
0    0
1    3
2    3
3    3
4    4
dtype: int64

dict-like `to_replace`

>>> df.replace({0: 10, 1: 100})
     A  B  C
0   10  5  a
1  100  6  b
2    2  7  c
3    3  8  d
4    4  9  e
>>> df.replace({'A': 0, 'B': 5}, 100)
     A    B  C
0  100  100  a
1    1    6  b
2    2    7  c
3    3    8  d
4    4    9  e
>>> df.replace({'A': {0: 100, 4: 400}})
     A  B  C
0  100  5  a
1    1  6  b
2    2  7  c
3    3  8  d
4  400  9  e

Regular expression `to_replace`

>>> df = pd.DataFrame({'A': ['bat', 'foo', 'bait'],
...                    'B': ['abc', 'bar', 'xyz']})
>>> df.replace(to_replace=r'^ba.$', value='new', regex=True)
      A    B
0   new  abc
1   foo  new
2  bait  xyz
>>> df.replace({'A': r'^ba.$'}, {'A': 'new'}, regex=True)
      A    B
0   new  abc
1   foo  bar
2  bait  xyz
>>> df.replace(regex=r'^ba.$', value='new')
      A    B
0   new  abc
1   foo  new
2  bait  xyz
>>> df.replace(regex={r'^ba.$': 'new', 'foo': 'xyz'})
      A    B
0   new  abc
1   xyz  new
2  bait  xyz
>>> df.replace(regex=[r'^ba.$', 'foo'], value='new')
      A    B
0   new  abc
1   new  new
2  bait  xyz

Note that when replacing multiple bool or datetime64 objects, the data types in the to_replace parameter must match the data type of the value being replaced:

>>> df = pd.DataFrame({'A': [True, False, True],
...                    'B': [False, True, False]})
>>> df.replace({'a string': 'new value', True: False})  # raises
Traceback (most recent call last):
    ...
TypeError: Cannot compare types 'ndarray(dtype=bool)' and 'str'

This raises a TypeError because one of the dict keys is not of the correct type for replacement.

Compare the behavior of s.replace({'a': None}) and s.replace('a', None) to understand the peculiarities of the to_replace parameter:

>>> s = pd.Series([10, 'a', 'a', 'b', 'a'])

When one uses a dict as the to_replace value, it is like the value(s) in the dict are equal to the value parameter. s.replace({'a': None}) is equivalent to s.replace(to_replace={'a': None}, value=None, method=None):

>>> s.replace({'a': None})
0      10
1    None
2    None
3       b
4    None
dtype: object

When value=None and to_replace is a scalar, list or tuple, replace uses the method parameter (default ‘pad’) to do the replacement. So this is why the ‘a’ values are being replaced by 10 in rows 1 and 2 and ‘b’ in row 4 in this case. The command s.replace('a', None) is actually equivalent to s.replace(to_replace='a', value=None, method='pad'):

>>> s.replace('a', None)
0    10
1    10
2    10
3     b
4     b
dtype: object
resample(rule, axis=0, closed: Optional[str] = None, label: Optional[str] = None, convention: str = 'start', kind: Optional[str] = None, loffset=None, base: Optional[int] = None, on=None, level=None, origin: Union[str, Timestamp, datetime.datetime, numpy.datetime64, int, numpy.int64, float] = 'start_day', offset: Union[Timedelta, datetime.timedelta, numpy.timedelta64, int, numpy.int64, float, str, None] = None) → Resampler

Resample time-series data.

Convenience method for frequency conversion and resampling of time series. Object must have a datetime-like index (DatetimeIndex, PeriodIndex, or TimedeltaIndex), or pass datetime-like values to the on or level keyword.

Parameters
  • rule (DateOffset, Timedelta or str) – The offset string or object representing target conversion.

  • axis ({0 or 'index', 1 or 'columns'}, default 0) – Which axis to use for up- or down-sampling. For Series this will default to 0, i.e. along the rows. Must be DatetimeIndex, TimedeltaIndex or PeriodIndex.

  • closed ({'right', 'left'}, default None) – Which side of bin interval is closed. The default is ‘left’ for all frequency offsets except for ‘M’, ‘A’, ‘Q’, ‘BM’, ‘BA’, ‘BQ’, and ‘W’ which all have a default of ‘right’.

  • label ({'right', 'left'}, default None) – Which bin edge label to label bucket with. The default is ‘left’ for all frequency offsets except for ‘M’, ‘A’, ‘Q’, ‘BM’, ‘BA’, ‘BQ’, and ‘W’ which all have a default of ‘right’.

  • convention ({'start', 'end', 's', 'e'}, default 'start') – For PeriodIndex only, controls whether to use the start or end of rule.

  • kind ({'timestamp', 'period'}, optional, default None) – Pass ‘timestamp’ to convert the resulting index to a DateTimeIndex or ‘period’ to convert it to a PeriodIndex. By default the input representation is retained.

  • loffset (timedelta, default None) –

    Adjust the resampled time labels.

    Deprecated since version 1.1.0: You should add the loffset to the df.index after the resample. See below.

  • base (int, default 0) –

    For frequencies that evenly subdivide 1 day, the “origin” of the aggregated intervals. For example, for ‘5min’ frequency, base could range from 0 through 4. Defaults to 0.

    Deprecated since version 1.1.0: The new arguments that you should use are ‘offset’ or ‘origin’.

  • on (str, optional) – For a DataFrame, column to use instead of index for resampling. Column must be datetime-like.

  • level (str or int, optional) – For a MultiIndex, level (name or number) to use for resampling. level must be datetime-like.

  • origin ({'epoch', 'start', 'start_day'}, Timestamp or str, default 'start_day') –

    The timestamp on which to adjust the grouping. The timezone of origin must match the timezone of the index. If a timestamp is not used, these values are also supported:

    • ’epoch’: origin is 1970-01-01

    • ’start’: origin is the first value of the timeseries

    • ’start_day’: origin is the first day at midnight of the timeseries

    New in version 1.1.0.

  • offset (Timedelta or str, default is None) –

    An offset timedelta added to the origin.

    New in version 1.1.0.

Returns

Return type

Resampler object

See also

groupby()

Group by mapping, function, label, or list of labels.

Series.resample()

Resample a Series.

DataFrame.resample()

Resample a DataFrame.

Notes

See the user guide for more.

To learn more about the offset strings, please see this link.

Examples

Start by creating a series with 9 one minute timestamps.

>>> index = pd.date_range('1/1/2000', periods=9, freq='T')
>>> series = pd.Series(range(9), index=index)
>>> series
2000-01-01 00:00:00    0
2000-01-01 00:01:00    1
2000-01-01 00:02:00    2
2000-01-01 00:03:00    3
2000-01-01 00:04:00    4
2000-01-01 00:05:00    5
2000-01-01 00:06:00    6
2000-01-01 00:07:00    7
2000-01-01 00:08:00    8
Freq: T, dtype: int64

Downsample the series into 3 minute bins and sum the values of the timestamps falling into a bin.

>>> series.resample('3T').sum()
2000-01-01 00:00:00     3
2000-01-01 00:03:00    12
2000-01-01 00:06:00    21
Freq: 3T, dtype: int64

Downsample the series into 3 minute bins as above, but label each bin using the right edge instead of the left. Please note that the value in the bucket used as the label is not included in the bucket, which it labels. For example, in the original series the bucket 2000-01-01 00:03:00 contains the value 3, but the summed value in the resampled bucket with the label 2000-01-01 00:03:00 does not include 3 (if it did, the summed value would be 6, not 3). To include this value close the right side of the bin interval as illustrated in the example below this one.

>>> series.resample('3T', label='right').sum()
2000-01-01 00:03:00     3
2000-01-01 00:06:00    12
2000-01-01 00:09:00    21
Freq: 3T, dtype: int64

Downsample the series into 3 minute bins as above, but close the right side of the bin interval.

>>> series.resample('3T', label='right', closed='right').sum()
2000-01-01 00:00:00     0
2000-01-01 00:03:00     6
2000-01-01 00:06:00    15
2000-01-01 00:09:00    15
Freq: 3T, dtype: int64

Upsample the series into 30 second bins.

>>> series.resample('30S').asfreq()[0:5]   # Select first 5 rows
2000-01-01 00:00:00   0.0
2000-01-01 00:00:30   NaN
2000-01-01 00:01:00   1.0
2000-01-01 00:01:30   NaN
2000-01-01 00:02:00   2.0
Freq: 30S, dtype: float64

Upsample the series into 30 second bins and fill the NaN values using the pad method.

>>> series.resample('30S').pad()[0:5]
2000-01-01 00:00:00    0
2000-01-01 00:00:30    0
2000-01-01 00:01:00    1
2000-01-01 00:01:30    1
2000-01-01 00:02:00    2
Freq: 30S, dtype: int64

Upsample the series into 30 second bins and fill the NaN values using the bfill method.

>>> series.resample('30S').bfill()[0:5]
2000-01-01 00:00:00    0
2000-01-01 00:00:30    1
2000-01-01 00:01:00    1
2000-01-01 00:01:30    2
2000-01-01 00:02:00    2
Freq: 30S, dtype: int64

Pass a custom function via apply

>>> def custom_resampler(array_like):
...     return np.sum(array_like) + 5
...
>>> series.resample('3T').apply(custom_resampler)
2000-01-01 00:00:00     8
2000-01-01 00:03:00    17
2000-01-01 00:06:00    26
Freq: 3T, dtype: int64

For a Series with a PeriodIndex, the keyword convention can be used to control whether to use the start or end of rule.

Resample a year by quarter using ‘start’ convention. Values are assigned to the first quarter of the period.

>>> s = pd.Series([1, 2], index=pd.period_range('2012-01-01',
...                                             freq='A',
...                                             periods=2))
>>> s
2012    1
2013    2
Freq: A-DEC, dtype: int64
>>> s.resample('Q', convention='start').asfreq()
2012Q1    1.0
2012Q2    NaN
2012Q3    NaN
2012Q4    NaN
2013Q1    2.0
2013Q2    NaN
2013Q3    NaN
2013Q4    NaN
Freq: Q-DEC, dtype: float64

Resample quarters by month using ‘end’ convention. Values are assigned to the last month of the period.

>>> q = pd.Series([1, 2, 3, 4], index=pd.period_range('2018-01-01',
...                                                   freq='Q',
...                                                   periods=4))
>>> q
2018Q1    1
2018Q2    2
2018Q3    3
2018Q4    4
Freq: Q-DEC, dtype: int64
>>> q.resample('M', convention='end').asfreq()
2018-03    1.0
2018-04    NaN
2018-05    NaN
2018-06    2.0
2018-07    NaN
2018-08    NaN
2018-09    3.0
2018-10    NaN
2018-11    NaN
2018-12    4.0
Freq: M, dtype: float64

For DataFrame objects, the keyword on can be used to specify the column instead of the index for resampling.

>>> d = dict({'price': [10, 11, 9, 13, 14, 18, 17, 19],
...           'volume': [50, 60, 40, 100, 50, 100, 40, 50]})
>>> df = pd.DataFrame(d)
>>> df['week_starting'] = pd.date_range('01/01/2018',
...                                     periods=8,
...                                     freq='W')
>>> df
   price  volume week_starting
0     10      50    2018-01-07
1     11      60    2018-01-14
2      9      40    2018-01-21
3     13     100    2018-01-28
4     14      50    2018-02-04
5     18     100    2018-02-11
6     17      40    2018-02-18
7     19      50    2018-02-25
>>> df.resample('M', on='week_starting').mean()
               price  volume
week_starting
2018-01-31     10.75    62.5
2018-02-28     17.00    60.0

For a DataFrame with MultiIndex, the keyword level can be used to specify on which level the resampling needs to take place.

>>> days = pd.date_range('1/1/2000', periods=4, freq='D')
>>> d2 = dict({'price': [10, 11, 9, 13, 14, 18, 17, 19],
...            'volume': [50, 60, 40, 100, 50, 100, 40, 50]})
>>> df2 = pd.DataFrame(d2,
...                    index=pd.MultiIndex.from_product([days,
...                                                     ['morning',
...                                                      'afternoon']]
...                                                     ))
>>> df2
                      price  volume
2000-01-01 morning       10      50
           afternoon     11      60
2000-01-02 morning        9      40
           afternoon     13     100
2000-01-03 morning       14      50
           afternoon     18     100
2000-01-04 morning       17      40
           afternoon     19      50
>>> df2.resample('D', level=0).sum()
            price  volume
2000-01-01     21     110
2000-01-02     22     140
2000-01-03     32     150
2000-01-04     36      90

If you want to adjust the start of the bins based on a fixed timestamp:

>>> start, end = '2000-10-01 23:30:00', '2000-10-02 00:30:00'
>>> rng = pd.date_range(start, end, freq='7min')
>>> ts = pd.Series(np.arange(len(rng)) * 3, index=rng)
>>> ts
2000-10-01 23:30:00     0
2000-10-01 23:37:00     3
2000-10-01 23:44:00     6
2000-10-01 23:51:00     9
2000-10-01 23:58:00    12
2000-10-02 00:05:00    15
2000-10-02 00:12:00    18
2000-10-02 00:19:00    21
2000-10-02 00:26:00    24
Freq: 7T, dtype: int64
>>> ts.resample('17min').sum()
2000-10-01 23:14:00     0
2000-10-01 23:31:00     9
2000-10-01 23:48:00    21
2000-10-02 00:05:00    54
2000-10-02 00:22:00    24
Freq: 17T, dtype: int64
>>> ts.resample('17min', origin='epoch').sum()
2000-10-01 23:18:00     0
2000-10-01 23:35:00    18
2000-10-01 23:52:00    27
2000-10-02 00:09:00    39
2000-10-02 00:26:00    24
Freq: 17T, dtype: int64
>>> ts.resample('17min', origin='2000-01-01').sum()
2000-10-01 23:24:00     3
2000-10-01 23:41:00    15
2000-10-01 23:58:00    45
2000-10-02 00:15:00    45
Freq: 17T, dtype: int64

If you want to adjust the start of the bins with an offset Timedelta, the two following lines are equivalent:

>>> ts.resample('17min', origin='start').sum()
2000-10-01 23:30:00     9
2000-10-01 23:47:00    21
2000-10-02 00:04:00    54
2000-10-02 00:21:00    24
Freq: 17T, dtype: int64
>>> ts.resample('17min', offset='23h30min').sum()
2000-10-01 23:30:00     9
2000-10-01 23:47:00    21
2000-10-02 00:04:00    54
2000-10-02 00:21:00    24
Freq: 17T, dtype: int64

To replace the use of the deprecated base argument, you can now use offset, in this example it is equivalent to have base=2:

>>> ts.resample('17min', offset='2min').sum()
2000-10-01 23:16:00     0
2000-10-01 23:33:00     9
2000-10-01 23:50:00    36
2000-10-02 00:07:00    39
2000-10-02 00:24:00    24
Freq: 17T, dtype: int64

To replace the use of the deprecated loffset argument:

>>> from pandas.tseries.frequencies import to_offset
>>> loffset = '19min'
>>> ts_out = ts.resample('17min').sum()
>>> ts_out.index = ts_out.index + to_offset(loffset)
>>> ts_out
2000-10-01 23:33:00     0
2000-10-01 23:50:00     9
2000-10-02 00:07:00    21
2000-10-02 00:24:00    54
2000-10-02 00:41:00    24
Freq: 17T, dtype: int64
reset_index(level: Union[Hashable, Sequence[Hashable], None] = None, drop: bool = False, inplace: bool = False, col_level: Hashable = 0, col_fill: Optional[Hashable] = '') → Optional[pandas.core.frame.DataFrame]

Reset the index, or a level of it.

Reset the index of the DataFrame, and use the default one instead. If the DataFrame has a MultiIndex, this method can remove one or more levels.

Parameters
  • level (int, str, tuple, or list, default None) – Only remove the given levels from the index. Removes all levels by default.

  • drop (bool, default False) – Do not try to insert index into dataframe columns. This resets the index to the default integer index.

  • inplace (bool, default False) – Modify the DataFrame in place (do not create a new object).

  • col_level (int or str, default 0) – If the columns have multiple levels, determines which level the labels are inserted into. By default it is inserted into the first level.

  • col_fill (object, default '') – If the columns have multiple levels, determines how the other levels are named. If None then the index name is repeated.

Returns

DataFrame with the new index or None if inplace=True.

Return type

DataFrame or None

See also

DataFrame.set_index()

Opposite of reset_index.

DataFrame.reindex()

Change to new indices or expand indices.

DataFrame.reindex_like()

Change to same indices as other DataFrame.

Examples

>>> df = pd.DataFrame([('bird', 389.0),
...                    ('bird', 24.0),
...                    ('mammal', 80.5),
...                    ('mammal', np.nan)],
...                   index=['falcon', 'parrot', 'lion', 'monkey'],
...                   columns=('class', 'max_speed'))
>>> df
         class  max_speed
falcon    bird      389.0
parrot    bird       24.0
lion    mammal       80.5
monkey  mammal        NaN

When we reset the index, the old index is added as a column, and a new sequential index is used:

>>> df.reset_index()
    index   class  max_speed
0  falcon    bird      389.0
1  parrot    bird       24.0
2    lion  mammal       80.5
3  monkey  mammal        NaN

We can use the drop parameter to avoid the old index being added as a column:

>>> df.reset_index(drop=True)
    class  max_speed
0    bird      389.0
1    bird       24.0
2  mammal       80.5
3  mammal        NaN

You can also use reset_index with MultiIndex.

>>> index = pd.MultiIndex.from_tuples([('bird', 'falcon'),
...                                    ('bird', 'parrot'),
...                                    ('mammal', 'lion'),
...                                    ('mammal', 'monkey')],
...                                   names=['class', 'name'])
>>> columns = pd.MultiIndex.from_tuples([('speed', 'max'),
...                                      ('species', 'type')])
>>> df = pd.DataFrame([(389.0, 'fly'),
...                    ( 24.0, 'fly'),
...                    ( 80.5, 'run'),
...                    (np.nan, 'jump')],
...                   index=index,
...                   columns=columns)
>>> df
               speed species
                 max    type
class  name
bird   falcon  389.0     fly
       parrot   24.0     fly
mammal lion     80.5     run
       monkey    NaN    jump

If the index has multiple levels, we can reset a subset of them:

>>> df.reset_index(level='class')
         class  speed species
                  max    type
name
falcon    bird  389.0     fly
parrot    bird   24.0     fly
lion    mammal   80.5     run
monkey  mammal    NaN    jump

If we are not dropping the index, by default, it is placed in the top level. We can place it in another level:

>>> df.reset_index(level='class', col_level=1)
                speed species
         class    max    type
name
falcon    bird  389.0     fly
parrot    bird   24.0     fly
lion    mammal   80.5     run
monkey  mammal    NaN    jump

When the index is inserted under another level, we can specify under which one with the parameter col_fill:

>>> df.reset_index(level='class', col_level=1, col_fill='species')
              species  speed species
                class    max    type
name
falcon           bird  389.0     fly
parrot           bird   24.0     fly
lion           mammal   80.5     run
monkey         mammal    NaN    jump

If we specify a nonexistent level for col_fill, it is created:

>>> df.reset_index(level='class', col_level=1, col_fill='genus')
                genus  speed species
                class    max    type
name
falcon           bird  389.0     fly
parrot           bird   24.0     fly
lion           mammal   80.5     run
monkey         mammal    NaN    jump
rfloordiv(other, axis='columns', level=None, fill_value=None)

Get Integer division of dataframe and other, element-wise (binary operator rfloordiv).

Equivalent to other // dataframe, but with support to substitute a fill_value for missing data in one of the inputs. With reverse version, floordiv.

Among flexible wrappers (add, sub, mul, div, mod, pow) to arithmetic operators: +, -, *, /, //, %, **.

Parameters
  • other (scalar, sequence, Series, or DataFrame) – Any single or multiple element data structure, or list-like object.

  • axis ({0 or 'index', 1 or 'columns'}) – Whether to compare by the index (0 or ‘index’) or columns (1 or ‘columns’). For Series input, axis to match Series index on.

  • level (int or label) – Broadcast across a level, matching Index values on the passed MultiIndex level.

  • fill_value (float or None, default None) – Fill existing missing (NaN) values, and any new element needed for successful DataFrame alignment, with this value before computation. If data in both corresponding DataFrame locations is missing the result will be missing.

Returns

Result of the arithmetic operation.

Return type

DataFrame

See also

DataFrame.add()

Add DataFrames.

DataFrame.sub()

Subtract DataFrames.

DataFrame.mul()

Multiply DataFrames.

DataFrame.div()

Divide DataFrames (float division).

DataFrame.truediv()

Divide DataFrames (float division).

DataFrame.floordiv()

Divide DataFrames (integer division).

DataFrame.mod()

Calculate modulo (remainder after division).

DataFrame.pow()

Calculate exponential power.

Notes

Mismatched indices will be unioned together.

Examples

>>> df = pd.DataFrame({'angles': [0, 3, 4],
...                    'degrees': [360, 180, 360]},
...                   index=['circle', 'triangle', 'rectangle'])
>>> df
           angles  degrees
circle          0      360
triangle        3      180
rectangle       4      360

Add a scalar with operator version which return the same results.

>>> df + 1
           angles  degrees
circle          1      361
triangle        4      181
rectangle       5      361
>>> df.add(1)
           angles  degrees
circle          1      361
triangle        4      181
rectangle       5      361

Divide by constant with reverse version.

>>> df.div(10)
           angles  degrees
circle        0.0     36.0
triangle      0.3     18.0
rectangle     0.4     36.0
>>> df.rdiv(10)
             angles   degrees
circle          inf  0.027778
triangle   3.333333  0.055556
rectangle  2.500000  0.027778

Subtract a list and Series by axis with operator version.

>>> df - [1, 2]
           angles  degrees
circle         -1      358
triangle        2      178
rectangle       3      358
>>> df.sub([1, 2], axis='columns')
           angles  degrees
circle         -1      358
triangle        2      178
rectangle       3      358
>>> df.sub(pd.Series([1, 1, 1], index=['circle', 'triangle', 'rectangle']),
...        axis='index')
           angles  degrees
circle         -1      359
triangle        2      179
rectangle       3      359

Multiply a DataFrame of different shape with operator version.

>>> other = pd.DataFrame({'angles': [0, 3, 4]},
...                      index=['circle', 'triangle', 'rectangle'])
>>> other
           angles
circle          0
triangle        3
rectangle       4
>>> df * other
           angles  degrees
circle          0      NaN
triangle        9      NaN
rectangle      16      NaN
>>> df.mul(other, fill_value=0)
           angles  degrees
circle          0      0.0
triangle        9      0.0
rectangle      16      0.0

Divide by a MultiIndex by level.

>>> df_multindex = pd.DataFrame({'angles': [0, 3, 4, 4, 5, 6],
...                              'degrees': [360, 180, 360, 360, 540, 720]},
...                             index=[['A', 'A', 'A', 'B', 'B', 'B'],
...                                    ['circle', 'triangle', 'rectangle',
...                                     'square', 'pentagon', 'hexagon']])
>>> df_multindex
             angles  degrees
A circle          0      360
  triangle        3      180
  rectangle       4      360
B square          4      360
  pentagon        5      540
  hexagon         6      720
>>> df.div(df_multindex, level=1, fill_value=0)
             angles  degrees
A circle        NaN      1.0
  triangle      1.0      1.0
  rectangle     1.0      1.0
B square        0.0      0.0
  pentagon      0.0      0.0
  hexagon       0.0      0.0
rmod(other, axis='columns', level=None, fill_value=None)

Get Modulo of dataframe and other, element-wise (binary operator rmod).

Equivalent to other % dataframe, but with support to substitute a fill_value for missing data in one of the inputs. With reverse version, mod.

Among flexible wrappers (add, sub, mul, div, mod, pow) to arithmetic operators: +, -, *, /, //, %, **.

Parameters
  • other (scalar, sequence, Series, or DataFrame) – Any single or multiple element data structure, or list-like object.

  • axis ({0 or 'index', 1 or 'columns'}) – Whether to compare by the index (0 or ‘index’) or columns (1 or ‘columns’). For Series input, axis to match Series index on.

  • level (int or label) – Broadcast across a level, matching Index values on the passed MultiIndex level.

  • fill_value (float or None, default None) – Fill existing missing (NaN) values, and any new element needed for successful DataFrame alignment, with this value before computation. If data in both corresponding DataFrame locations is missing the result will be missing.

Returns

Result of the arithmetic operation.

Return type

DataFrame

See also

DataFrame.add()

Add DataFrames.

DataFrame.sub()

Subtract DataFrames.

DataFrame.mul()

Multiply DataFrames.

DataFrame.div()

Divide DataFrames (float division).

DataFrame.truediv()

Divide DataFrames (float division).

DataFrame.floordiv()

Divide DataFrames (integer division).

DataFrame.mod()

Calculate modulo (remainder after division).

DataFrame.pow()

Calculate exponential power.

Notes

Mismatched indices will be unioned together.

Examples

>>> df = pd.DataFrame({'angles': [0, 3, 4],
...                    'degrees': [360, 180, 360]},
...                   index=['circle', 'triangle', 'rectangle'])
>>> df
           angles  degrees
circle          0      360
triangle        3      180
rectangle       4      360

Add a scalar with operator version which return the same results.

>>> df + 1
           angles  degrees
circle          1      361
triangle        4      181
rectangle       5      361
>>> df.add(1)
           angles  degrees
circle          1      361
triangle        4      181
rectangle       5      361

Divide by constant with reverse version.

>>> df.div(10)
           angles  degrees
circle        0.0     36.0
triangle      0.3     18.0
rectangle     0.4     36.0
>>> df.rdiv(10)
             angles   degrees
circle          inf  0.027778
triangle   3.333333  0.055556
rectangle  2.500000  0.027778

Subtract a list and Series by axis with operator version.

>>> df - [1, 2]
           angles  degrees
circle         -1      358
triangle        2      178
rectangle       3      358
>>> df.sub([1, 2], axis='columns')
           angles  degrees
circle         -1      358
triangle        2      178
rectangle       3      358
>>> df.sub(pd.Series([1, 1, 1], index=['circle', 'triangle', 'rectangle']),
...        axis='index')
           angles  degrees
circle         -1      359
triangle        2      179
rectangle       3      359

Multiply a DataFrame of different shape with operator version.

>>> other = pd.DataFrame({'angles': [0, 3, 4]},
...                      index=['circle', 'triangle', 'rectangle'])
>>> other
           angles
circle          0
triangle        3
rectangle       4
>>> df * other
           angles  degrees
circle          0      NaN
triangle        9      NaN
rectangle      16      NaN
>>> df.mul(other, fill_value=0)
           angles  degrees
circle          0      0.0
triangle        9      0.0
rectangle      16      0.0

Divide by a MultiIndex by level.

>>> df_multindex = pd.DataFrame({'angles': [0, 3, 4, 4, 5, 6],
...                              'degrees': [360, 180, 360, 360, 540, 720]},
...                             index=[['A', 'A', 'A', 'B', 'B', 'B'],
...                                    ['circle', 'triangle', 'rectangle',
...                                     'square', 'pentagon', 'hexagon']])
>>> df_multindex
             angles  degrees
A circle          0      360
  triangle        3      180
  rectangle       4      360
B square          4      360
  pentagon        5      540
  hexagon         6      720
>>> df.div(df_multindex, level=1, fill_value=0)
             angles  degrees
A circle        NaN      1.0
  triangle      1.0      1.0
  rectangle     1.0      1.0
B square        0.0      0.0
  pentagon      0.0      0.0
  hexagon       0.0      0.0
rmul(other, axis='columns', level=None, fill_value=None)

Get Multiplication of dataframe and other, element-wise (binary operator rmul).

Equivalent to other * dataframe, but with support to substitute a fill_value for missing data in one of the inputs. With reverse version, mul.

Among flexible wrappers (add, sub, mul, div, mod, pow) to arithmetic operators: +, -, *, /, //, %, **.

Parameters
  • other (scalar, sequence, Series, or DataFrame) – Any single or multiple element data structure, or list-like object.

  • axis ({0 or 'index', 1 or 'columns'}) – Whether to compare by the index (0 or ‘index’) or columns (1 or ‘columns’). For Series input, axis to match Series index on.

  • level (int or label) – Broadcast across a level, matching Index values on the passed MultiIndex level.

  • fill_value (float or None, default None) – Fill existing missing (NaN) values, and any new element needed for successful DataFrame alignment, with this value before computation. If data in both corresponding DataFrame locations is missing the result will be missing.

Returns

Result of the arithmetic operation.

Return type

DataFrame

See also

DataFrame.add()

Add DataFrames.

DataFrame.sub()

Subtract DataFrames.

DataFrame.mul()

Multiply DataFrames.

DataFrame.div()

Divide DataFrames (float division).

DataFrame.truediv()

Divide DataFrames (float division).

DataFrame.floordiv()

Divide DataFrames (integer division).

DataFrame.mod()

Calculate modulo (remainder after division).

DataFrame.pow()

Calculate exponential power.

Notes

Mismatched indices will be unioned together.

Examples

>>> df = pd.DataFrame({'angles': [0, 3, 4],
...                    'degrees': [360, 180, 360]},
...                   index=['circle', 'triangle', 'rectangle'])
>>> df
           angles  degrees
circle          0      360
triangle        3      180
rectangle       4      360

Add a scalar with operator version which return the same results.

>>> df + 1
           angles  degrees
circle          1      361
triangle        4      181
rectangle       5      361
>>> df.add(1)
           angles  degrees
circle          1      361
triangle        4      181
rectangle       5      361

Divide by constant with reverse version.

>>> df.div(10)
           angles  degrees
circle        0.0     36.0
triangle      0.3     18.0
rectangle     0.4     36.0
>>> df.rdiv(10)
             angles   degrees
circle          inf  0.027778
triangle   3.333333  0.055556
rectangle  2.500000  0.027778

Subtract a list and Series by axis with operator version.

>>> df - [1, 2]
           angles  degrees
circle         -1      358
triangle        2      178
rectangle       3      358
>>> df.sub([1, 2], axis='columns')
           angles  degrees
circle         -1      358
triangle        2      178
rectangle       3      358
>>> df.sub(pd.Series([1, 1, 1], index=['circle', 'triangle', 'rectangle']),
...        axis='index')
           angles  degrees
circle         -1      359
triangle        2      179
rectangle       3      359

Multiply a DataFrame of different shape with operator version.

>>> other = pd.DataFrame({'angles': [0, 3, 4]},
...                      index=['circle', 'triangle', 'rectangle'])
>>> other
           angles
circle          0
triangle        3
rectangle       4
>>> df * other
           angles  degrees
circle          0      NaN
triangle        9      NaN
rectangle      16      NaN
>>> df.mul(other, fill_value=0)
           angles  degrees
circle          0      0.0
triangle        9      0.0
rectangle      16      0.0

Divide by a MultiIndex by level.

>>> df_multindex = pd.DataFrame({'angles': [0, 3, 4, 4, 5, 6],
...                              'degrees': [360, 180, 360, 360, 540, 720]},
...                             index=[['A', 'A', 'A', 'B', 'B', 'B'],
...                                    ['circle', 'triangle', 'rectangle',
...                                     'square', 'pentagon', 'hexagon']])
>>> df_multindex
             angles  degrees
A circle          0      360
  triangle        3      180
  rectangle       4      360
B square          4      360
  pentagon        5      540
  hexagon         6      720
>>> df.div(df_multindex, level=1, fill_value=0)
             angles  degrees
A circle        NaN      1.0
  triangle      1.0      1.0
  rectangle     1.0      1.0
B square        0.0      0.0
  pentagon      0.0      0.0
  hexagon       0.0      0.0
rolling(window, min_periods=None, center=False, win_type=None, on=None, axis=0, closed=None)

Provide rolling window calculations.

Parameters
  • window (int, offset, or BaseIndexer subclass) –

    Size of the moving window. This is the number of observations used for calculating the statistic. Each window will be a fixed size.

    If its an offset then this will be the time period of each window. Each window will be a variable sized based on the observations included in the time-period. This is only valid for datetimelike indexes.

    If a BaseIndexer subclass is passed, calculates the window boundaries based on the defined get_window_bounds method. Additional rolling keyword arguments, namely min_periods, center, and closed will be passed to get_window_bounds.

  • min_periods (int, default None) – Minimum number of observations in window required to have a value (otherwise result is NA). For a window that is specified by an offset, min_periods will default to 1. Otherwise, min_periods will default to the size of the window.

  • center (bool, default False) – Set the labels at the center of the window.

  • win_type (str, default None) – Provide a window type. If None, all points are evenly weighted. See the notes below for further information.

  • on (str, optional) – For a DataFrame, a datetime-like column or MultiIndex level on which to calculate the rolling window, rather than the DataFrame’s index. Provided integer column is ignored and excluded from result since an integer index is not used to calculate the rolling window.

  • axis (int or str, default 0) –

  • closed (str, default None) – Make the interval closed on the ‘right’, ‘left’, ‘both’ or ‘neither’ endpoints. For offset-based windows, it defaults to ‘right’. For fixed windows, defaults to ‘both’. Remaining cases not implemented for fixed windows.

Returns

Return type

a Window or Rolling sub-classed for the particular operation

See also

expanding()

Provides expanding transformations.

ewm()

Provides exponential weighted functions.

Notes

By default, the result is set to the right edge of the window. This can be changed to the center of the window by setting center=True.

To learn more about the offsets & frequency strings, please see this link.

The recognized win_types are:

  • boxcar

  • triang

  • blackman

  • hamming

  • bartlett

  • parzen

  • bohman

  • blackmanharris

  • nuttall

  • barthann

  • kaiser (needs parameter: beta)

  • gaussian (needs parameter: std)

  • general_gaussian (needs parameters: power, width)

  • slepian (needs parameter: width)

  • exponential (needs parameter: tau), center is set to None.

If win_type=None all points are evenly weighted. To learn more about different window types see scipy.signal window functions.

Certain window types require additional parameters to be passed. Please see the third example below on how to add the additional parameters.

Examples

>>> df = pd.DataFrame({'B': [0, 1, 2, np.nan, 4]})
>>> df
     B
0  0.0
1  1.0
2  2.0
3  NaN
4  4.0

Rolling sum with a window length of 2, using the ‘triang’ window type.

>>> df.rolling(2, win_type='triang').sum()
     B
0  NaN
1  0.5
2  1.5
3  NaN
4  NaN

Rolling sum with a window length of 2, using the ‘gaussian’ window type (note how we need to specify std).

>>> df.rolling(2, win_type='gaussian').sum(std=3)
          B
0       NaN
1  0.986207
2  2.958621
3       NaN
4       NaN

Rolling sum with a window length of 2, min_periods defaults to the window length.

>>> df.rolling(2).sum()
     B
0  NaN
1  1.0
2  3.0
3  NaN
4  NaN

Same as above, but explicitly set the min_periods

>>> df.rolling(2, min_periods=1).sum()
     B
0  0.0
1  1.0
2  3.0
3  2.0
4  4.0

Same as above, but with forward-looking windows

>>> indexer = pd.api.indexers.FixedForwardWindowIndexer(window_size=2)
>>> df.rolling(window=indexer, min_periods=1).sum()
     B
0  1.0
1  3.0
2  2.0
3  4.0
4  4.0

A ragged (meaning not-a-regular frequency), time-indexed DataFrame

>>> df = pd.DataFrame({'B': [0, 1, 2, np.nan, 4]},
...                   index = [pd.Timestamp('20130101 09:00:00'),
...                            pd.Timestamp('20130101 09:00:02'),
...                            pd.Timestamp('20130101 09:00:03'),
...                            pd.Timestamp('20130101 09:00:05'),
...                            pd.Timestamp('20130101 09:00:06')])
>>> df
                       B
2013-01-01 09:00:00  0.0
2013-01-01 09:00:02  1.0
2013-01-01 09:00:03  2.0
2013-01-01 09:00:05  NaN
2013-01-01 09:00:06  4.0

Contrasting to an integer rolling window, this will roll a variable length window corresponding to the time period. The default for min_periods is 1.

>>> df.rolling('2s').sum()
                       B
2013-01-01 09:00:00  0.0
2013-01-01 09:00:02  1.0
2013-01-01 09:00:03  3.0
2013-01-01 09:00:05  NaN
2013-01-01 09:00:06  4.0
round(decimals=0, *args, **kwargs) → pandas.core.frame.DataFrame

Round a DataFrame to a variable number of decimal places.

Parameters
  • decimals (int, dict, Series) – Number of decimal places to round each column to. If an int is given, round each column to the same number of places. Otherwise dict and Series round to variable numbers of places. Column names should be in the keys if decimals is a dict-like, or in the index if decimals is a Series. Any columns not included in decimals will be left as is. Elements of decimals which are not columns of the input will be ignored.

  • *args – Additional keywords have no effect but might be accepted for compatibility with numpy.

  • **kwargs – Additional keywords have no effect but might be accepted for compatibility with numpy.

Returns

A DataFrame with the affected columns rounded to the specified number of decimal places.

Return type

DataFrame

See also

numpy.around()

Round a numpy array to the given number of decimals.

Series.round()

Round a Series to the given number of decimals.

Examples

>>> df = pd.DataFrame([(.21, .32), (.01, .67), (.66, .03), (.21, .18)],
...                   columns=['dogs', 'cats'])
>>> df
    dogs  cats
0  0.21  0.32
1  0.01  0.67
2  0.66  0.03
3  0.21  0.18

By providing an integer each column is rounded to the same number of decimal places

>>> df.round(1)
    dogs  cats
0   0.2   0.3
1   0.0   0.7
2   0.7   0.0
3   0.2   0.2

With a dict, the number of places for specific columns can be specified with the column names as key and the number of decimal places as value

>>> df.round({'dogs': 1, 'cats': 0})
    dogs  cats
0   0.2   0.0
1   0.0   1.0
2   0.7   0.0
3   0.2   0.0

Using a Series, the number of places for specific columns can be specified with the column names as index and the number of decimal places as value

>>> decimals = pd.Series([0, 1], index=['cats', 'dogs'])
>>> df.round(decimals)
    dogs  cats
0   0.2   0.0
1   0.0   1.0
2   0.7   0.0
3   0.2   0.0
rpow(other, axis='columns', level=None, fill_value=None)

Get Exponential power of dataframe and other, element-wise (binary operator rpow).

Equivalent to other ** dataframe, but with support to substitute a fill_value for missing data in one of the inputs. With reverse version, pow.

Among flexible wrappers (add, sub, mul, div, mod, pow) to arithmetic operators: +, -, *, /, //, %, **.

Parameters
  • other (scalar, sequence, Series, or DataFrame) – Any single or multiple element data structure, or list-like object.

  • axis ({0 or 'index', 1 or 'columns'}) – Whether to compare by the index (0 or ‘index’) or columns (1 or ‘columns’). For Series input, axis to match Series index on.

  • level (int or label) – Broadcast across a level, matching Index values on the passed MultiIndex level.

  • fill_value (float or None, default None) – Fill existing missing (NaN) values, and any new element needed for successful DataFrame alignment, with this value before computation. If data in both corresponding DataFrame locations is missing the result will be missing.

Returns

Result of the arithmetic operation.

Return type

DataFrame

See also

DataFrame.add()

Add DataFrames.

DataFrame.sub()

Subtract DataFrames.

DataFrame.mul()

Multiply DataFrames.

DataFrame.div()

Divide DataFrames (float division).

DataFrame.truediv()

Divide DataFrames (float division).

DataFrame.floordiv()

Divide DataFrames (integer division).

DataFrame.mod()

Calculate modulo (remainder after division).

DataFrame.pow()

Calculate exponential power.

Notes

Mismatched indices will be unioned together.

Examples

>>> df = pd.DataFrame({'angles': [0, 3, 4],
...                    'degrees': [360, 180, 360]},
...                   index=['circle', 'triangle', 'rectangle'])
>>> df
           angles  degrees
circle          0      360
triangle        3      180
rectangle       4      360

Add a scalar with operator version which return the same results.

>>> df + 1
           angles  degrees
circle          1      361
triangle        4      181
rectangle       5      361
>>> df.add(1)
           angles  degrees
circle          1      361
triangle        4      181
rectangle       5      361

Divide by constant with reverse version.

>>> df.div(10)
           angles  degrees
circle        0.0     36.0
triangle      0.3     18.0
rectangle     0.4     36.0
>>> df.rdiv(10)
             angles   degrees
circle          inf  0.027778
triangle   3.333333  0.055556
rectangle  2.500000  0.027778

Subtract a list and Series by axis with operator version.

>>> df - [1, 2]
           angles  degrees
circle         -1      358
triangle        2      178
rectangle       3      358
>>> df.sub([1, 2], axis='columns')
           angles  degrees
circle         -1      358
triangle        2      178
rectangle       3      358
>>> df.sub(pd.Series([1, 1, 1], index=['circle', 'triangle', 'rectangle']),
...        axis='index')
           angles  degrees
circle         -1      359
triangle        2      179
rectangle       3      359

Multiply a DataFrame of different shape with operator version.

>>> other = pd.DataFrame({'angles': [0, 3, 4]},
...                      index=['circle', 'triangle', 'rectangle'])
>>> other
           angles
circle          0
triangle        3
rectangle       4
>>> df * other
           angles  degrees
circle          0      NaN
triangle        9      NaN
rectangle      16      NaN
>>> df.mul(other, fill_value=0)
           angles  degrees
circle          0      0.0
triangle        9      0.0
rectangle      16      0.0

Divide by a MultiIndex by level.

>>> df_multindex = pd.DataFrame({'angles': [0, 3, 4, 4, 5, 6],
...                              'degrees': [360, 180, 360, 360, 540, 720]},
...                             index=[['A', 'A', 'A', 'B', 'B', 'B'],
...                                    ['circle', 'triangle', 'rectangle',
...                                     'square', 'pentagon', 'hexagon']])
>>> df_multindex
             angles  degrees
A circle          0      360
  triangle        3      180
  rectangle       4      360
B square          4      360
  pentagon        5      540
  hexagon         6      720
>>> df.div(df_multindex, level=1, fill_value=0)
             angles  degrees
A circle        NaN      1.0
  triangle      1.0      1.0
  rectangle     1.0      1.0
B square        0.0      0.0
  pentagon      0.0      0.0
  hexagon       0.0      0.0
rsub(other, axis='columns', level=None, fill_value=None)

Get Subtraction of dataframe and other, element-wise (binary operator rsub).

Equivalent to other - dataframe, but with support to substitute a fill_value for missing data in one of the inputs. With reverse version, sub.

Among flexible wrappers (add, sub, mul, div, mod, pow) to arithmetic operators: +, -, *, /, //, %, **.

Parameters
  • other (scalar, sequence, Series, or DataFrame) – Any single or multiple element data structure, or list-like object.

  • axis ({0 or 'index', 1 or 'columns'}) – Whether to compare by the index (0 or ‘index’) or columns (1 or ‘columns’). For Series input, axis to match Series index on.

  • level (int or label) – Broadcast across a level, matching Index values on the passed MultiIndex level.

  • fill_value (float or None, default None) – Fill existing missing (NaN) values, and any new element needed for successful DataFrame alignment, with this value before computation. If data in both corresponding DataFrame locations is missing the result will be missing.

Returns

Result of the arithmetic operation.

Return type

DataFrame

See also

DataFrame.add()

Add DataFrames.

DataFrame.sub()

Subtract DataFrames.

DataFrame.mul()

Multiply DataFrames.

DataFrame.div()

Divide DataFrames (float division).

DataFrame.truediv()

Divide DataFrames (float division).

DataFrame.floordiv()

Divide DataFrames (integer division).

DataFrame.mod()

Calculate modulo (remainder after division).

DataFrame.pow()

Calculate exponential power.

Notes

Mismatched indices will be unioned together.

Examples

>>> df = pd.DataFrame({'angles': [0, 3, 4],
...                    'degrees': [360, 180, 360]},
...                   index=['circle', 'triangle', 'rectangle'])
>>> df
           angles  degrees
circle          0      360
triangle        3      180
rectangle       4      360

Add a scalar with operator version which return the same results.

>>> df + 1
           angles  degrees
circle          1      361
triangle        4      181
rectangle       5      361
>>> df.add(1)
           angles  degrees
circle          1      361
triangle        4      181
rectangle       5      361

Divide by constant with reverse version.

>>> df.div(10)
           angles  degrees
circle        0.0     36.0
triangle      0.3     18.0
rectangle     0.4     36.0
>>> df.rdiv(10)
             angles   degrees
circle          inf  0.027778
triangle   3.333333  0.055556
rectangle  2.500000  0.027778

Subtract a list and Series by axis with operator version.

>>> df - [1, 2]
           angles  degrees
circle         -1      358
triangle        2      178
rectangle       3      358
>>> df.sub([1, 2], axis='columns')
           angles  degrees
circle         -1      358
triangle        2      178
rectangle       3      358
>>> df.sub(pd.Series([1, 1, 1], index=['circle', 'triangle', 'rectangle']),
...        axis='index')
           angles  degrees
circle         -1      359
triangle        2      179
rectangle       3      359

Multiply a DataFrame of different shape with operator version.

>>> other = pd.DataFrame({'angles': [0, 3, 4]},
...                      index=['circle', 'triangle', 'rectangle'])
>>> other
           angles
circle          0
triangle        3
rectangle       4
>>> df * other
           angles  degrees
circle          0      NaN
triangle        9      NaN
rectangle      16      NaN
>>> df.mul(other, fill_value=0)
           angles  degrees
circle          0      0.0
triangle        9      0.0
rectangle      16      0.0

Divide by a MultiIndex by level.

>>> df_multindex = pd.DataFrame({'angles': [0, 3, 4, 4, 5, 6],
...                              'degrees': [360, 180, 360, 360, 540, 720]},
...                             index=[['A', 'A', 'A', 'B', 'B', 'B'],
...                                    ['circle', 'triangle', 'rectangle',
...                                     'square', 'pentagon', 'hexagon']])
>>> df_multindex
             angles  degrees
A circle          0      360
  triangle        3      180
  rectangle       4      360
B square          4      360
  pentagon        5      540
  hexagon         6      720
>>> df.div(df_multindex, level=1, fill_value=0)
             angles  degrees
A circle        NaN      1.0
  triangle      1.0      1.0
  rectangle     1.0      1.0
B square        0.0      0.0
  pentagon      0.0      0.0
  hexagon       0.0      0.0
rtruediv(other, axis='columns', level=None, fill_value=None)

Get Floating division of dataframe and other, element-wise (binary operator rtruediv).

Equivalent to other / dataframe, but with support to substitute a fill_value for missing data in one of the inputs. With reverse version, truediv.

Among flexible wrappers (add, sub, mul, div, mod, pow) to arithmetic operators: +, -, *, /, //, %, **.

Parameters
  • other (scalar, sequence, Series, or DataFrame) – Any single or multiple element data structure, or list-like object.

  • axis ({0 or 'index', 1 or 'columns'}) – Whether to compare by the index (0 or ‘index’) or columns (1 or ‘columns’). For Series input, axis to match Series index on.

  • level (int or label) – Broadcast across a level, matching Index values on the passed MultiIndex level.

  • fill_value (float or None, default None) – Fill existing missing (NaN) values, and any new element needed for successful DataFrame alignment, with this value before computation. If data in both corresponding DataFrame locations is missing the result will be missing.

Returns

Result of the arithmetic operation.

Return type

DataFrame

See also

DataFrame.add()

Add DataFrames.

DataFrame.sub()

Subtract DataFrames.

DataFrame.mul()

Multiply DataFrames.

DataFrame.div()

Divide DataFrames (float division).

DataFrame.truediv()

Divide DataFrames (float division).

DataFrame.floordiv()

Divide DataFrames (integer division).

DataFrame.mod()

Calculate modulo (remainder after division).

DataFrame.pow()

Calculate exponential power.

Notes

Mismatched indices will be unioned together.

Examples

>>> df = pd.DataFrame({'angles': [0, 3, 4],
...                    'degrees': [360, 180, 360]},
...                   index=['circle', 'triangle', 'rectangle'])
>>> df
           angles  degrees
circle          0      360
triangle        3      180
rectangle       4      360

Add a scalar with operator version which return the same results.

>>> df + 1
           angles  degrees
circle          1      361
triangle        4      181
rectangle       5      361
>>> df.add(1)
           angles  degrees
circle          1      361
triangle        4      181
rectangle       5      361

Divide by constant with reverse version.

>>> df.div(10)
           angles  degrees
circle        0.0     36.0
triangle      0.3     18.0
rectangle     0.4     36.0
>>> df.rdiv(10)
             angles   degrees
circle          inf  0.027778
triangle   3.333333  0.055556
rectangle  2.500000  0.027778

Subtract a list and Series by axis with operator version.

>>> df - [1, 2]
           angles  degrees
circle         -1      358
triangle        2      178
rectangle       3      358
>>> df.sub([1, 2], axis='columns')
           angles  degrees
circle         -1      358
triangle        2      178
rectangle       3      358
>>> df.sub(pd.Series([1, 1, 1], index=['circle', 'triangle', 'rectangle']),
...        axis='index')
           angles  degrees
circle         -1      359
triangle        2      179
rectangle       3      359

Multiply a DataFrame of different shape with operator version.

>>> other = pd.DataFrame({'angles': [0, 3, 4]},
...                      index=['circle', 'triangle', 'rectangle'])
>>> other
           angles
circle          0
triangle        3
rectangle       4
>>> df * other
           angles  degrees
circle          0      NaN
triangle        9      NaN
rectangle      16      NaN
>>> df.mul(other, fill_value=0)
           angles  degrees
circle          0      0.0
triangle        9      0.0
rectangle      16      0.0

Divide by a MultiIndex by level.

>>> df_multindex = pd.DataFrame({'angles': [0, 3, 4, 4, 5, 6],
...                              'degrees': [360, 180, 360, 360, 540, 720]},
...                             index=[['A', 'A', 'A', 'B', 'B', 'B'],
...                                    ['circle', 'triangle', 'rectangle',
...                                     'square', 'pentagon', 'hexagon']])
>>> df_multindex
             angles  degrees
A circle          0      360
  triangle        3      180
  rectangle       4      360
B square          4      360
  pentagon        5      540
  hexagon         6      720
>>> df.div(df_multindex, level=1, fill_value=0)
             angles  degrees
A circle        NaN      1.0
  triangle      1.0      1.0
  rectangle     1.0      1.0
B square        0.0      0.0
  pentagon      0.0      0.0
  hexagon       0.0      0.0
sample(n=None, frac=None, replace=False, weights=None, random_state=None, axis=None) → FrameOrSeries

Return a random sample of items from an axis of object.

You can use random_state for reproducibility.

Parameters
  • n (int, optional) – Number of items from axis to return. Cannot be used with frac. Default = 1 if frac = None.

  • frac (float, optional) – Fraction of axis items to return. Cannot be used with n.

  • replace (bool, default False) – Allow or disallow sampling of the same row more than once.

  • weights (str or ndarray-like, optional) – Default ‘None’ results in equal probability weighting. If passed a Series, will align with target object on index. Index values in weights not found in sampled object will be ignored and index values in sampled object not in weights will be assigned weights of zero. If called on a DataFrame, will accept the name of a column when axis = 0. Unless weights are a Series, weights must be same length as axis being sampled. If weights do not sum to 1, they will be normalized to sum to 1. Missing values in the weights column will be treated as zero. Infinite values not allowed.

  • random_state (int, array-like, BitGenerator, np.random.RandomState, optional) –

    If int, array-like, or BitGenerator (NumPy>=1.17), seed for random number generator If np.random.RandomState, use as numpy RandomState object.

    Changed in version 1.1.0: array-like and BitGenerator (for NumPy>=1.17) object now passed to np.random.RandomState() as seed

  • axis ({0 or ‘index’, 1 or ‘columns’, None}, default None) – Axis to sample. Accepts axis number or name. Default is stat axis for given data type (0 for Series and DataFrames).

Returns

A new object of same type as caller containing n items randomly sampled from the caller object.

Return type

Series or DataFrame

See also

DataFrameGroupBy.sample()

Generates random samples from each group of a DataFrame object.

SeriesGroupBy.sample()

Generates random samples from each group of a Series object.

numpy.random.choice()

Generates a random sample from a given 1-D numpy array.

Notes

If frac > 1, replacement should be set to True.

Examples

>>> df = pd.DataFrame({'num_legs': [2, 4, 8, 0],
...                    'num_wings': [2, 0, 0, 0],
...                    'num_specimen_seen': [10, 2, 1, 8]},
...                   index=['falcon', 'dog', 'spider', 'fish'])
>>> df
        num_legs  num_wings  num_specimen_seen
falcon         2          2                 10
dog            4          0                  2
spider         8          0                  1
fish           0          0                  8

Extract 3 random elements from the Series df['num_legs']: Note that we use random_state to ensure the reproducibility of the examples.

>>> df['num_legs'].sample(n=3, random_state=1)
fish      0
spider    8
falcon    2
Name: num_legs, dtype: int64

A random 50% sample of the DataFrame with replacement:

>>> df.sample(frac=0.5, replace=True, random_state=1)
      num_legs  num_wings  num_specimen_seen
dog          4          0                  2
fish         0          0                  8

An upsample sample of the DataFrame with replacement: Note that replace parameter has to be True for frac parameter > 1.

>>> df.sample(frac=2, replace=True, random_state=1)
        num_legs  num_wings  num_specimen_seen
dog            4          0                  2
fish           0          0                  8
falcon         2          2                 10
falcon         2          2                 10
fish           0          0                  8
dog            4          0                  2
fish           0          0                  8
dog            4          0                  2

Using a DataFrame column as weights. Rows with larger value in the num_specimen_seen column are more likely to be sampled.

>>> df.sample(n=2, weights='num_specimen_seen', random_state=1)
        num_legs  num_wings  num_specimen_seen
falcon         2          2                 10
fish           0          0                  8
select_dtypes(include=None, exclude=None) → pandas.core.frame.DataFrame

Return a subset of the DataFrame’s columns based on the column dtypes.

Parameters

exclude (include,) – A selection of dtypes or strings to be included/excluded. At least one of these parameters must be supplied.

Returns

The subset of the frame including the dtypes in include and excluding the dtypes in exclude.

Return type

DataFrame

Raises

ValueError

  • If both of include and exclude are empty * If include and exclude have overlapping elements * If any kind of string dtype is passed in.

See also

DataFrame.dtypes()

Return Series with the data type of each column.

Notes

  • To select all numeric types, use np.number or 'number'

  • To select strings you must use the object dtype, but note that this will return all object dtype columns

  • See the numpy dtype hierarchy

  • To select datetimes, use np.datetime64, 'datetime' or 'datetime64'

  • To select timedeltas, use np.timedelta64, 'timedelta' or 'timedelta64'

  • To select Pandas categorical dtypes, use 'category'

  • To select Pandas datetimetz dtypes, use 'datetimetz' (new in 0.20.0) or 'datetime64[ns, tz]'

Examples

>>> df = pd.DataFrame({'a': [1, 2] * 3,
...                    'b': [True, False] * 3,
...                    'c': [1.0, 2.0] * 3})
>>> df
        a      b  c
0       1   True  1.0
1       2  False  2.0
2       1   True  1.0
3       2  False  2.0
4       1   True  1.0
5       2  False  2.0
>>> df.select_dtypes(include='bool')
   b
0  True
1  False
2  True
3  False
4  True
5  False
>>> df.select_dtypes(include=['float64'])
   c
0  1.0
1  2.0
2  1.0
3  2.0
4  1.0
5  2.0
>>> df.select_dtypes(exclude=['int64'])
       b    c
0   True  1.0
1  False  2.0
2   True  1.0
3  False  2.0
4   True  1.0
5  False  2.0
sem(axis=None, skipna=None, level=None, ddof=1, numeric_only=None, **kwargs)

Return unbiased standard error of the mean over requested axis.

Normalized by N-1 by default. This can be changed using the ddof argument

Parameters
  • axis ({index (0), columns (1)}) –

  • skipna (bool, default True) – Exclude NA/null values. If an entire row/column is NA, the result will be NA.

  • level (int or level name, default None) – If the axis is a MultiIndex (hierarchical), count along a particular level, collapsing into a Series.

  • ddof (int, default 1) – Delta Degrees of Freedom. The divisor used in calculations is N - ddof, where N represents the number of elements.

  • numeric_only (bool, default None) – Include only float, int, boolean columns. If None, will attempt to use everything, then use only numeric data. Not implemented for Series.

Returns

Return type

Series or DataFrame (if level specified)

set_axis(labels, axis: Union[str, int] = 0, inplace: bool = False)

Assign desired index to given axis.

Indexes for column or row labels can be changed by assigning a list-like or Index.

Parameters
  • labels (list-like, Index) – The values for the new index.

  • axis ({0 or 'index', 1 or 'columns'}, default 0) – The axis to update. The value 0 identifies the rows, and 1 identifies the columns.

  • inplace (bool, default False) – Whether to return a new DataFrame instance.

Returns

renamed – An object of type DataFrame if inplace=False, None otherwise.

Return type

DataFrame or None

See also

DataFrame.rename_axis()

Alter the name of the index or columns. Examples ——– >>> df = pd.DataFrame({“A”: [1, 2, 3], “B”: [4, 5, 6]}) Change the row labels. >>> df.set_axis([‘a’, ‘b’, ‘c’], axis=’index’) A B a 1 4 b 2 5 c 3 6 Change the column labels. >>> df.set_axis([‘I’, ‘II’], axis=’columns’) I II 0 1 4 1 2 5 2 3 6 Now, update the labels inplace. >>> df.set_axis([‘i’, ‘ii’], axis=’columns’, inplace=True) >>> df i ii 0 1 4 1 2 5 2 3 6

set_index(keys, drop=True, append=False, inplace=False, verify_integrity=False)

Set the DataFrame index using existing columns.

Set the DataFrame index (row labels) using one or more existing columns or arrays (of the correct length). The index can replace the existing index or expand on it.

Parameters
  • keys (label or array-like or list of labels/arrays) – This parameter can be either a single column key, a single array of the same length as the calling DataFrame, or a list containing an arbitrary combination of column keys and arrays. Here, “array” encompasses Series, Index, np.ndarray, and instances of Iterator.

  • drop (bool, default True) – Delete columns to be used as the new index.

  • append (bool, default False) – Whether to append columns to existing index.

  • inplace (bool, default False) – Modify the DataFrame in place (do not create a new object).

  • verify_integrity (bool, default False) – Check the new index for duplicates. Otherwise defer the check until necessary. Setting to False will improve the performance of this method.

Returns

Changed row labels.

Return type

DataFrame

See also

DataFrame.reset_index()

Opposite of set_index.

DataFrame.reindex()

Change to new indices or expand indices.

DataFrame.reindex_like()

Change to same indices as other DataFrame.

Examples

>>> df = pd.DataFrame({'month': [1, 4, 7, 10],
...                    'year': [2012, 2014, 2013, 2014],
...                    'sale': [55, 40, 84, 31]})
>>> df
   month  year  sale
0      1  2012    55
1      4  2014    40
2      7  2013    84
3     10  2014    31

Set the index to become the ‘month’ column:

>>> df.set_index('month')
       year  sale
month
1      2012    55
4      2014    40
7      2013    84
10     2014    31

Create a MultiIndex using columns ‘year’ and ‘month’:

>>> df.set_index(['year', 'month'])
            sale
year  month
2012  1     55
2014  4     40
2013  7     84
2014  10    31

Create a MultiIndex using an Index and a column:

>>> df.set_index([pd.Index([1, 2, 3, 4]), 'year'])
         month  sale
   year
1  2012  1      55
2  2014  4      40
3  2013  7      84
4  2014  10     31

Create a MultiIndex using two Series:

>>> s = pd.Series([1, 2, 3, 4])
>>> df.set_index([s, s**2])
      month  year  sale
1 1       1  2012    55
2 4       4  2014    40
3 9       7  2013    84
4 16     10  2014    31
property shape

Return a tuple representing the dimensionality of the DataFrame.

See also

ndarray.shape

Examples

>>> df = pd.DataFrame({'col1': [1, 2], 'col2': [3, 4]})
>>> df.shape
(2, 2)
>>> df = pd.DataFrame({'col1': [1, 2], 'col2': [3, 4],
...                    'col3': [5, 6]})
>>> df.shape
(2, 3)
shift(periods=1, freq=None, axis=0, fill_value=None) → pandas.core.frame.DataFrame

Shift index by desired number of periods with an optional time freq.

When freq is not passed, shift the index without realigning the data. If freq is passed (in this case, the index must be date or datetime, or it will raise a NotImplementedError), the index will be increased using the periods and the freq. freq can be inferred when specified as “infer” as long as either freq or inferred_freq attribute is set in the index.

Parameters
  • periods (int) – Number of periods to shift. Can be positive or negative.

  • freq (DateOffset, tseries.offsets, timedelta, or str, optional) – Offset to use from the tseries module or time rule (e.g. ‘EOM’). If freq is specified then the index values are shifted but the data is not realigned. That is, use freq if you would like to extend the index when shifting and preserve the original data. If freq is specified as “infer” then it will be inferred from the freq or inferred_freq attributes of the index. If neither of those attributes exist, a ValueError is thrown

  • axis ({0 or 'index', 1 or 'columns', None}, default None) – Shift direction.

  • fill_value (object, optional) –

    The scalar value to use for newly introduced missing values. the default depends on the dtype of self. For numeric data, np.nan is used. For datetime, timedelta, or period data, etc. NaT is used. For extension dtypes, self.dtype.na_value is used.

    Changed in version 1.1.0.

Returns

Copy of input object, shifted.

Return type

DataFrame

See also

Index.shift()

Shift values of Index.

DatetimeIndex.shift()

Shift values of DatetimeIndex.

PeriodIndex.shift()

Shift values of PeriodIndex.

tshift()

Shift the time index, using the index’s frequency if available.

Examples

>>> df = pd.DataFrame({"Col1": [10, 20, 15, 30, 45],
...                    "Col2": [13, 23, 18, 33, 48],
...                    "Col3": [17, 27, 22, 37, 52]},
...                   index=pd.date_range("2020-01-01", "2020-01-05"))
>>> df
            Col1  Col2  Col3
2020-01-01    10    13    17
2020-01-02    20    23    27
2020-01-03    15    18    22
2020-01-04    30    33    37
2020-01-05    45    48    52
>>> df.shift(periods=3)
            Col1  Col2  Col3
2020-01-01   NaN   NaN   NaN
2020-01-02   NaN   NaN   NaN
2020-01-03   NaN   NaN   NaN
2020-01-04  10.0  13.0  17.0
2020-01-05  20.0  23.0  27.0
>>> df.shift(periods=1, axis="columns")
            Col1  Col2  Col3
2020-01-01   NaN  10.0  13.0
2020-01-02   NaN  20.0  23.0
2020-01-03   NaN  15.0  18.0
2020-01-04   NaN  30.0  33.0
2020-01-05   NaN  45.0  48.0
>>> df.shift(periods=3, fill_value=0)
            Col1  Col2  Col3
2020-01-01     0     0     0
2020-01-02     0     0     0
2020-01-03     0     0     0
2020-01-04    10    13    17
2020-01-05    20    23    27
>>> df.shift(periods=3, freq="D")
            Col1  Col2  Col3
2020-01-04    10    13    17
2020-01-05    20    23    27
2020-01-06    15    18    22
2020-01-07    30    33    37
2020-01-08    45    48    52
>>> df.shift(periods=3, freq="infer")
            Col1  Col2  Col3
2020-01-04    10    13    17
2020-01-05    20    23    27
2020-01-06    15    18    22
2020-01-07    30    33    37
2020-01-08    45    48    52
property size

Return an int representing the number of elements in this object.

Return the number of rows if Series. Otherwise return the number of rows times number of columns if DataFrame.

See also

ndarray.size

Number of elements in the array.

Examples

>>> s = pd.Series({'a': 1, 'b': 2, 'c': 3})
>>> s.size
3
>>> df = pd.DataFrame({'col1': [1, 2], 'col2': [3, 4]})
>>> df.size
4
skew(axis=None, skipna=None, level=None, numeric_only=None, **kwargs)

Return unbiased skew over requested axis.

Normalized by N-1.

Parameters
  • axis ({index (0), columns (1)}) – Axis for the function to be applied on.

  • skipna (bool, default True) – Exclude NA/null values when computing the result.

  • level (int or level name, default None) – If the axis is a MultiIndex (hierarchical), count along a particular level, collapsing into a Series.

  • numeric_only (bool, default None) – Include only float, int, boolean columns. If None, will attempt to use everything, then use only numeric data. Not implemented for Series.

  • **kwargs – Additional keyword arguments to be passed to the function.

Returns

Return type

Series or DataFrame (if level specified)

slice_shift(periods: int = 1, axis=0) → FrameOrSeries

Equivalent to shift without copying data.

The shifted data will not include the dropped periods and the shifted axis will be smaller than the original.

Parameters

periods (int) – Number of periods to move, can be positive or negative.

Returns

shifted

Return type

same type as caller

Notes

While the slice_shift is faster than shift, you may pay for it later during alignment.

sort_index(axis=0, level=None, ascending: bool = True, inplace: bool = False, kind: str = 'quicksort', na_position: str = 'last', sort_remaining: bool = True, ignore_index: bool = False, key: Optional[Callable[[Index], Union[Index, AnyArrayLike]]] = None)

Sort object by labels (along an axis).

Returns a new DataFrame sorted by label if inplace argument is False, otherwise updates the original DataFrame and returns None.

Parameters
  • axis ({0 or 'index', 1 or 'columns'}, default 0) – The axis along which to sort. The value 0 identifies the rows, and 1 identifies the columns.

  • level (int or level name or list of ints or list of level names) – If not None, sort on values in specified index level(s).

  • ascending (bool or list of bools, default True) – Sort ascending vs. descending. When the index is a MultiIndex the sort direction can be controlled for each level individually.

  • inplace (bool, default False) – If True, perform operation in-place.

  • kind ({'quicksort', 'mergesort', 'heapsort'}, default 'quicksort') – Choice of sorting algorithm. See also ndarray.np.sort for more information. mergesort is the only stable algorithm. For DataFrames, this option is only applied when sorting on a single column or label.

  • na_position ({'first', 'last'}, default 'last') – Puts NaNs at the beginning if first; last puts NaNs at the end. Not implemented for MultiIndex.

  • sort_remaining (bool, default True) – If True and sorting by level and index is multilevel, sort by other levels too (in order) after sorting by specified level.

  • ignore_index (bool, default False) –

    If True, the resulting axis will be labeled 0, 1, …, n - 1.

    New in version 1.0.0.

  • key (callable, optional) –

    If not None, apply the key function to the index values before sorting. This is similar to the key argument in the builtin sorted() function, with the notable difference that this key function should be vectorized. It should expect an Index and return an Index of the same shape. For MultiIndex inputs, the key is applied per level.

    New in version 1.1.0.

Returns

The original DataFrame sorted by the labels.

Return type

DataFrame

See also

Series.sort_index()

Sort Series by the index.

DataFrame.sort_values()

Sort DataFrame by the value.

Series.sort_values()

Sort Series by the value.

Examples

>>> df = pd.DataFrame([1, 2, 3, 4, 5], index=[100, 29, 234, 1, 150],
...                   columns=['A'])
>>> df.sort_index()
     A
1    4
29   2
100  1
150  5
234  3

By default, it sorts in ascending order, to sort in descending order, use ascending=False

>>> df.sort_index(ascending=False)
     A
234  3
150  5
100  1
29   2
1    4

A key function can be specified which is applied to the index before sorting. For a MultiIndex this is applied to each level separately.

>>> df = pd.DataFrame({"a": [1, 2, 3, 4]}, index=['A', 'b', 'C', 'd'])
>>> df.sort_index(key=lambda x: x.str.lower())
   a
A  1
b  2
C  3
d  4
sort_values(by, axis=0, ascending=True, inplace=False, kind='quicksort', na_position='last', ignore_index=False, key: Optional[Callable[[Series], Union[Series, AnyArrayLike]]] = None)

Sort by the values along either axis.

Parameters

by (str or list of str) –

Name or list of names to sort by.

  • if axis is 0 or ‘index’ then by may contain index levels and/or column labels.

  • if axis is 1 or ‘columns’ then by may contain column levels and/or index labels.

Changed in version 0.23.0: Allow specifying index or column level names.

axis{0 or ‘index’, 1 or ‘columns’}, default 0

Axis to be sorted.

ascendingbool or list of bool, default True

Sort ascending vs. descending. Specify list for multiple sort orders. If this is a list of bools, must match the length of the by.

inplacebool, default False

If True, perform operation in-place.

kind{‘quicksort’, ‘mergesort’, ‘heapsort’}, default ‘quicksort’

Choice of sorting algorithm. See also ndarray.np.sort for more information. mergesort is the only stable algorithm. For DataFrames, this option is only applied when sorting on a single column or label.

na_position{‘first’, ‘last’}, default ‘last’

Puts NaNs at the beginning if first; last puts NaNs at the end.

ignore_indexbool, default False

If True, the resulting axis will be labeled 0, 1, …, n - 1.

New in version 1.0.0.

keycallable, optional

Apply the key function to the values before sorting. This is similar to the key argument in the builtin sorted() function, with the notable difference that this key function should be vectorized. It should expect a Series and return a Series with the same shape as the input. It will be applied to each column in by independently.

New in version 1.1.0.

Returns

DataFrame with sorted values if inplace=False, None otherwise.

Return type

DataFrame or None

See also

DataFrame.sort_index()

Sort a DataFrame by the index.

Series.sort_values()

Similar method for a Series.

Examples

>>> df = pd.DataFrame({
...     'col1': ['A', 'A', 'B', np.nan, 'D', 'C'],
...     'col2': [2, 1, 9, 8, 7, 4],
...     'col3': [0, 1, 9, 4, 2, 3],
...     'col4': ['a', 'B', 'c', 'D', 'e', 'F']
... })
>>> df
  col1  col2  col3 col4
0    A     2     0    a
1    A     1     1    B
2    B     9     9    c
3  NaN     8     4    D
4    D     7     2    e
5    C     4     3    F

Sort by col1

>>> df.sort_values(by=['col1'])
  col1  col2  col3 col4
0    A     2     0    a
1    A     1     1    B
2    B     9     9    c
5    C     4     3    F
4    D     7     2    e
3  NaN     8     4    D

Sort by multiple columns

>>> df.sort_values(by=['col1', 'col2'])
  col1  col2  col3 col4
1    A     1     1    B
0    A     2     0    a
2    B     9     9    c
5    C     4     3    F
4    D     7     2    e
3  NaN     8     4    D

Sort Descending

>>> df.sort_values(by='col1', ascending=False)
  col1  col2  col3 col4
4    D     7     2    e
5    C     4     3    F
2    B     9     9    c
0    A     2     0    a
1    A     1     1    B
3  NaN     8     4    D

Putting NAs first

>>> df.sort_values(by='col1', ascending=False, na_position='first')
  col1  col2  col3 col4
3  NaN     8     4    D
4    D     7     2    e
5    C     4     3    F
2    B     9     9    c
0    A     2     0    a
1    A     1     1    B

Sorting with a key function

>>> df.sort_values(by='col4', key=lambda col: col.str.lower())
   col1  col2  col3 col4
0    A     2     0    a
1    A     1     1    B
2    B     9     9    c
3  NaN     8     4    D
4    D     7     2    e
5    C     4     3    F
sparse

alias of pandas.core.arrays.sparse.accessor.SparseFrameAccessor

squeeze(axis=None)

Squeeze 1 dimensional axis objects into scalars.

Series or DataFrames with a single element are squeezed to a scalar. DataFrames with a single column or a single row are squeezed to a Series. Otherwise the object is unchanged.

This method is most useful when you don’t know if your object is a Series or DataFrame, but you do know it has just a single column. In that case you can safely call squeeze to ensure you have a Series.

Parameters

axis ({0 or 'index', 1 or 'columns', None}, default None) – A specific axis to squeeze. By default, all length-1 axes are squeezed.

Returns

The projection after squeezing axis or all the axes.

Return type

DataFrame, Series, or scalar

See also

Series.iloc()

Integer-location based indexing for selecting scalars.

DataFrame.iloc()

Integer-location based indexing for selecting Series.

Series.to_frame()

Inverse of DataFrame.squeeze for a single-column DataFrame.

Examples

>>> primes = pd.Series([2, 3, 5, 7])

Slicing might produce a Series with a single value:

>>> even_primes = primes[primes % 2 == 0]
>>> even_primes
0    2
dtype: int64
>>> even_primes.squeeze()
2

Squeezing objects with more than one value in every axis does nothing:

>>> odd_primes = primes[primes % 2 == 1]
>>> odd_primes
1    3
2    5
3    7
dtype: int64
>>> odd_primes.squeeze()
1    3
2    5
3    7
dtype: int64

Squeezing is even more effective when used with DataFrames.

>>> df = pd.DataFrame([[1, 2], [3, 4]], columns=['a', 'b'])
>>> df
   a  b
0  1  2
1  3  4

Slicing a single column will produce a DataFrame with the columns having only one value:

>>> df_a = df[['a']]
>>> df_a
   a
0  1
1  3

So the columns can be squeezed down, resulting in a Series:

>>> df_a.squeeze('columns')
0    1
1    3
Name: a, dtype: int64

Slicing a single row from a single column will produce a single scalar DataFrame:

>>> df_0a = df.loc[df.index < 1, ['a']]
>>> df_0a
   a
0  1

Squeezing the rows produces a single scalar Series:

>>> df_0a.squeeze('rows')
a    1
Name: 0, dtype: int64

Squeezing all axes will project directly into a scalar:

>>> df_0a.squeeze()
1
stack(level=-1, dropna=True)

Stack the prescribed level(s) from columns to index.

Return a reshaped DataFrame or Series having a multi-level index with one or more new inner-most levels compared to the current DataFrame. The new inner-most levels are created by pivoting the columns of the current dataframe:

  • if the columns have a single level, the output is a Series;

  • if the columns have multiple levels, the new index level(s) is (are) taken from the prescribed level(s) and the output is a DataFrame.

Parameters
  • level (int, str, list, default -1) – Level(s) to stack from the column axis onto the index axis, defined as one index or label, or a list of indices or labels.

  • dropna (bool, default True) – Whether to drop rows in the resulting Frame/Series with missing values. Stacking a column level onto the index axis can create combinations of index and column values that are missing from the original dataframe. See Examples section.

Returns

Stacked dataframe or series.

Return type

DataFrame or Series

See also

DataFrame.unstack()

Unstack prescribed level(s) from index axis onto column axis.

DataFrame.pivot()

Reshape dataframe from long format to wide format.

DataFrame.pivot_table()

Create a spreadsheet-style pivot table as a DataFrame.

Notes

The function is named by analogy with a collection of books being reorganized from being side by side on a horizontal position (the columns of the dataframe) to being stacked vertically on top of each other (in the index of the dataframe).

Examples

Single level columns

>>> df_single_level_cols = pd.DataFrame([[0, 1], [2, 3]],
...                                     index=['cat', 'dog'],
...                                     columns=['weight', 'height'])

Stacking a dataframe with a single level column axis returns a Series:

>>> df_single_level_cols
     weight height
cat       0      1
dog       2      3
>>> df_single_level_cols.stack()
cat  weight    0
     height    1
dog  weight    2
     height    3
dtype: int64

Multi level columns: simple case

>>> multicol1 = pd.MultiIndex.from_tuples([('weight', 'kg'),
...                                        ('weight', 'pounds')])
>>> df_multi_level_cols1 = pd.DataFrame([[1, 2], [2, 4]],
...                                     index=['cat', 'dog'],
...                                     columns=multicol1)

Stacking a dataframe with a multi-level column axis:

>>> df_multi_level_cols1
     weight
         kg    pounds
cat       1        2
dog       2        4
>>> df_multi_level_cols1.stack()
            weight
cat kg           1
    pounds       2
dog kg           2
    pounds       4

Missing values

>>> multicol2 = pd.MultiIndex.from_tuples([('weight', 'kg'),
...                                        ('height', 'm')])
>>> df_multi_level_cols2 = pd.DataFrame([[1.0, 2.0], [3.0, 4.0]],
...                                     index=['cat', 'dog'],
...                                     columns=multicol2)

It is common to have missing values when stacking a dataframe with multi-level columns, as the stacked dataframe typically has more values than the original dataframe. Missing values are filled with NaNs:

>>> df_multi_level_cols2
    weight height
        kg      m
cat    1.0    2.0
dog    3.0    4.0
>>> df_multi_level_cols2.stack()
        height  weight
cat kg     NaN     1.0
    m      2.0     NaN
dog kg     NaN     3.0
    m      4.0     NaN

Prescribing the level(s) to be stacked

The first parameter controls which level or levels are stacked:

>>> df_multi_level_cols2.stack(0)
             kg    m
cat height  NaN  2.0
    weight  1.0  NaN
dog height  NaN  4.0
    weight  3.0  NaN
>>> df_multi_level_cols2.stack([0, 1])
cat  height  m     2.0
     weight  kg    1.0
dog  height  m     4.0
     weight  kg    3.0
dtype: float64

Dropping missing values

>>> df_multi_level_cols3 = pd.DataFrame([[None, 1.0], [2.0, 3.0]],
...                                     index=['cat', 'dog'],
...                                     columns=multicol2)

Note that rows where all values are missing are dropped by default but this behaviour can be controlled via the dropna keyword parameter:

>>> df_multi_level_cols3
    weight height
        kg      m
cat    NaN    1.0
dog    2.0    3.0
>>> df_multi_level_cols3.stack(dropna=False)
        height  weight
cat kg     NaN     NaN
    m      1.0     NaN
dog kg     NaN     2.0
    m      3.0     NaN
>>> df_multi_level_cols3.stack(dropna=True)
        height  weight
cat m      1.0     NaN
dog kg     NaN     2.0
    m      3.0     NaN
std(axis=None, skipna=None, level=None, ddof=1, numeric_only=None, **kwargs)

Return sample standard deviation over requested axis.

Normalized by N-1 by default. This can be changed using the ddof argument

Parameters
  • axis ({index (0), columns (1)}) –

  • skipna (bool, default True) – Exclude NA/null values. If an entire row/column is NA, the result will be NA.

  • level (int or level name, default None) – If the axis is a MultiIndex (hierarchical), count along a particular level, collapsing into a Series.

  • ddof (int, default 1) – Delta Degrees of Freedom. The divisor used in calculations is N - ddof, where N represents the number of elements.

  • numeric_only (bool, default None) – Include only float, int, boolean columns. If None, will attempt to use everything, then use only numeric data. Not implemented for Series.

Returns

Return type

Series or DataFrame (if level specified)

property style

Returns a Styler object.

Contains methods for building a styled HTML representation of the DataFrame.

See also

io.formats.style.Styler

Helps style a DataFrame or Series according to the data with HTML and CSS.

sub(other, axis='columns', level=None, fill_value=None)

Get Subtraction of dataframe and other, element-wise (binary operator sub).

Equivalent to dataframe - other, but with support to substitute a fill_value for missing data in one of the inputs. With reverse version, rsub.

Among flexible wrappers (add, sub, mul, div, mod, pow) to arithmetic operators: +, -, *, /, //, %, **.

Parameters
  • other (scalar, sequence, Series, or DataFrame) – Any single or multiple element data structure, or list-like object.

  • axis ({0 or 'index', 1 or 'columns'}) – Whether to compare by the index (0 or ‘index’) or columns (1 or ‘columns’). For Series input, axis to match Series index on.

  • level (int or label) – Broadcast across a level, matching Index values on the passed MultiIndex level.

  • fill_value (float or None, default None) – Fill existing missing (NaN) values, and any new element needed for successful DataFrame alignment, with this value before computation. If data in both corresponding DataFrame locations is missing the result will be missing.

Returns

Result of the arithmetic operation.

Return type

DataFrame

See also

DataFrame.add()

Add DataFrames.

DataFrame.sub()

Subtract DataFrames.

DataFrame.mul()

Multiply DataFrames.

DataFrame.div()

Divide DataFrames (float division).

DataFrame.truediv()

Divide DataFrames (float division).

DataFrame.floordiv()

Divide DataFrames (integer division).

DataFrame.mod()

Calculate modulo (remainder after division).

DataFrame.pow()

Calculate exponential power.

Notes

Mismatched indices will be unioned together.

Examples

>>> df = pd.DataFrame({'angles': [0, 3, 4],
...                    'degrees': [360, 180, 360]},
...                   index=['circle', 'triangle', 'rectangle'])
>>> df
           angles  degrees
circle          0      360
triangle        3      180
rectangle       4      360

Add a scalar with operator version which return the same results.

>>> df + 1
           angles  degrees
circle          1      361
triangle        4      181
rectangle       5      361
>>> df.add(1)
           angles  degrees
circle          1      361
triangle        4      181
rectangle       5      361

Divide by constant with reverse version.

>>> df.div(10)
           angles  degrees
circle        0.0     36.0
triangle      0.3     18.0
rectangle     0.4     36.0
>>> df.rdiv(10)
             angles   degrees
circle          inf  0.027778
triangle   3.333333  0.055556
rectangle  2.500000  0.027778

Subtract a list and Series by axis with operator version.

>>> df - [1, 2]
           angles  degrees
circle         -1      358
triangle        2      178
rectangle       3      358
>>> df.sub([1, 2], axis='columns')
           angles  degrees
circle         -1      358
triangle        2      178
rectangle       3      358
>>> df.sub(pd.Series([1, 1, 1], index=['circle', 'triangle', 'rectangle']),
...        axis='index')
           angles  degrees
circle         -1      359
triangle        2      179
rectangle       3      359

Multiply a DataFrame of different shape with operator version.

>>> other = pd.DataFrame({'angles': [0, 3, 4]},
...                      index=['circle', 'triangle', 'rectangle'])
>>> other
           angles
circle          0
triangle        3
rectangle       4
>>> df * other
           angles  degrees
circle          0      NaN
triangle        9      NaN
rectangle      16      NaN
>>> df.mul(other, fill_value=0)
           angles  degrees
circle          0      0.0
triangle        9      0.0
rectangle      16      0.0

Divide by a MultiIndex by level.

>>> df_multindex = pd.DataFrame({'angles': [0, 3, 4, 4, 5, 6],
...                              'degrees': [360, 180, 360, 360, 540, 720]},
...                             index=[['A', 'A', 'A', 'B', 'B', 'B'],
...                                    ['circle', 'triangle', 'rectangle',
...                                     'square', 'pentagon', 'hexagon']])
>>> df_multindex
             angles  degrees
A circle          0      360
  triangle        3      180
  rectangle       4      360
B square          4      360
  pentagon        5      540
  hexagon         6      720
>>> df.div(df_multindex, level=1, fill_value=0)
             angles  degrees
A circle        NaN      1.0
  triangle      1.0      1.0
  rectangle     1.0      1.0
B square        0.0      0.0
  pentagon      0.0      0.0
  hexagon       0.0      0.0
subtract(other, axis='columns', level=None, fill_value=None)

Get Subtraction of dataframe and other, element-wise (binary operator sub).

Equivalent to dataframe - other, but with support to substitute a fill_value for missing data in one of the inputs. With reverse version, rsub.

Among flexible wrappers (add, sub, mul, div, mod, pow) to arithmetic operators: +, -, *, /, //, %, **.

Parameters
  • other (scalar, sequence, Series, or DataFrame) – Any single or multiple element data structure, or list-like object.

  • axis ({0 or 'index', 1 or 'columns'}) – Whether to compare by the index (0 or ‘index’) or columns (1 or ‘columns’). For Series input, axis to match Series index on.

  • level (int or label) – Broadcast across a level, matching Index values on the passed MultiIndex level.

  • fill_value (float or None, default None) – Fill existing missing (NaN) values, and any new element needed for successful DataFrame alignment, with this value before computation. If data in both corresponding DataFrame locations is missing the result will be missing.

Returns

Result of the arithmetic operation.

Return type

DataFrame

See also

DataFrame.add()

Add DataFrames.

DataFrame.sub()

Subtract DataFrames.

DataFrame.mul()

Multiply DataFrames.

DataFrame.div()

Divide DataFrames (float division).

DataFrame.truediv()

Divide DataFrames (float division).

DataFrame.floordiv()

Divide DataFrames (integer division).

DataFrame.mod()

Calculate modulo (remainder after division).

DataFrame.pow()

Calculate exponential power.

Notes

Mismatched indices will be unioned together.

Examples

>>> df = pd.DataFrame({'angles': [0, 3, 4],
...                    'degrees': [360, 180, 360]},
...                   index=['circle', 'triangle', 'rectangle'])
>>> df
           angles  degrees
circle          0      360
triangle        3      180
rectangle       4      360

Add a scalar with operator version which return the same results.

>>> df + 1
           angles  degrees
circle          1      361
triangle        4      181
rectangle       5      361
>>> df.add(1)
           angles  degrees
circle          1      361
triangle        4      181
rectangle       5      361

Divide by constant with reverse version.

>>> df.div(10)
           angles  degrees
circle        0.0     36.0
triangle      0.3     18.0
rectangle     0.4     36.0
>>> df.rdiv(10)
             angles   degrees
circle          inf  0.027778
triangle   3.333333  0.055556
rectangle  2.500000  0.027778

Subtract a list and Series by axis with operator version.

>>> df - [1, 2]
           angles  degrees
circle         -1      358
triangle        2      178
rectangle       3      358
>>> df.sub([1, 2], axis='columns')
           angles  degrees
circle         -1      358
triangle        2      178
rectangle       3      358
>>> df.sub(pd.Series([1, 1, 1], index=['circle', 'triangle', 'rectangle']),
...        axis='index')
           angles  degrees
circle         -1      359
triangle        2      179
rectangle       3      359

Multiply a DataFrame of different shape with operator version.

>>> other = pd.DataFrame({'angles': [0, 3, 4]},
...                      index=['circle', 'triangle', 'rectangle'])
>>> other
           angles
circle          0
triangle        3
rectangle       4
>>> df * other
           angles  degrees
circle          0      NaN
triangle        9      NaN
rectangle      16      NaN
>>> df.mul(other, fill_value=0)
           angles  degrees
circle          0      0.0
triangle        9      0.0
rectangle      16      0.0

Divide by a MultiIndex by level.

>>> df_multindex = pd.DataFrame({'angles': [0, 3, 4, 4, 5, 6],
...                              'degrees': [360, 180, 360, 360, 540, 720]},
...                             index=[['A', 'A', 'A', 'B', 'B', 'B'],
...                                    ['circle', 'triangle', 'rectangle',
...                                     'square', 'pentagon', 'hexagon']])
>>> df_multindex
             angles  degrees
A circle          0      360
  triangle        3      180
  rectangle       4      360
B square          4      360
  pentagon        5      540
  hexagon         6      720
>>> df.div(df_multindex, level=1, fill_value=0)
             angles  degrees
A circle        NaN      1.0
  triangle      1.0      1.0
  rectangle     1.0      1.0
B square        0.0      0.0
  pentagon      0.0      0.0
  hexagon       0.0      0.0
sum(axis=None, skipna=None, level=None, numeric_only=None, min_count=0, **kwargs)

Return the sum of the values for the requested axis.

This is equivalent to the method numpy.sum.

Parameters
  • axis ({index (0), columns (1)}) – Axis for the function to be applied on.

  • skipna (bool, default True) – Exclude NA/null values when computing the result.

  • level (int or level name, default None) – If the axis is a MultiIndex (hierarchical), count along a particular level, collapsing into a Series.

  • numeric_only (bool, default None) – Include only float, int, boolean columns. If None, will attempt to use everything, then use only numeric data. Not implemented for Series.

  • min_count (int, default 0) –

    The required number of valid values to perform the operation. If fewer than min_count non-NA values are present the result will be NA.

    New in version 0.22.0: Added with the default being 0. This means the sum of an all-NA or empty Series is 0, and the product of an all-NA or empty Series is 1.

  • **kwargs – Additional keyword arguments to be passed to the function.

Returns

Return type

Series or DataFrame (if level specified)

See also

Series.sum()

Return the sum.

Series.min()

Return the minimum.

Series.max()

Return the maximum.

Series.idxmin()

Return the index of the minimum.

Series.idxmax()

Return the index of the maximum.

DataFrame.sum()

Return the sum over the requested axis.

DataFrame.min()

Return the minimum over the requested axis.

DataFrame.max()

Return the maximum over the requested axis.

DataFrame.idxmin()

Return the index of the minimum over the requested axis.

DataFrame.idxmax()

Return the index of the maximum over the requested axis.

Examples

>>> idx = pd.MultiIndex.from_arrays([
...     ['warm', 'warm', 'cold', 'cold'],
...     ['dog', 'falcon', 'fish', 'spider']],
...     names=['blooded', 'animal'])
>>> s = pd.Series([4, 2, 0, 8], name='legs', index=idx)
>>> s
blooded  animal
warm     dog       4
         falcon    2
cold     fish      0
         spider    8
Name: legs, dtype: int64
>>> s.sum()
14

Sum using level names, as well as indices.

>>> s.sum(level='blooded')
blooded
warm    6
cold    8
Name: legs, dtype: int64
>>> s.sum(level=0)
blooded
warm    6
cold    8
Name: legs, dtype: int64

By default, the sum of an empty or all-NA Series is 0.

>>> pd.Series([]).sum()  # min_count=0 is the default
0.0

This can be controlled with the min_count parameter. For example, if you’d like the sum of an empty series to be NaN, pass min_count=1.

>>> pd.Series([]).sum(min_count=1)
nan

Thanks to the skipna parameter, min_count handles all-NA and empty series identically.

>>> pd.Series([np.nan]).sum()
0.0
>>> pd.Series([np.nan]).sum(min_count=1)
nan
swapaxes(axis1, axis2, copy=True) → FrameOrSeries

Interchange axes and swap values axes appropriately.

Returns

y

Return type

same as input

swaplevel(i=-2, j=-1, axis=0) → pandas.core.frame.DataFrame

Swap levels i and j in a MultiIndex on a particular axis.

Parameters
  • j (i,) – Levels of the indices to be swapped. Can pass level name as string.

  • axis ({0 or 'index', 1 or 'columns'}, default 0) – The axis to swap levels on. 0 or ‘index’ for row-wise, 1 or ‘columns’ for column-wise.

Returns

Return type

DataFrame

tail(n: int = 5) → FrameOrSeries

Return the last n rows.

This function returns last n rows from the object based on position. It is useful for quickly verifying data, for example, after sorting or appending rows.

For negative values of n, this function returns all rows except the first n rows, equivalent to df[n:].

Parameters

n (int, default 5) – Number of rows to select.

Returns

The last n rows of the caller object.

Return type

type of caller

See also

DataFrame.head()

The first n rows of the caller object.

Examples

>>> df = pd.DataFrame({'animal': ['alligator', 'bee', 'falcon', 'lion',
...                    'monkey', 'parrot', 'shark', 'whale', 'zebra']})
>>> df
      animal
0  alligator
1        bee
2     falcon
3       lion
4     monkey
5     parrot
6      shark
7      whale
8      zebra

Viewing the last 5 lines

>>> df.tail()
   animal
4  monkey
5  parrot
6   shark
7   whale
8   zebra

Viewing the last n lines (three in this case)

>>> df.tail(3)
  animal
6  shark
7  whale
8  zebra

For negative values of n

>>> df.tail(-3)
   animal
3    lion
4  monkey
5  parrot
6   shark
7   whale
8   zebra
take(indices, axis=0, is_copy: Optional[bool] = None, **kwargs) → FrameOrSeries

Return the elements in the given positional indices along an axis.

This means that we are not indexing according to actual values in the index attribute of the object. We are indexing according to the actual position of the element in the object.

Parameters
  • indices (array-like) – An array of ints indicating which positions to take.

  • axis ({0 or 'index', 1 or 'columns', None}, default 0) – The axis on which to select elements. 0 means that we are selecting rows, 1 means that we are selecting columns.

  • is_copy (bool) –

    Before pandas 1.0, is_copy=False can be specified to ensure that the return value is an actual copy. Starting with pandas 1.0, take always returns a copy, and the keyword is therefore deprecated.

    Deprecated since version 1.0.0.

  • **kwargs – For compatibility with numpy.take(). Has no effect on the output.

Returns

taken – An array-like containing the elements taken from the object.

Return type

same type as caller

See also

DataFrame.loc()

Select a subset of a DataFrame by labels.

DataFrame.iloc()

Select a subset of a DataFrame by positions.

numpy.take()

Take elements from an array along an axis.

Examples

>>> df = pd.DataFrame([('falcon', 'bird', 389.0),
...                    ('parrot', 'bird', 24.0),
...                    ('lion', 'mammal', 80.5),
...                    ('monkey', 'mammal', np.nan)],
...                   columns=['name', 'class', 'max_speed'],
...                   index=[0, 2, 3, 1])
>>> df
     name   class  max_speed
0  falcon    bird      389.0
2  parrot    bird       24.0
3    lion  mammal       80.5
1  monkey  mammal        NaN

Take elements at positions 0 and 3 along the axis 0 (default).

Note how the actual indices selected (0 and 1) do not correspond to our selected indices 0 and 3. That’s because we are selecting the 0th and 3rd rows, not rows whose indices equal 0 and 3.

>>> df.take([0, 3])
     name   class  max_speed
0  falcon    bird      389.0
1  monkey  mammal        NaN

Take elements at indices 1 and 2 along the axis 1 (column selection).

>>> df.take([1, 2], axis=1)
    class  max_speed
0    bird      389.0
2    bird       24.0
3  mammal       80.5
1  mammal        NaN

We may take elements using negative integers for positive indices, starting from the end of the object, just like with Python lists.

>>> df.take([-1, -2])
     name   class  max_speed
1  monkey  mammal        NaN
3    lion  mammal       80.5
to_clipboard(excel: bool = True, sep: Optional[str] = None, **kwargs) → None

Copy object to the system clipboard.

Write a text representation of object to the system clipboard. This can be pasted into Excel, for example.

Parameters
  • excel (bool, default True) –

    Produce output in a csv format for easy pasting into excel.

    • True, use the provided separator for csv pasting.

    • False, write a string representation of the object to the clipboard.

  • sep (str, default '\t') – Field delimiter.

  • **kwargs – These parameters will be passed to DataFrame.to_csv.

See also

DataFrame.to_csv()

Write a DataFrame to a comma-separated values (csv) file.

read_clipboard()

Read text from clipboard and pass to read_table.

Notes

Requirements for your platform.

  • Linux : xclip, or xsel (with PyQt4 modules)

  • Windows : none

  • OS X : none

Examples

Copy the contents of a DataFrame to the clipboard.

>>> df = pd.DataFrame([[1, 2, 3], [4, 5, 6]], columns=['A', 'B', 'C'])
>>> df.to_clipboard(sep=',')  
... # Wrote the following to the system clipboard:
... # ,A,B,C
... # 0,1,2,3
... # 1,4,5,6

We can omit the index by passing the keyword index and setting it to false.

>>> df.to_clipboard(sep=',', index=False)  
... # Wrote the following to the system clipboard:
... # A,B,C
... # 1,2,3
... # 4,5,6
to_csv(path_or_buf: Union[str, pathlib.Path, IO[AnyStr], None] = None, sep: str = ',', na_rep: str = '', float_format: Optional[str] = None, columns: Optional[Sequence[Optional[Hashable]]] = None, header: Union[bool, List[str]] = True, index: bool = True, index_label: Union[bool, str, Sequence[Optional[Hashable]], None] = None, mode: str = 'w', encoding: Optional[str] = None, compression: Union[str, Mapping[str, str], None] = 'infer', quoting: Optional[int] = None, quotechar: str = '"', line_terminator: Optional[str] = None, chunksize: Optional[int] = None, date_format: Optional[str] = None, doublequote: bool = True, escapechar: Optional[str] = None, decimal: Optional[str] = '.', errors: str = 'strict') → Optional[str]

Write object to a comma-separated values (csv) file.

Changed in version 0.24.0: The order of arguments for Series was changed.

Parameters
  • path_or_buf (str or file handle, default None) –

    File path or object, if None is provided the result is returned as a string. If a file object is passed it should be opened with newline=’’, disabling universal newlines.

    Changed in version 0.24.0: Was previously named “path” for Series.

  • sep (str, default ',') – String of length 1. Field delimiter for the output file.

  • na_rep (str, default '') – Missing data representation.

  • float_format (str, default None) – Format string for floating point numbers.

  • columns (sequence, optional) – Columns to write.

  • header (bool or list of str, default True) –

    Write out the column names. If a list of strings is given it is assumed to be aliases for the column names.

    Changed in version 0.24.0: Previously defaulted to False for Series.

  • index (bool, default True) – Write row names (index).

  • index_label (str or sequence, or False, default None) – Column label for index column(s) if desired. If None is given, and header and index are True, then the index names are used. A sequence should be given if the object uses MultiIndex. If False do not print fields for index names. Use index_label=False for easier importing in R.

  • mode (str) – Python write mode, default ‘w’.

  • encoding (str, optional) – A string representing the encoding to use in the output file, defaults to ‘utf-8’.

  • compression (str or dict, default 'infer') –

    If str, represents compression mode. If dict, value at ‘method’ is the compression mode. Compression mode may be any of the following possible values: {‘infer’, ‘gzip’, ‘bz2’, ‘zip’, ‘xz’, None}. If compression mode is ‘infer’ and path_or_buf is path-like, then detect compression mode from the following extensions: ‘.gz’, ‘.bz2’, ‘.zip’ or ‘.xz’. (otherwise no compression). If dict given and mode is one of {‘zip’, ‘gzip’, ‘bz2’}, or inferred as one of the above, other entries passed as additional compression options.

    Changed in version 1.0.0: May now be a dict with key ‘method’ as compression mode and other entries as additional compression options if compression mode is ‘zip’.

    Changed in version 1.1.0: Passing compression options as keys in dict is supported for compression modes ‘gzip’ and ‘bz2’ as well as ‘zip’.

  • quoting (optional constant from csv module) – Defaults to csv.QUOTE_MINIMAL. If you have set a float_format then floats are converted to strings and thus csv.QUOTE_NONNUMERIC will treat them as non-numeric.

  • quotechar (str, default '"') – String of length 1. Character used to quote fields.

  • line_terminator (str, optional) –

    The newline character or character sequence to use in the output file. Defaults to os.linesep, which depends on the OS in which this method is called (‘n’ for linux, ‘rn’ for Windows, i.e.).

    Changed in version 0.24.0.

  • chunksize (int or None) – Rows to write at a time.

  • date_format (str, default None) – Format string for datetime objects.

  • doublequote (bool, default True) – Control quoting of quotechar inside a field.

  • escapechar (str, default None) – String of length 1. Character used to escape sep and quotechar when appropriate.

  • decimal (str, default '.') – Character recognized as decimal separator. E.g. use ‘,’ for European data.

  • errors (str, default 'strict') –

    Specifies how encoding and decoding errors are to be handled. See the errors argument for open() for a full list of options.

    New in version 1.1.0.

Returns

If path_or_buf is None, returns the resulting csv format as a string. Otherwise returns None.

Return type

None or str

See also

read_csv()

Load a CSV file into a DataFrame.

to_excel()

Write DataFrame to an Excel file.

Examples

>>> df = pd.DataFrame({'name': ['Raphael', 'Donatello'],
...                    'mask': ['red', 'purple'],
...                    'weapon': ['sai', 'bo staff']})
>>> df.to_csv(index=False)
'name,mask,weapon\nRaphael,red,sai\nDonatello,purple,bo staff\n'

Create ‘out.zip’ containing ‘out.csv’

>>> compression_opts = dict(method='zip',
...                         archive_name='out.csv')  
>>> df.to_csv('out.zip', index=False,
...           compression=compression_opts)  
to_dict(orient='dict', into=<class 'dict'>)

Convert the DataFrame to a dictionary.

The type of the key-value pairs can be customized with the parameters (see below).

Parameters
  • orient (str {'dict', 'list', 'series', 'split', 'records', 'index'}) –

    Determines the type of the values of the dictionary.

    • ’dict’ (default) : dict like {column -> {index -> value}}

    • ’list’ : dict like {column -> [values]}

    • ’series’ : dict like {column -> Series(values)}

    • ’split’ : dict like {‘index’ -> [index], ‘columns’ -> [columns], ‘data’ -> [values]}

    • ’records’ : list like [{column -> value}, … , {column -> value}]

    • ’index’ : dict like {index -> {column -> value}}

    Abbreviations are allowed. s indicates series and sp indicates split.

  • into (class, default dict) – The collections.abc.Mapping subclass used for all Mappings in the return value. Can be the actual class or an empty instance of the mapping type you want. If you want a collections.defaultdict, you must pass it initialized.

Returns

Return a collections.abc.Mapping object representing the DataFrame. The resulting transformation depends on the orient parameter.

Return type

dict, list or collections.abc.Mapping

See also

DataFrame.from_dict()

Create a DataFrame from a dictionary.

DataFrame.to_json()

Convert a DataFrame to JSON format.

Examples

>>> df = pd.DataFrame({'col1': [1, 2],
...                    'col2': [0.5, 0.75]},
...                   index=['row1', 'row2'])
>>> df
      col1  col2
row1     1  0.50
row2     2  0.75
>>> df.to_dict()
{'col1': {'row1': 1, 'row2': 2}, 'col2': {'row1': 0.5, 'row2': 0.75}}

You can specify the return orientation.

>>> df.to_dict('series')
{'col1': row1    1
         row2    2
Name: col1, dtype: int64,
'col2': row1    0.50
        row2    0.75
Name: col2, dtype: float64}
>>> df.to_dict('split')
{'index': ['row1', 'row2'], 'columns': ['col1', 'col2'],
 'data': [[1, 0.5], [2, 0.75]]}
>>> df.to_dict('records')
[{'col1': 1, 'col2': 0.5}, {'col1': 2, 'col2': 0.75}]
>>> df.to_dict('index')
{'row1': {'col1': 1, 'col2': 0.5}, 'row2': {'col1': 2, 'col2': 0.75}}

You can also specify the mapping type.

>>> from collections import OrderedDict, defaultdict
>>> df.to_dict(into=OrderedDict)
OrderedDict([('col1', OrderedDict([('row1', 1), ('row2', 2)])),
             ('col2', OrderedDict([('row1', 0.5), ('row2', 0.75)]))])

If you want a defaultdict, you need to initialize it:

>>> dd = defaultdict(list)
>>> df.to_dict('records', into=dd)
[defaultdict(<class 'list'>, {'col1': 1, 'col2': 0.5}),
 defaultdict(<class 'list'>, {'col1': 2, 'col2': 0.75})]
to_excel(excel_writer, sheet_name='Sheet1', na_rep='', float_format=None, columns=None, header=True, index=True, index_label=None, startrow=0, startcol=0, engine=None, merge_cells=True, encoding=None, inf_rep='inf', verbose=True, freeze_panes=None) → None

Write object to an Excel sheet.

To write a single object to an Excel .xlsx file it is only necessary to specify a target file name. To write to multiple sheets it is necessary to create an ExcelWriter object with a target file name, and specify a sheet in the file to write to.

Multiple sheets may be written to by specifying unique sheet_name. With all data written to the file it is necessary to save the changes. Note that creating an ExcelWriter object with a file name that already exists will result in the contents of the existing file being erased.

Parameters
  • excel_writer (str or ExcelWriter object) – File path or existing ExcelWriter.

  • sheet_name (str, default 'Sheet1') – Name of sheet which will contain DataFrame.

  • na_rep (str, default '') – Missing data representation.

  • float_format (str, optional) – Format string for floating point numbers. For example float_format="%.2f" will format 0.1234 to 0.12.

  • columns (sequence or list of str, optional) – Columns to write.

  • header (bool or list of str, default True) – Write out the column names. If a list of string is given it is assumed to be aliases for the column names.

  • index (bool, default True) – Write row names (index).

  • index_label (str or sequence, optional) – Column label for index column(s) if desired. If not specified, and header and index are True, then the index names are used. A sequence should be given if the DataFrame uses MultiIndex.

  • startrow (int, default 0) – Upper left cell row to dump data frame.

  • startcol (int, default 0) – Upper left cell column to dump data frame.

  • engine (str, optional) – Write engine to use, ‘openpyxl’ or ‘xlsxwriter’. You can also set this via the options io.excel.xlsx.writer, io.excel.xls.writer, and io.excel.xlsm.writer.

  • merge_cells (bool, default True) – Write MultiIndex and Hierarchical Rows as merged cells.

  • encoding (str, optional) – Encoding of the resulting excel file. Only necessary for xlwt, other writers support unicode natively.

  • inf_rep (str, default 'inf') – Representation for infinity (there is no native representation for infinity in Excel).

  • verbose (bool, default True) – Display more information in the error logs.

  • freeze_panes (tuple of int (length 2), optional) – Specifies the one-based bottommost row and rightmost column that is to be frozen.

See also

to_csv()

Write DataFrame to a comma-separated values (csv) file.

ExcelWriter()

Class for writing DataFrame objects into excel sheets.

read_excel()

Read an Excel file into a pandas DataFrame.

read_csv()

Read a comma-separated values (csv) file into DataFrame.

Notes

For compatibility with to_csv(), to_excel serializes lists and dicts to strings before writing.

Once a workbook has been saved it is not possible write further data without rewriting the whole workbook.

Examples

Create, write to and save a workbook:

>>> df1 = pd.DataFrame([['a', 'b'], ['c', 'd']],
...                    index=['row 1', 'row 2'],
...                    columns=['col 1', 'col 2'])
>>> df1.to_excel("output.xlsx")  

To specify the sheet name:

>>> df1.to_excel("output.xlsx",
...              sheet_name='Sheet_name_1')  

If you wish to write to more than one sheet in the workbook, it is necessary to specify an ExcelWriter object:

>>> df2 = df1.copy()
>>> with pd.ExcelWriter('output.xlsx') as writer:  
...     df1.to_excel(writer, sheet_name='Sheet_name_1')
...     df2.to_excel(writer, sheet_name='Sheet_name_2')

ExcelWriter can also be used to append to an existing Excel file:

>>> with pd.ExcelWriter('output.xlsx',
...                     mode='a') as writer:  
...     df.to_excel(writer, sheet_name='Sheet_name_3')

To set the library that is used to write the Excel file, you can pass the engine keyword (the default engine is automatically chosen depending on the file extension):

>>> df1.to_excel('output1.xlsx', engine='xlsxwriter')  
to_feather(path, **kwargs) → None

Write a DataFrame to the binary Feather format.

Parameters
  • path (str) – String file path.

  • **kwargs

    Additional keywords passed to pyarrow.feather.write_feather(). Starting with pyarrow 0.17, this includes the compression, compression_level, chunksize and version keywords.

    New in version 1.1.0.

to_gbq(destination_table, project_id=None, chunksize=None, reauth=False, if_exists='fail', auth_local_webserver=False, table_schema=None, location=None, progress_bar=True, credentials=None) → None

Write a DataFrame to a Google BigQuery table.

This function requires the pandas-gbq package.

See the How to authenticate with Google BigQuery guide for authentication instructions.

Parameters
  • destination_table (str) – Name of table to be written, in the form dataset.tablename.

  • project_id (str, optional) – Google BigQuery Account project ID. Optional when available from the environment.

  • chunksize (int, optional) – Number of rows to be inserted in each chunk from the dataframe. Set to None to load the whole dataframe at once.

  • reauth (bool, default False) – Force Google BigQuery to re-authenticate the user. This is useful if multiple accounts are used.

  • if_exists (str, default 'fail') –

    Behavior when the destination table exists. Value can be one of:

    'fail'

    If table exists raise pandas_gbq.gbq.TableCreationError.

    'replace'

    If table exists, drop it, recreate it, and insert data.

    'append'

    If table exists, insert data. Create if does not exist.

  • auth_local_webserver (bool, default False) –

    Use the local webserver flow instead of the console flow when getting user credentials.

    New in version 0.2.0 of pandas-gbq.

  • table_schema (list of dicts, optional) –

    List of BigQuery table fields to which according DataFrame columns conform to, e.g. [{'name': 'col1', 'type': 'STRING'},...]. If schema is not provided, it will be generated according to dtypes of DataFrame columns. See BigQuery API documentation on available names of a field.

    New in version 0.3.1 of pandas-gbq.

  • location (str, optional) –

    Location where the load job should run. See the BigQuery locations documentation for a list of available locations. The location must match that of the target dataset.

    New in version 0.5.0 of pandas-gbq.

  • progress_bar (bool, default True) –

    Use the library tqdm to show the progress bar for the upload, chunk by chunk.

    New in version 0.5.0 of pandas-gbq.

  • credentials (google.auth.credentials.Credentials, optional) –

    Credentials for accessing Google APIs. Use this parameter to override default credentials, such as to use Compute Engine google.auth.compute_engine.Credentials or Service Account google.oauth2.service_account.Credentials directly.

    New in version 0.8.0 of pandas-gbq.

    New in version 0.24.0.

See also

pandas_gbq.to_gbq()

This function in the pandas-gbq library.

read_gbq()

Read a DataFrame from Google BigQuery.

to_hdf(path_or_buf, key: str, mode: str = 'a', complevel: Optional[int] = None, complib: Optional[str] = None, append: bool = False, format: Optional[str] = None, index: bool = True, min_itemsize: Union[int, Dict[str, int], None] = None, nan_rep=None, dropna: Optional[bool] = None, data_columns: Union[bool, List[str], None] = None, errors: str = 'strict', encoding: str = 'UTF-8') → None

Write the contained data to an HDF5 file using HDFStore.

Hierarchical Data Format (HDF) is self-describing, allowing an application to interpret the structure and contents of a file with no outside information. One HDF file can hold a mix of related objects which can be accessed as a group or as individual objects.

In order to add another DataFrame or Series to an existing HDF file please use append mode and a different a key.

For more information see the user guide.

Parameters
  • path_or_buf (str or pandas.HDFStore) – File path or HDFStore object.

  • key (str) – Identifier for the group in the store.

  • mode ({'a', 'w', 'r+'}, default 'a') –

    Mode to open file:

    • ’w’: write, a new file is created (an existing file with the same name would be deleted).

    • ’a’: append, an existing file is opened for reading and writing, and if the file does not exist it is created.

    • ’r+’: similar to ‘a’, but the file must already exist.

  • complevel ({0-9}, optional) – Specifies a compression level for data. A value of 0 disables compression.

  • complib ({'zlib', 'lzo', 'bzip2', 'blosc'}, default 'zlib') – Specifies the compression library to be used. As of v0.20.2 these additional compressors for Blosc are supported (default if no compressor specified: ‘blosc:blosclz’): {‘blosc:blosclz’, ‘blosc:lz4’, ‘blosc:lz4hc’, ‘blosc:snappy’, ‘blosc:zlib’, ‘blosc:zstd’}. Specifying a compression library which is not available issues a ValueError.

  • append (bool, default False) – For Table formats, append the input data to the existing.

  • format ({'fixed', 'table', None}, default 'fixed') –

    Possible values:

    • ’fixed’: Fixed format. Fast writing/reading. Not-appendable, nor searchable.

    • ’table’: Table format. Write as a PyTables Table structure which may perform worse but allow more flexible operations like searching / selecting subsets of the data.

    • If None, pd.get_option(‘io.hdf.default_format’) is checked, followed by fallback to “fixed”

  • errors (str, default 'strict') – Specifies how encoding and decoding errors are to be handled. See the errors argument for open() for a full list of options.

  • encoding (str, default "UTF-8") –

  • min_itemsize (dict or int, optional) – Map column names to minimum string sizes for columns.

  • nan_rep (Any, optional) – How to represent null values as str. Not allowed with append=True.

  • data_columns (list of columns or True, optional) – List of columns to create as indexed data columns for on-disk queries, or True to use all columns. By default only the axes of the object are indexed. See Query via data columns. Applicable only to format=’table’.

See also

DataFrame.read_hdf()

Read from HDF file.

DataFrame.to_parquet()

Write a DataFrame to the binary parquet format.

DataFrame.to_sql()

Write to a sql table.

DataFrame.to_feather()

Write out feather-format for DataFrames.

DataFrame.to_csv()

Write out to a csv file.

Examples

>>> df = pd.DataFrame({'A': [1, 2, 3], 'B': [4, 5, 6]},
...                   index=['a', 'b', 'c'])
>>> df.to_hdf('data.h5', key='df', mode='w')

We can add another object to the same file:

>>> s = pd.Series([1, 2, 3, 4])
>>> s.to_hdf('data.h5', key='s')

Reading from HDF file:

>>> pd.read_hdf('data.h5', 'df')
A  B
a  1  4
b  2  5
c  3  6
>>> pd.read_hdf('data.h5', 's')
0    1
1    2
2    3
3    4
dtype: int64

Deleting file with data:

>>> import os
>>> os.remove('data.h5')
to_html(buf=None, columns=None, col_space=None, header=True, index=True, na_rep='NaN', formatters=None, float_format=None, sparsify=None, index_names=True, justify=None, max_rows=None, max_cols=None, show_dimensions=False, decimal='.', bold_rows=True, classes=None, escape=True, notebook=False, border=None, table_id=None, render_links=False, encoding=None)

Render a DataFrame as an HTML table.

Parameters
  • buf (str, Path or StringIO-like, optional, default None) – Buffer to write to. If None, the output is returned as a string.

  • columns (sequence, optional, default None) – The subset of columns to write. Writes all columns by default.

  • col_space (str or int, list or dict of int or str, optional) –

    The minimum width of each column in CSS length units. An int is assumed to be px units.

    New in version 0.25.0: Ability to use str.

  • header (bool, optional) – Whether to print column labels, default True.

  • index (bool, optional, default True) – Whether to print index (row) labels.

  • na_rep (str, optional, default 'NaN') – String representation of NAN to use.

  • formatters (list, tuple or dict of one-param. functions, optional) – Formatter functions to apply to columns’ elements by position or name. The result of each function must be a unicode string. List/tuple must be of length equal to the number of columns.

  • float_format (one-parameter function, optional, default None) – Formatter function to apply to columns’ elements if they are floats. The result of this function must be a unicode string.

  • sparsify (bool, optional, default True) – Set to False for a DataFrame with a hierarchical index to print every multiindex key at each row.

  • index_names (bool, optional, default True) – Prints the names of the indexes.

  • justify (str, default None) –

    How to justify the column labels. If None uses the option from the print configuration (controlled by set_option), ‘right’ out of the box. Valid values are

    • left

    • right

    • center

    • justify

    • justify-all

    • start

    • end

    • inherit

    • match-parent

    • initial

    • unset.

  • max_rows (int, optional) – Maximum number of rows to display in the console.

  • min_rows (int, optional) – The number of rows to display in the console in a truncated repr (when number of rows is above max_rows).

  • max_cols (int, optional) – Maximum number of columns to display in the console.

  • show_dimensions (bool, default False) – Display DataFrame dimensions (number of rows by number of columns).

  • decimal (str, default '.') – Character recognized as decimal separator, e.g. ‘,’ in Europe.

  • bold_rows (bool, default True) – Make the row labels bold in the output.

  • classes (str or list or tuple, default None) – CSS class(es) to apply to the resulting html table.

  • escape (bool, default True) – Convert the characters <, >, and & to HTML-safe sequences.

  • notebook ({True, False}, default False) – Whether the generated HTML is for IPython Notebook.

  • border (int) – A border=border attribute is included in the opening <table> tag. Default pd.options.display.html.border.

  • encoding (str, default "utf-8") –

    Set character encoding.

    New in version 1.0.

  • table_id (str, optional) –

    A css id is included in the opening <table> tag if specified.

    New in version 0.23.0.

  • render_links (bool, default False) –

    Convert URLs to HTML links.

    New in version 0.24.0.

Returns

If buf is None, returns the result as a string. Otherwise returns None.

Return type

str or None

See also

to_string()

Convert DataFrame to a string.

to_json(path_or_buf: Union[str, pathlib.Path, IO[AnyStr], None] = None, orient: Optional[str] = None, date_format: Optional[str] = None, double_precision: int = 10, force_ascii: bool = True, date_unit: str = 'ms', default_handler: Optional[Callable[[Any], Union[str, int, float, bool, List, Dict, None]]] = None, lines: bool = False, compression: Optional[str] = 'infer', index: bool = True, indent: Optional[int] = None) → Optional[str]

Convert the object to a JSON string.

Note NaN’s and None will be converted to null and datetime objects will be converted to UNIX timestamps.

Parameters
  • path_or_buf (str or file handle, optional) – File path or object. If not specified, the result is returned as a string.

  • orient (str) –

    Indication of expected JSON string format.

    • Series:

      • default is ‘index’

      • allowed values are: {‘split’,’records’,’index’,’table’}.

    • DataFrame:

      • default is ‘columns’

      • allowed values are: {‘split’, ‘records’, ‘index’, ‘columns’, ‘values’, ‘table’}.

    • The format of the JSON string:

      • ’split’ : dict like {‘index’ -> [index], ‘columns’ -> [columns], ‘data’ -> [values]}

      • ’records’ : list like [{column -> value}, … , {column -> value}]

      • ’index’ : dict like {index -> {column -> value}}

      • ’columns’ : dict like {column -> {index -> value}}

      • ’values’ : just the values array

      • ’table’ : dict like {‘schema’: {schema}, ‘data’: {data}}

      Describing the data, where data component is like orient='records'.

    Changed in version 0.20.0.

  • date_format ({None, 'epoch', 'iso'}) – Type of date conversion. ‘epoch’ = epoch milliseconds, ‘iso’ = ISO8601. The default depends on the orient. For orient='table', the default is ‘iso’. For all other orients, the default is ‘epoch’.

  • double_precision (int, default 10) – The number of decimal places to use when encoding floating point values.

  • force_ascii (bool, default True) – Force encoded string to be ASCII.

  • date_unit (str, default 'ms' (milliseconds)) – The time unit to encode to, governs timestamp and ISO8601 precision. One of ‘s’, ‘ms’, ‘us’, ‘ns’ for second, millisecond, microsecond, and nanosecond respectively.

  • default_handler (callable, default None) – Handler to call if object cannot otherwise be converted to a suitable format for JSON. Should receive a single argument which is the object to convert and return a serialisable object.

  • lines (bool, default False) – If ‘orient’ is ‘records’ write out line delimited json format. Will throw ValueError if incorrect ‘orient’ since others are not list like.

  • compression ({'infer', 'gzip', 'bz2', 'zip', 'xz', None}) –

    A string representing the compression to use in the output file, only used when the first argument is a filename. By default, the compression is inferred from the filename.

    Changed in version 0.24.0: ‘infer’ option added and set to default

  • index (bool, default True) –

    Whether to include the index values in the JSON string. Not including the index (index=False) is only supported when orient is ‘split’ or ‘table’.

    New in version 0.23.0.

  • indent (int, optional) –

    Length of whitespace used to indent each record.

    New in version 1.0.0.

Returns

If path_or_buf is None, returns the resulting json format as a string. Otherwise returns None.

Return type

None or str

See also

read_json()

Convert a JSON string to pandas object.

Notes

The behavior of indent=0 varies from the stdlib, which does not indent the output but does insert newlines. Currently, indent=0 and the default indent=None are equivalent in pandas, though this may change in a future release.

Examples

>>> import json
>>> df = pd.DataFrame(
...     [["a", "b"], ["c", "d"]],
...     index=["row 1", "row 2"],
...     columns=["col 1", "col 2"],
... )
>>> result = df.to_json(orient="split")
>>> parsed = json.loads(result)
>>> json.dumps(parsed, indent=4)  
{
    "columns": [
        "col 1",
        "col 2"
    ],
    "index": [
        "row 1",
        "row 2"
    ],
    "data": [
        [
            "a",
            "b"
        ],
        [
            "c",
            "d"
        ]
    ]
}

Encoding/decoding a Dataframe using 'records' formatted JSON. Note that index labels are not preserved with this encoding.

>>> result = df.to_json(orient="records")
>>> parsed = json.loads(result)
>>> json.dumps(parsed, indent=4)  
[
    {
        "col 1": "a",
        "col 2": "b"
    },
    {
        "col 1": "c",
        "col 2": "d"
    }
]

Encoding/decoding a Dataframe using 'index' formatted JSON:

>>> result = df.to_json(orient="index")
>>> parsed = json.loads(result)
>>> json.dumps(parsed, indent=4)  
{
    "row 1": {
        "col 1": "a",
        "col 2": "b"
    },
    "row 2": {
        "col 1": "c",
        "col 2": "d"
    }
}

Encoding/decoding a Dataframe using 'columns' formatted JSON:

>>> result = df.to_json(orient="columns")
>>> parsed = json.loads(result)
>>> json.dumps(parsed, indent=4)  
{
    "col 1": {
        "row 1": "a",
        "row 2": "c"
    },
    "col 2": {
        "row 1": "b",
        "row 2": "d"
    }
}

Encoding/decoding a Dataframe using 'values' formatted JSON:

>>> result = df.to_json(orient="values")
>>> parsed = json.loads(result)
>>> json.dumps(parsed, indent=4)  
[
    [
        "a",
        "b"
    ],
    [
        "c",
        "d"
    ]
]

Encoding with Table Schema:

>>> result = df.to_json(orient="table")
>>> parsed = json.loads(result)
>>> json.dumps(parsed, indent=4)  
{
    "schema": {
        "fields": [
            {
                "name": "index",
                "type": "string"
            },
            {
                "name": "col 1",
                "type": "string"
            },
            {
                "name": "col 2",
                "type": "string"
            }
        ],
        "primaryKey": [
            "index"
        ],
        "pandas_version": "0.20.0"
    },
    "data": [
        {
            "index": "row 1",
            "col 1": "a",
            "col 2": "b"
        },
        {
            "index": "row 2",
            "col 1": "c",
            "col 2": "d"
        }
    ]
}
to_latex(buf=None, columns=None, col_space=None, header=True, index=True, na_rep='NaN', formatters=None, float_format=None, sparsify=None, index_names=True, bold_rows=False, column_format=None, longtable=None, escape=None, encoding=None, decimal='.', multicolumn=None, multicolumn_format=None, multirow=None, caption=None, label=None)

Render object to a LaTeX tabular, longtable, or nested table/tabular.

Requires \usepackage{booktabs}. The output can be copy/pasted into a main LaTeX document or read from an external file with \input{table.tex}.

Changed in version 0.20.2: Added to Series.

Changed in version 1.0.0: Added caption and label arguments.

Parameters
  • buf (str, Path or StringIO-like, optional, default None) – Buffer to write to. If None, the output is returned as a string.

  • columns (list of label, optional) – The subset of columns to write. Writes all columns by default.

  • col_space (int, optional) – The minimum width of each column.

  • header (bool or list of str, default True) – Write out the column names. If a list of strings is given, it is assumed to be aliases for the column names.

  • index (bool, default True) – Write row names (index).

  • na_rep (str, default 'NaN') – Missing data representation.

  • formatters (list of functions or dict of {str: function}, optional) – Formatter functions to apply to columns’ elements by position or name. The result of each function must be a unicode string. List must be of length equal to the number of columns.

  • float_format (one-parameter function or str, optional, default None) – Formatter for floating point numbers. For example float_format="%.2f" and float_format="{:0.2f}".format will both result in 0.1234 being formatted as 0.12.

  • sparsify (bool, optional) – Set to False for a DataFrame with a hierarchical index to print every multiindex key at each row. By default, the value will be read from the config module.

  • index_names (bool, default True) – Prints the names of the indexes.

  • bold_rows (bool, default False) – Make the row labels bold in the output.

  • column_format (str, optional) – The columns format as specified in LaTeX table format e.g. ‘rcl’ for 3 columns. By default, ‘l’ will be used for all columns except columns of numbers, which default to ‘r’.

  • longtable (bool, optional) – By default, the value will be read from the pandas config module. Use a longtable environment instead of tabular. Requires adding a usepackage{longtable} to your LaTeX preamble.

  • escape (bool, optional) – By default, the value will be read from the pandas config module. When set to False prevents from escaping latex special characters in column names.

  • encoding (str, optional) – A string representing the encoding to use in the output file, defaults to ‘utf-8’.

  • decimal (str, default '.') – Character recognized as decimal separator, e.g. ‘,’ in Europe.

  • multicolumn (bool, default True) – Use multicolumn to enhance MultiIndex columns. The default will be read from the config module.

  • multicolumn_format (str, default 'l') – The alignment for multicolumns, similar to column_format The default will be read from the config module.

  • multirow (bool, default False) – Use multirow to enhance MultiIndex rows. Requires adding a usepackage{multirow} to your LaTeX preamble. Will print centered labels (instead of top-aligned) across the contained rows, separating groups via clines. The default will be read from the pandas config module.

  • caption (str, optional) –

    The LaTeX caption to be placed inside \caption{} in the output.

    New in version 1.0.0.

  • label (str, optional) –

    The LaTeX label to be placed inside \label{} in the output. This is used with \ref{} in the main .tex file.

    New in version 1.0.0.

Returns

If buf is None, returns the result as a string. Otherwise returns None.

Return type

str or None

See also

DataFrame.to_string()

Render a DataFrame to a console-friendly tabular output.

DataFrame.to_html()

Render a DataFrame as an HTML table.

Examples

>>> df = pd.DataFrame({'name': ['Raphael', 'Donatello'],
...                    'mask': ['red', 'purple'],
...                    'weapon': ['sai', 'bo staff']})
>>> print(df.to_latex(index=False))  
\begin{tabular}{lll}
 \toprule
       name &    mask &    weapon \\
 \midrule
    Raphael &     red &       sai \\
  Donatello &  purple &  bo staff \\
\bottomrule
\end{tabular}
to_markdown(buf: Optional[IO[str]] = None, mode: Optional[str] = None, index: bool = True, **kwargs) → Optional[str]

Print DataFrame in Markdown-friendly format.

New in version 1.0.0.

Parameters
  • buf (str, Path or StringIO-like, optional, default None) – Buffer to write to. If None, the output is returned as a string.

  • mode (str, optional) – Mode in which file is opened.

  • index (bool, optional, default True) –

    Add index (row) labels.

    New in version 1.1.0.

  • **kwargs – These parameters will be passed to tabulate.

Returns

DataFrame in Markdown-friendly format.

Return type

str

Examples

>>> s = pd.Series(["elk", "pig", "dog", "quetzal"], name="animal")
>>> print(s.to_markdown())
|    | animal   |
|---:|:---------|
|  0 | elk      |
|  1 | pig      |
|  2 | dog      |
|  3 | quetzal  |

Output markdown with a tabulate option.

>>> print(s.to_markdown(tablefmt="grid"))
+----+----------+
|    | animal   |
+====+==========+
|  0 | elk      |
+----+----------+
|  1 | pig      |
+----+----------+
|  2 | dog      |
+----+----------+
|  3 | quetzal  |
+----+----------+
to_numpy(dtype=None, copy: bool = False, na_value=<object object>) → numpy.ndarray

Convert the DataFrame to a NumPy array.

New in version 0.24.0.

By default, the dtype of the returned array will be the common NumPy dtype of all types in the DataFrame. For example, if the dtypes are float16 and float32, the results dtype will be float32. This may require copying data and coercing values, which may be expensive.

Parameters
  • dtype (str or numpy.dtype, optional) – The dtype to pass to numpy.asarray().

  • copy (bool, default False) – Whether to ensure that the returned value is not a view on another array. Note that copy=False does not ensure that to_numpy() is no-copy. Rather, copy=True ensure that a copy is made, even if not strictly necessary.

  • na_value (Any, optional) –

    The value to use for missing values. The default value depends on dtype and the dtypes of the DataFrame columns.

    New in version 1.1.0.

Returns

Return type

numpy.ndarray

See also

Series.to_numpy()

Similar method for Series.

Examples

>>> pd.DataFrame({"A": [1, 2], "B": [3, 4]}).to_numpy()
array([[1, 3],
       [2, 4]])

With heterogeneous data, the lowest common type will have to be used.

>>> df = pd.DataFrame({"A": [1, 2], "B": [3.0, 4.5]})
>>> df.to_numpy()
array([[1. , 3. ],
       [2. , 4.5]])

For a mix of numeric and non-numeric types, the output array will have object dtype.

>>> df['C'] = pd.date_range('2000', periods=2)
>>> df.to_numpy()
array([[1, 3.0, Timestamp('2000-01-01 00:00:00')],
       [2, 4.5, Timestamp('2000-01-02 00:00:00')]], dtype=object)
to_parquet(path: Union[str, pathlib.Path, IO[AnyStr]], engine: str = 'auto', compression: Optional[str] = 'snappy', index: Optional[bool] = None, partition_cols: Optional[List[str]] = None, **kwargs) → None

Write a DataFrame to the binary parquet format.

This function writes the dataframe as a parquet file. You can choose different parquet backends, and have the option of compression. See the user guide for more details.

Parameters
  • path (str or file-like object) –

    If a string, it will be used as Root Directory path when writing a partitioned dataset. By file-like object, we refer to objects with a write() method, such as a file handler (e.g. via builtin open function) or io.BytesIO. The engine fastparquet does not accept file-like objects.

    Changed in version 1.0.0.

    Previously this was “fname”

  • engine ({'auto', 'pyarrow', 'fastparquet'}, default 'auto') – Parquet library to use. If ‘auto’, then the option io.parquet.engine is used. The default io.parquet.engine behavior is to try ‘pyarrow’, falling back to ‘fastparquet’ if ‘pyarrow’ is unavailable.

  • compression ({'snappy', 'gzip', 'brotli', None}, default 'snappy') – Name of the compression to use. Use None for no compression.

  • index (bool, default None) –

    If True, include the dataframe’s index(es) in the file output. If False, they will not be written to the file. If None, similar to True the dataframe’s index(es) will be saved. However, instead of being saved as values, the RangeIndex will be stored as a range in the metadata so it doesn’t require much space and is faster. Other indexes will be included as columns in the file output.

    New in version 0.24.0.

  • partition_cols (list, optional, default None) –

    Column names by which to partition the dataset. Columns are partitioned in the order they are given. Must be None if path is not a string.

    New in version 0.24.0.

  • **kwargs – Additional arguments passed to the parquet library. See pandas io for more details.

See also

read_parquet()

Read a parquet file.

DataFrame.to_csv()

Write a csv file.

DataFrame.to_sql()

Write to a sql table.

DataFrame.to_hdf()

Write to hdf.

Notes

This function requires either the fastparquet or pyarrow library.

Examples

>>> df = pd.DataFrame(data={'col1': [1, 2], 'col2': [3, 4]})
>>> df.to_parquet('df.parquet.gzip',
...               compression='gzip')  
>>> pd.read_parquet('df.parquet.gzip')  
   col1  col2
0     1     3
1     2     4

If you want to get a buffer to the parquet content you can use a io.BytesIO object, as long as you don’t use partition_cols, which creates multiple files.

>>> import io
>>> f = io.BytesIO()
>>> df.to_parquet(f)
>>> f.seek(0)
0
>>> content = f.read()
to_period(freq=None, axis: Union[str, int] = 0, copy: bool = True) → pandas.core.frame.DataFrame

Convert DataFrame from DatetimeIndex to PeriodIndex.

Convert DataFrame from DatetimeIndex to PeriodIndex with desired frequency (inferred from index if not passed).

Parameters
  • freq (str, default) – Frequency of the PeriodIndex.

  • axis ({0 or 'index', 1 or 'columns'}, default 0) – The axis to convert (the index by default).

  • copy (bool, default True) – If False then underlying input data is not copied.

Returns

Return type

DataFrame with PeriodIndex

to_pickle(path, compression: Optional[str] = 'infer', protocol: int = 5) → None

Pickle (serialize) object to file.

Parameters
  • path (str) – File path where the pickled object will be stored.

  • compression ({'infer', 'gzip', 'bz2', 'zip', 'xz', None}, default 'infer') – A string representing the compression to use in the output file. By default, infers from the file extension in specified path.

  • protocol (int) –

    Int which indicates which protocol should be used by the pickler, default HIGHEST_PROTOCOL (see [1]_ paragraph 12.1.2). The possible values are 0, 1, 2, 3, 4. A negative value for the protocol parameter is equivalent to setting its value to HIGHEST_PROTOCOL.

    1

    https://docs.python.org/3/library/pickle.html.

See also

read_pickle()

Load pickled pandas object (or any object) from file.

DataFrame.to_hdf()

Write DataFrame to an HDF5 file.

DataFrame.to_sql()

Write DataFrame to a SQL database.

DataFrame.to_parquet()

Write a DataFrame to the binary parquet format.

Examples

>>> original_df = pd.DataFrame({"foo": range(5), "bar": range(5, 10)})
>>> original_df
   foo  bar
0    0    5
1    1    6
2    2    7
3    3    8
4    4    9
>>> original_df.to_pickle("./dummy.pkl")
>>> unpickled_df = pd.read_pickle("./dummy.pkl")
>>> unpickled_df
   foo  bar
0    0    5
1    1    6
2    2    7
3    3    8
4    4    9
>>> import os
>>> os.remove("./dummy.pkl")
to_records(index=True, column_dtypes=None, index_dtypes=None) → numpy.recarray

Convert DataFrame to a NumPy record array.

Index will be included as the first field of the record array if requested.

Parameters
  • index (bool, default True) – Include index in resulting record array, stored in ‘index’ field or using the index label, if set.

  • column_dtypes (str, type, dict, default None) –

    New in version 0.24.0.

    If a string or type, the data type to store all columns. If a dictionary, a mapping of column names and indices (zero-indexed) to specific data types.

  • index_dtypes (str, type, dict, default None) –

    New in version 0.24.0.

    If a string or type, the data type to store all index levels. If a dictionary, a mapping of index level names and indices (zero-indexed) to specific data types.

    This mapping is applied only if index=True.

Returns

NumPy ndarray with the DataFrame labels as fields and each row of the DataFrame as entries.

Return type

numpy.recarray

See also

DataFrame.from_records()

Convert structured or record ndarray to DataFrame.

numpy.recarray()

An ndarray that allows field access using attributes, analogous to typed columns in a spreadsheet.

Examples

>>> df = pd.DataFrame({'A': [1, 2], 'B': [0.5, 0.75]},
...                   index=['a', 'b'])
>>> df
   A     B
a  1  0.50
b  2  0.75
>>> df.to_records()
rec.array([('a', 1, 0.5 ), ('b', 2, 0.75)],
          dtype=[('index', 'O'), ('A', '<i8'), ('B', '<f8')])

If the DataFrame index has no label then the recarray field name is set to ‘index’. If the index has a label then this is used as the field name:

>>> df.index = df.index.rename("I")
>>> df.to_records()
rec.array([('a', 1, 0.5 ), ('b', 2, 0.75)],
          dtype=[('I', 'O'), ('A', '<i8'), ('B', '<f8')])

The index can be excluded from the record array:

>>> df.to_records(index=False)
rec.array([(1, 0.5 ), (2, 0.75)],
          dtype=[('A', '<i8'), ('B', '<f8')])

Data types can be specified for the columns:

>>> df.to_records(column_dtypes={"A": "int32"})
rec.array([('a', 1, 0.5 ), ('b', 2, 0.75)],
          dtype=[('I', 'O'), ('A', '<i4'), ('B', '<f8')])

As well as for the index:

>>> df.to_records(index_dtypes="<S2")
rec.array([(b'a', 1, 0.5 ), (b'b', 2, 0.75)],
          dtype=[('I', 'S2'), ('A', '<i8'), ('B', '<f8')])
>>> index_dtypes = f"<S{df.index.str.len().max()}"
>>> df.to_records(index_dtypes=index_dtypes)
rec.array([(b'a', 1, 0.5 ), (b'b', 2, 0.75)],
          dtype=[('I', 'S1'), ('A', '<i8'), ('B', '<f8')])
to_sql(name: str, con, schema=None, if_exists: str = 'fail', index: bool = True, index_label=None, chunksize=None, dtype=None, method=None) → None

Write records stored in a DataFrame to a SQL database.

Databases supported by SQLAlchemy [1]_ are supported. Tables can be newly created, appended to, or overwritten.

Parameters
  • name (str) – Name of SQL table.

  • con (sqlalchemy.engine.(Engine or Connection) or sqlite3.Connection) – Using SQLAlchemy makes it possible to use any DB supported by that library. Legacy support is provided for sqlite3.Connection objects. The user is responsible for engine disposal and connection closure for the SQLAlchemy connectable See here.

  • schema (str, optional) – Specify the schema (if database flavor supports this). If None, use default schema.

  • if_exists ({'fail', 'replace', 'append'}, default 'fail') –

    How to behave if the table already exists.

    • fail: Raise a ValueError.

    • replace: Drop the table before inserting new values.

    • append: Insert new values to the existing table.

  • index (bool, default True) – Write DataFrame index as a column. Uses index_label as the column name in the table.

  • index_label (str or sequence, default None) – Column label for index column(s). If None is given (default) and index is True, then the index names are used. A sequence should be given if the DataFrame uses MultiIndex.

  • chunksize (int, optional) – Specify the number of rows in each batch to be written at a time. By default, all rows will be written at once.

  • dtype (dict or scalar, optional) – Specifying the datatype for columns. If a dictionary is used, the keys should be the column names and the values should be the SQLAlchemy types or strings for the sqlite3 legacy mode. If a scalar is provided, it will be applied to all columns.

  • method ({None, 'multi', callable}, optional) –

    Controls the SQL insertion clause used:

    • None : Uses standard SQL INSERT clause (one per row).

    • ’multi’: Pass multiple values in a single INSERT clause.

    • callable with signature (pd_table, conn, keys, data_iter).

    Details and a sample callable implementation can be found in the section insert method.

    New in version 0.24.0.

Raises

ValueError – When the table already exists and if_exists is ‘fail’ (the default).

See also

read_sql()

Read a DataFrame from a table.

Notes

Timezone aware datetime columns will be written as Timestamp with timezone type with SQLAlchemy if supported by the database. Otherwise, the datetimes will be stored as timezone unaware timestamps local to the original timezone.

New in version 0.24.0.

References

1

https://docs.sqlalchemy.org

2

https://www.python.org/dev/peps/pep-0249/

Examples

Create an in-memory SQLite database.

>>> from sqlalchemy import create_engine
>>> engine = create_engine('sqlite://', echo=False)

Create a table from scratch with 3 rows.

>>> df = pd.DataFrame({'name' : ['User 1', 'User 2', 'User 3']})
>>> df
     name
0  User 1
1  User 2
2  User 3
>>> df.to_sql('users', con=engine)
>>> engine.execute("SELECT * FROM users").fetchall()
[(0, 'User 1'), (1, 'User 2'), (2, 'User 3')]

An sqlalchemy.engine.Connection can also be passed to to con: >>> with engine.begin() as connection: … df1 = pd.DataFrame({‘name’ : [‘User 4’, ‘User 5’]}) … df1.to_sql(‘users’, con=connection, if_exists=’append’)

This is allowed to support operations that require that the same DBAPI connection is used for the entire operation.

>>> df2 = pd.DataFrame({'name' : ['User 6', 'User 7']})
>>> df2.to_sql('users', con=engine, if_exists='append')
>>> engine.execute("SELECT * FROM users").fetchall()
[(0, 'User 1'), (1, 'User 2'), (2, 'User 3'),
 (0, 'User 4'), (1, 'User 5'), (0, 'User 6'),
 (1, 'User 7')]

Overwrite the table with just df2.

>>> df2.to_sql('users', con=engine, if_exists='replace',
...            index_label='id')
>>> engine.execute("SELECT * FROM users").fetchall()
[(0, 'User 6'), (1, 'User 7')]

Specify the dtype (especially useful for integers with missing values). Notice that while pandas is forced to store the data as floating point, the database supports nullable integers. When fetching the data with Python, we get back integer scalars.

>>> df = pd.DataFrame({"A": [1, None, 2]})
>>> df
     A
0  1.0
1  NaN
2  2.0
>>> from sqlalchemy.types import Integer
>>> df.to_sql('integers', con=engine, index=False,
...           dtype={"A": Integer()})
>>> engine.execute("SELECT * FROM integers").fetchall()
[(1,), (None,), (2,)]
to_stata(path: Union[str, pathlib.Path, IO[AnyStr]], convert_dates: Optional[Dict[Optional[Hashable], str]] = None, write_index: bool = True, byteorder: Optional[str] = None, time_stamp: Optional[datetime.datetime] = None, data_label: Optional[str] = None, variable_labels: Optional[Dict[Optional[Hashable], str]] = None, version: Optional[int] = 114, convert_strl: Optional[Sequence[Optional[Hashable]]] = None, compression: Union[str, Mapping[str, str], None] = 'infer') → None

Export DataFrame object to Stata dta format.

Writes the DataFrame to a Stata dataset file. “dta” files contain a Stata dataset.

Parameters
  • path (str, buffer or path object) –

    String, path object (pathlib.Path or py._path.local.LocalPath) or object implementing a binary write() function. If using a buffer then the buffer will not be automatically closed after the file data has been written.

    Changed in version 1.0.0.

    Previously this was “fname”

  • convert_dates (dict) – Dictionary mapping columns containing datetime types to stata internal format to use when writing the dates. Options are ‘tc’, ‘td’, ‘tm’, ‘tw’, ‘th’, ‘tq’, ‘ty’. Column can be either an integer or a name. Datetime columns that do not have a conversion type specified will be converted to ‘tc’. Raises NotImplementedError if a datetime column has timezone information.

  • write_index (bool) – Write the index to Stata dataset.

  • byteorder (str) – Can be “>”, “<”, “little”, or “big”. default is sys.byteorder.

  • time_stamp (datetime) – A datetime to use as file creation date. Default is the current time.

  • data_label (str, optional) – A label for the data set. Must be 80 characters or smaller.

  • variable_labels (dict) – Dictionary containing columns as keys and variable labels as values. Each label must be 80 characters or smaller.

  • version ({114, 117, 118, 119, None}, default 114) –

    Version to use in the output dta file. Set to None to let pandas decide between 118 or 119 formats depending on the number of columns in the frame. Version 114 can be read by Stata 10 and later. Version 117 can be read by Stata 13 or later. Version 118 is supported in Stata 14 and later. Version 119 is supported in Stata 15 and later. Version 114 limits string variables to 244 characters or fewer while versions 117 and later allow strings with lengths up to 2,000,000 characters. Versions 118 and 119 support Unicode characters, and version 119 supports more than 32,767 variables.

    New in version 0.23.0.

    Changed in version 1.0.0: Added support for formats 118 and 119.

  • convert_strl (list, optional) –

    List of column names to convert to string columns to Stata StrL format. Only available if version is 117. Storing strings in the StrL format can produce smaller dta files if strings have more than 8 characters and values are repeated.

    New in version 0.23.0.

  • compression (str or dict, default 'infer') –

    For on-the-fly compression of the output dta. If string, specifies compression mode. If dict, value at key ‘method’ specifies compression mode. Compression mode must be one of {‘infer’, ‘gzip’, ‘bz2’, ‘zip’, ‘xz’, None}. If compression mode is ‘infer’ and fname is path-like, then detect compression from the following extensions: ‘.gz’, ‘.bz2’, ‘.zip’, or ‘.xz’ (otherwise no compression). If dict and compression mode is one of {‘zip’, ‘gzip’, ‘bz2’}, or inferred as one of the above, other entries passed as additional compression options.

    New in version 1.1.0.

Raises
  • NotImplementedError

    • If datetimes contain timezone information * Column dtype is not representable in Stata

  • ValueError

    • Columns listed in convert_dates are neither datetime64[ns] or datetime.datetime * Column listed in convert_dates is not in DataFrame * Categorical label contains more than 32,000 characters

See also

read_stata()

Import Stata data files.

io.stata.StataWriter()

Low-level writer for Stata data files.

io.stata.StataWriter117()

Low-level writer for version 117 files.

Examples

>>> df = pd.DataFrame({'animal': ['falcon', 'parrot', 'falcon',
...                               'parrot'],
...                    'speed': [350, 18, 361, 15]})
>>> df.to_stata('animals.dta')  
to_string(buf: Union[str, pathlib.Path, IO[str], None] = None, columns: Optional[Sequence[str]] = None, col_space: Optional[int] = None, header: Union[bool, Sequence[str]] = True, index: bool = True, na_rep: str = 'NaN', formatters: Union[List[Callable], Tuple[Callable, ...], Mapping[Union[str, int], Callable], None] = None, float_format: Union[str, Callable, EngFormatter, None] = None, sparsify: Optional[bool] = None, index_names: bool = True, justify: Optional[str] = None, max_rows: Optional[int] = None, min_rows: Optional[int] = None, max_cols: Optional[int] = None, show_dimensions: bool = False, decimal: str = '.', line_width: Optional[int] = None, max_colwidth: Optional[int] = None, encoding: Optional[str] = None) → Optional[str]

Render a DataFrame to a console-friendly tabular output.

Parameters
  • buf (str, Path or StringIO-like, optional, default None) – Buffer to write to. If None, the output is returned as a string.

  • columns (sequence, optional, default None) – The subset of columns to write. Writes all columns by default.

  • col_space (int, list or dict of int, optional) – The minimum width of each column.

  • header (bool or sequence, optional) – Write out the column names. If a list of strings is given, it is assumed to be aliases for the column names.

  • index (bool, optional, default True) – Whether to print index (row) labels.

  • na_rep (str, optional, default 'NaN') – String representation of NAN to use.

  • formatters (list, tuple or dict of one-param. functions, optional) – Formatter functions to apply to columns’ elements by position or name. The result of each function must be a unicode string. List/tuple must be of length equal to the number of columns.

  • float_format (one-parameter function, optional, default None) – Formatter function to apply to columns’ elements if they are floats. The result of this function must be a unicode string.

  • sparsify (bool, optional, default True) – Set to False for a DataFrame with a hierarchical index to print every multiindex key at each row.

  • index_names (bool, optional, default True) – Prints the names of the indexes.

  • justify (str, default None) –

    How to justify the column labels. If None uses the option from the print configuration (controlled by set_option), ‘right’ out of the box. Valid values are

    • left

    • right

    • center

    • justify

    • justify-all

    • start

    • end

    • inherit

    • match-parent

    • initial

    • unset.

  • max_rows (int, optional) – Maximum number of rows to display in the console.

  • min_rows (int, optional) – The number of rows to display in the console in a truncated repr (when number of rows is above max_rows).

  • max_cols (int, optional) – Maximum number of columns to display in the console.

  • show_dimensions (bool, default False) – Display DataFrame dimensions (number of rows by number of columns).

  • decimal (str, default '.') – Character recognized as decimal separator, e.g. ‘,’ in Europe.

  • line_width (int, optional) – Width to wrap a line in characters.

  • max_colwidth (int, optional) –

    Max width to truncate each column in characters. By default, no limit.

    New in version 1.0.0.

  • encoding (str, default "utf-8") –

    Set character encoding.

    New in version 1.0.

Returns

If buf is None, returns the result as a string. Otherwise returns None.

Return type

str or None

See also

to_html()

Convert DataFrame to HTML.

Examples

>>> d = {'col1': [1, 2, 3], 'col2': [4, 5, 6]}
>>> df = pd.DataFrame(d)
>>> print(df.to_string())
   col1  col2
0     1     4
1     2     5
2     3     6
to_timestamp(freq=None, how: str = 'start', axis: Union[str, int] = 0, copy: bool = True) → pandas.core.frame.DataFrame

Cast to DatetimeIndex of timestamps, at beginning of period.

Parameters
  • freq (str, default frequency of PeriodIndex) – Desired frequency.

  • how ({'s', 'e', 'start', 'end'}) – Convention for converting period to timestamp; start of period vs. end.

  • axis ({0 or 'index', 1 or 'columns'}, default 0) – The axis to convert (the index by default).

  • copy (bool, default True) – If False then underlying input data is not copied.

Returns

Return type

DataFrame with DatetimeIndex

to_xarray()

Return an xarray object from the pandas object.

Returns

Data in the pandas structure converted to Dataset if the object is a DataFrame, or a DataArray if the object is a Series.

Return type

xarray.DataArray or xarray.Dataset

See also

DataFrame.to_hdf()

Write DataFrame to an HDF5 file.

DataFrame.to_parquet()

Write a DataFrame to the binary parquet format.

Notes

See the xarray docs

Examples

>>> df = pd.DataFrame([('falcon', 'bird', 389.0, 2),
...                    ('parrot', 'bird', 24.0, 2),
...                    ('lion', 'mammal', 80.5, 4),
...                    ('monkey', 'mammal', np.nan, 4)],
...                   columns=['name', 'class', 'max_speed',
...                            'num_legs'])
>>> df
     name   class  max_speed  num_legs
0  falcon    bird      389.0         2
1  parrot    bird       24.0         2
2    lion  mammal       80.5         4
3  monkey  mammal        NaN         4
>>> df.to_xarray()
<xarray.Dataset>
Dimensions:    (index: 4)
Coordinates:
  * index      (index) int64 0 1 2 3
Data variables:
    name       (index) object 'falcon' 'parrot' 'lion' 'monkey'
    class      (index) object 'bird' 'bird' 'mammal' 'mammal'
    max_speed  (index) float64 389.0 24.0 80.5 nan
    num_legs   (index) int64 2 2 4 4
>>> df['max_speed'].to_xarray()
<xarray.DataArray 'max_speed' (index: 4)>
array([389. ,  24. ,  80.5,   nan])
Coordinates:
  * index    (index) int64 0 1 2 3
>>> dates = pd.to_datetime(['2018-01-01', '2018-01-01',
...                         '2018-01-02', '2018-01-02'])
>>> df_multiindex = pd.DataFrame({'date': dates,
...                               'animal': ['falcon', 'parrot',
...                                          'falcon', 'parrot'],
...                               'speed': [350, 18, 361, 15]})
>>> df_multiindex = df_multiindex.set_index(['date', 'animal'])
>>> df_multiindex
                   speed
date       animal
2018-01-01 falcon    350
           parrot     18
2018-01-02 falcon    361
           parrot     15
>>> df_multiindex.to_xarray()
<xarray.Dataset>
Dimensions:  (animal: 2, date: 2)
Coordinates:
  * date     (date) datetime64[ns] 2018-01-01 2018-01-02
  * animal   (animal) object 'falcon' 'parrot'
Data variables:
    speed    (date, animal) int64 350 18 361 15
transform(func, axis=0, *args, **kwargs) → pandas.core.frame.DataFrame

Call func on self producing a DataFrame with transformed values.

Produced DataFrame will have same axis length as self.

Parameters
  • func (function, str, list or dict) –

    Function to use for transforming the data. If a function, must either work when passed a DataFrame or when passed to DataFrame.apply.

    Accepted combinations are:

    • function

    • string function name

    • list of functions and/or function names, e.g. [np.exp. 'sqrt']

    • dict of axis labels -> functions, function names or list of such.

  • axis ({0 or 'index', 1 or 'columns'}, default 0) – If 0 or ‘index’: apply function to each column. If 1 or ‘columns’: apply function to each row.

  • *args – Positional arguments to pass to func.

  • **kwargs – Keyword arguments to pass to func.

Returns

A DataFrame that must have the same length as self.

Return type

DataFrame

:raises ValueError : If the returned DataFrame has a different length than self.:

See also

DataFrame.agg()

Only perform aggregating type operations.

DataFrame.apply()

Invoke function on a DataFrame.

Examples

>>> df = pd.DataFrame({'A': range(3), 'B': range(1, 4)})
>>> df
   A  B
0  0  1
1  1  2
2  2  3
>>> df.transform(lambda x: x + 1)
   A  B
0  1  2
1  2  3
2  3  4

Even though the resulting DataFrame must have the same length as the input DataFrame, it is possible to provide several input functions:

>>> s = pd.Series(range(3))
>>> s
0    0
1    1
2    2
dtype: int64
>>> s.transform([np.sqrt, np.exp])
       sqrt        exp
0  0.000000   1.000000
1  1.000000   2.718282
2  1.414214   7.389056
transpose(*args, copy: bool = False) → pandas.core.frame.DataFrame

Transpose index and columns.

Reflect the DataFrame over its main diagonal by writing rows as columns and vice-versa. The property T is an accessor to the method transpose().

Parameters
  • *args (tuple, optional) – Accepted for compatibility with NumPy.

  • copy (bool, default False) –

    Whether to copy the data after transposing, even for DataFrames with a single dtype.

    Note that a copy is always required for mixed dtype DataFrames, or for DataFrames with any extension types.

Returns

The transposed DataFrame.

Return type

DataFrame

See also

numpy.transpose()

Permute the dimensions of a given array.

Notes

Transposing a DataFrame with mixed dtypes will result in a homogeneous DataFrame with the object dtype. In such a case, a copy of the data is always made.

Examples

Square DataFrame with homogeneous dtype

>>> d1 = {'col1': [1, 2], 'col2': [3, 4]}
>>> df1 = pd.DataFrame(data=d1)
>>> df1
   col1  col2
0     1     3
1     2     4
>>> df1_transposed = df1.T # or df1.transpose()
>>> df1_transposed
      0  1
col1  1  2
col2  3  4

When the dtype is homogeneous in the original DataFrame, we get a transposed DataFrame with the same dtype:

>>> df1.dtypes
col1    int64
col2    int64
dtype: object
>>> df1_transposed.dtypes
0    int64
1    int64
dtype: object

Non-square DataFrame with mixed dtypes

>>> d2 = {'name': ['Alice', 'Bob'],
...       'score': [9.5, 8],
...       'employed': [False, True],
...       'kids': [0, 0]}
>>> df2 = pd.DataFrame(data=d2)
>>> df2
    name  score  employed  kids
0  Alice    9.5     False     0
1    Bob    8.0      True     0
>>> df2_transposed = df2.T # or df2.transpose()
>>> df2_transposed
              0     1
name      Alice   Bob
score       9.5     8
employed  False  True
kids          0     0

When the DataFrame has mixed dtypes, we get a transposed DataFrame with the object dtype:

>>> df2.dtypes
name         object
score       float64
employed       bool
kids          int64
dtype: object
>>> df2_transposed.dtypes
0    object
1    object
dtype: object
truediv(other, axis='columns', level=None, fill_value=None)

Get Floating division of dataframe and other, element-wise (binary operator truediv).

Equivalent to dataframe / other, but with support to substitute a fill_value for missing data in one of the inputs. With reverse version, rtruediv.

Among flexible wrappers (add, sub, mul, div, mod, pow) to arithmetic operators: +, -, *, /, //, %, **.

Parameters
  • other (scalar, sequence, Series, or DataFrame) – Any single or multiple element data structure, or list-like object.

  • axis ({0 or 'index', 1 or 'columns'}) – Whether to compare by the index (0 or ‘index’) or columns (1 or ‘columns’). For Series input, axis to match Series index on.

  • level (int or label) – Broadcast across a level, matching Index values on the passed MultiIndex level.

  • fill_value (float or None, default None) – Fill existing missing (NaN) values, and any new element needed for successful DataFrame alignment, with this value before computation. If data in both corresponding DataFrame locations is missing the result will be missing.

Returns

Result of the arithmetic operation.

Return type

DataFrame

See also

DataFrame.add()

Add DataFrames.

DataFrame.sub()

Subtract DataFrames.

DataFrame.mul()

Multiply DataFrames.

DataFrame.div()

Divide DataFrames (float division).

DataFrame.truediv()

Divide DataFrames (float division).

DataFrame.floordiv()

Divide DataFrames (integer division).

DataFrame.mod()

Calculate modulo (remainder after division).

DataFrame.pow()

Calculate exponential power.

Notes

Mismatched indices will be unioned together.

Examples

>>> df = pd.DataFrame({'angles': [0, 3, 4],
...                    'degrees': [360, 180, 360]},
...                   index=['circle', 'triangle', 'rectangle'])
>>> df
           angles  degrees
circle          0      360
triangle        3      180
rectangle       4      360

Add a scalar with operator version which return the same results.

>>> df + 1
           angles  degrees
circle          1      361
triangle        4      181
rectangle       5      361
>>> df.add(1)
           angles  degrees
circle          1      361
triangle        4      181
rectangle       5      361

Divide by constant with reverse version.

>>> df.div(10)
           angles  degrees
circle        0.0     36.0
triangle      0.3     18.0
rectangle     0.4     36.0
>>> df.rdiv(10)
             angles   degrees
circle          inf  0.027778
triangle   3.333333  0.055556
rectangle  2.500000  0.027778

Subtract a list and Series by axis with operator version.

>>> df - [1, 2]
           angles  degrees
circle         -1      358
triangle        2      178
rectangle       3      358
>>> df.sub([1, 2], axis='columns')
           angles  degrees
circle         -1      358
triangle        2      178
rectangle       3      358
>>> df.sub(pd.Series([1, 1, 1], index=['circle', 'triangle', 'rectangle']),
...        axis='index')
           angles  degrees
circle         -1      359
triangle        2      179
rectangle       3      359

Multiply a DataFrame of different shape with operator version.

>>> other = pd.DataFrame({'angles': [0, 3, 4]},
...                      index=['circle', 'triangle', 'rectangle'])
>>> other
           angles
circle          0
triangle        3
rectangle       4
>>> df * other
           angles  degrees
circle          0      NaN
triangle        9      NaN
rectangle      16      NaN
>>> df.mul(other, fill_value=0)
           angles  degrees
circle          0      0.0
triangle        9      0.0
rectangle      16      0.0

Divide by a MultiIndex by level.

>>> df_multindex = pd.DataFrame({'angles': [0, 3, 4, 4, 5, 6],
...                              'degrees': [360, 180, 360, 360, 540, 720]},
...                             index=[['A', 'A', 'A', 'B', 'B', 'B'],
...                                    ['circle', 'triangle', 'rectangle',
...                                     'square', 'pentagon', 'hexagon']])
>>> df_multindex
             angles  degrees
A circle          0      360
  triangle        3      180
  rectangle       4      360
B square          4      360
  pentagon        5      540
  hexagon         6      720
>>> df.div(df_multindex, level=1, fill_value=0)
             angles  degrees
A circle        NaN      1.0
  triangle      1.0      1.0
  rectangle     1.0      1.0
B square        0.0      0.0
  pentagon      0.0      0.0
  hexagon       0.0      0.0
truncate(before=None, after=None, axis=None, copy: bool = True) → FrameOrSeries

Truncate a Series or DataFrame before and after some index value.

This is a useful shorthand for boolean indexing based on index values above or below certain thresholds.

Parameters
  • before (date, str, int) – Truncate all rows before this index value.

  • after (date, str, int) – Truncate all rows after this index value.

  • axis ({0 or 'index', 1 or 'columns'}, optional) – Axis to truncate. Truncates the index (rows) by default.

  • copy (bool, default is True,) – Return a copy of the truncated section.

Returns

The truncated Series or DataFrame.

Return type

type of caller

See also

DataFrame.loc()

Select a subset of a DataFrame by label.

DataFrame.iloc()

Select a subset of a DataFrame by position.

Notes

If the index being truncated contains only datetime values, before and after may be specified as strings instead of Timestamps.

Examples

>>> df = pd.DataFrame({'A': ['a', 'b', 'c', 'd', 'e'],
...                    'B': ['f', 'g', 'h', 'i', 'j'],
...                    'C': ['k', 'l', 'm', 'n', 'o']},
...                   index=[1, 2, 3, 4, 5])
>>> df
   A  B  C
1  a  f  k
2  b  g  l
3  c  h  m
4  d  i  n
5  e  j  o
>>> df.truncate(before=2, after=4)
   A  B  C
2  b  g  l
3  c  h  m
4  d  i  n

The columns of a DataFrame can be truncated.

>>> df.truncate(before="A", after="B", axis="columns")
   A  B
1  a  f
2  b  g
3  c  h
4  d  i
5  e  j

For Series, only rows can be truncated.

>>> df['A'].truncate(before=2, after=4)
2    b
3    c
4    d
Name: A, dtype: object

The index values in truncate can be datetimes or string dates.

>>> dates = pd.date_range('2016-01-01', '2016-02-01', freq='s')
>>> df = pd.DataFrame(index=dates, data={'A': 1})
>>> df.tail()
                     A
2016-01-31 23:59:56  1
2016-01-31 23:59:57  1
2016-01-31 23:59:58  1
2016-01-31 23:59:59  1
2016-02-01 00:00:00  1
>>> df.truncate(before=pd.Timestamp('2016-01-05'),
...             after=pd.Timestamp('2016-01-10')).tail()
                     A
2016-01-09 23:59:56  1
2016-01-09 23:59:57  1
2016-01-09 23:59:58  1
2016-01-09 23:59:59  1
2016-01-10 00:00:00  1

Because the index is a DatetimeIndex containing only dates, we can specify before and after as strings. They will be coerced to Timestamps before truncation.

>>> df.truncate('2016-01-05', '2016-01-10').tail()
                     A
2016-01-09 23:59:56  1
2016-01-09 23:59:57  1
2016-01-09 23:59:58  1
2016-01-09 23:59:59  1
2016-01-10 00:00:00  1

Note that truncate assumes a 0 value for any unspecified time component (midnight). This differs from partial string slicing, which returns any partially matching dates.

>>> df.loc['2016-01-05':'2016-01-10', :].tail()
                     A
2016-01-10 23:59:55  1
2016-01-10 23:59:56  1
2016-01-10 23:59:57  1
2016-01-10 23:59:58  1
2016-01-10 23:59:59  1
tshift(periods: int = 1, freq=None, axis: Union[str, int] = 0) → FrameOrSeries

Shift the time index, using the index’s frequency if available.

Deprecated since version 1.1.0: Use shift instead.

Parameters
  • periods (int) – Number of periods to move, can be positive or negative.

  • freq (DateOffset, timedelta, or str, default None) – Increment to use from the tseries module or time rule expressed as a string (e.g. ‘EOM’).

  • axis ({0 or ‘index’, 1 or ‘columns’, None}, default 0) – Corresponds to the axis that contains the Index.

Returns

shifted

Return type

Series/DataFrame

Notes

If freq is not specified then tries to use the freq or inferred_freq attributes of the index. If neither of those attributes exist, a ValueError is thrown

tz_convert(tz, axis=0, level=None, copy: bool = True) → FrameOrSeries

Convert tz-aware axis to target time zone.

Parameters
  • tz (str or tzinfo object) –

  • axis (the axis to convert) –

  • level (int, str, default None) – If axis is a MultiIndex, convert a specific level. Otherwise must be None.

  • copy (bool, default True) – Also make a copy of the underlying data.

Returns

Object with time zone converted axis.

Return type

{klass}

Raises

TypeError – If the axis is tz-naive.

tz_localize(tz, axis=0, level=None, copy: bool = True, ambiguous='raise', nonexistent: str = 'raise') → FrameOrSeries

Localize tz-naive index of a Series or DataFrame to target time zone.

This operation localizes the Index. To localize the values in a timezone-naive Series, use Series.dt.tz_localize().

Parameters
  • tz (str or tzinfo) –

  • axis (the axis to localize) –

  • level (int, str, default None) – If axis ia a MultiIndex, localize a specific level. Otherwise must be None.

  • copy (bool, default True) – Also make a copy of the underlying data.

  • ambiguous ('infer', bool-ndarray, 'NaT', default 'raise') –

    When clocks moved backward due to DST, ambiguous times may arise. For example in Central European Time (UTC+01), when going from 03:00 DST to 02:00 non-DST, 02:30:00 local time occurs both at 00:30:00 UTC and at 01:30:00 UTC. In such a situation, the ambiguous parameter dictates how ambiguous times should be handled.

    • ’infer’ will attempt to infer fall dst-transition hours based on order

    • bool-ndarray where True signifies a DST time, False designates a non-DST time (note that this flag is only applicable for ambiguous times)

    • ’NaT’ will return NaT where there are ambiguous times

    • ’raise’ will raise an AmbiguousTimeError if there are ambiguous times.

  • nonexistent (str, default 'raise') –

    A nonexistent time does not exist in a particular timezone where clocks moved forward due to DST. Valid values are:

    • ’shift_forward’ will shift the nonexistent time forward to the closest existing time

    • ’shift_backward’ will shift the nonexistent time backward to the closest existing time

    • ’NaT’ will return NaT where there are nonexistent times

    • timedelta objects will shift nonexistent times by the timedelta

    • ’raise’ will raise an NonExistentTimeError if there are nonexistent times.

    New in version 0.24.0.

Returns

Same type as the input.

Return type

Series or DataFrame

Raises

TypeError – If the TimeSeries is tz-aware and tz is not None.

Examples

Localize local times:

>>> s = pd.Series([1],
...               index=pd.DatetimeIndex(['2018-09-15 01:30:00']))
>>> s.tz_localize('CET')
2018-09-15 01:30:00+02:00    1
dtype: int64

Be careful with DST changes. When there is sequential data, pandas can infer the DST time:

>>> s = pd.Series(range(7),
...               index=pd.DatetimeIndex(['2018-10-28 01:30:00',
...                                       '2018-10-28 02:00:00',
...                                       '2018-10-28 02:30:00',
...                                       '2018-10-28 02:00:00',
...                                       '2018-10-28 02:30:00',
...                                       '2018-10-28 03:00:00',
...                                       '2018-10-28 03:30:00']))
>>> s.tz_localize('CET', ambiguous='infer')
2018-10-28 01:30:00+02:00    0
2018-10-28 02:00:00+02:00    1
2018-10-28 02:30:00+02:00    2
2018-10-28 02:00:00+01:00    3
2018-10-28 02:30:00+01:00    4
2018-10-28 03:00:00+01:00    5
2018-10-28 03:30:00+01:00    6
dtype: int64

In some cases, inferring the DST is impossible. In such cases, you can pass an ndarray to the ambiguous parameter to set the DST explicitly

>>> s = pd.Series(range(3),
...               index=pd.DatetimeIndex(['2018-10-28 01:20:00',
...                                       '2018-10-28 02:36:00',
...                                       '2018-10-28 03:46:00']))
>>> s.tz_localize('CET', ambiguous=np.array([True, True, False]))
2018-10-28 01:20:00+02:00    0
2018-10-28 02:36:00+02:00    1
2018-10-28 03:46:00+01:00    2
dtype: int64

If the DST transition causes nonexistent times, you can shift these dates forward or backward with a timedelta object or ‘shift_forward’ or ‘shift_backward’.

>>> s = pd.Series(range(2),
...               index=pd.DatetimeIndex(['2015-03-29 02:30:00',
...                                       '2015-03-29 03:30:00']))
>>> s.tz_localize('Europe/Warsaw', nonexistent='shift_forward')
2015-03-29 03:00:00+02:00    0
2015-03-29 03:30:00+02:00    1
dtype: int64
>>> s.tz_localize('Europe/Warsaw', nonexistent='shift_backward')
2015-03-29 01:59:59.999999999+01:00    0
2015-03-29 03:30:00+02:00              1
dtype: int64
>>> s.tz_localize('Europe/Warsaw', nonexistent=pd.Timedelta('1H'))
2015-03-29 03:30:00+02:00    0
2015-03-29 03:30:00+02:00    1
dtype: int64
unstack(level=-1, fill_value=None)

Pivot a level of the (necessarily hierarchical) index labels.

Returns a DataFrame having a new level of column labels whose inner-most level consists of the pivoted index labels.

If the index is not a MultiIndex, the output will be a Series (the analogue of stack when the columns are not a MultiIndex).

Parameters
  • level (int, str, or list of these, default -1 (last level)) – Level(s) of index to unstack, can pass level name.

  • fill_value (int, str or dict) – Replace NaN with this value if the unstack produces missing values.

Returns

Return type

Series or DataFrame

See also

DataFrame.pivot()

Pivot a table based on column values.

DataFrame.stack()

Pivot a level of the column labels (inverse operation from unstack).

Examples

>>> index = pd.MultiIndex.from_tuples([('one', 'a'), ('one', 'b'),
...                                    ('two', 'a'), ('two', 'b')])
>>> s = pd.Series(np.arange(1.0, 5.0), index=index)
>>> s
one  a   1.0
     b   2.0
two  a   3.0
     b   4.0
dtype: float64
>>> s.unstack(level=-1)
     a   b
one  1.0  2.0
two  3.0  4.0
>>> s.unstack(level=0)
   one  two
a  1.0   3.0
b  2.0   4.0
>>> df = s.unstack(level=0)
>>> df.unstack()
one  a  1.0
     b  2.0
two  a  3.0
     b  4.0
dtype: float64
update(other, join='left', overwrite=True, filter_func=None, errors='ignore') → None

Modify in place using non-NA values from another DataFrame.

Aligns on indices. There is no return value.

Parameters
  • other (DataFrame, or object coercible into a DataFrame) – Should have at least one matching index/column label with the original DataFrame. If a Series is passed, its name attribute must be set, and that will be used as the column name to align with the original DataFrame.

  • join ({'left'}, default 'left') – Only left join is implemented, keeping the index and columns of the original object.

  • overwrite (bool, default True) –

    How to handle non-NA values for overlapping keys:

    • True: overwrite original DataFrame’s values with values from other.

    • False: only update values that are NA in the original DataFrame.

  • filter_func (callable(1d-array) -> bool 1d-array, optional) – Can choose to replace values other than NA. Return True for values that should be updated.

  • errors ({'raise', 'ignore'}, default 'ignore') –

    If ‘raise’, will raise a ValueError if the DataFrame and other both contain non-NA data in the same place.

    Changed in version 0.24.0: Changed from raise_conflict=False|True to errors=’ignore’|’raise’.

Returns

None

Return type

method directly changes calling object

Raises
  • ValueError

    • When errors=’raise’ and there’s overlapping non-NA data. * When errors is not either ‘ignore’ or ‘raise’

  • NotImplementedError

    • If join != ‘left’

See also

dict.update()

Similar method for dictionaries.

DataFrame.merge()

For column(s)-on-columns(s) operations.

Examples

>>> df = pd.DataFrame({'A': [1, 2, 3],
...                    'B': [400, 500, 600]})
>>> new_df = pd.DataFrame({'B': [4, 5, 6],
...                        'C': [7, 8, 9]})
>>> df.update(new_df)
>>> df
   A  B
0  1  4
1  2  5
2  3  6

The DataFrame’s length does not increase as a result of the update, only values at matching index/column labels are updated.

>>> df = pd.DataFrame({'A': ['a', 'b', 'c'],
...                    'B': ['x', 'y', 'z']})
>>> new_df = pd.DataFrame({'B': ['d', 'e', 'f', 'g', 'h', 'i']})
>>> df.update(new_df)
>>> df
   A  B
0  a  d
1  b  e
2  c  f

For Series, it’s name attribute must be set.

>>> df = pd.DataFrame({'A': ['a', 'b', 'c'],
...                    'B': ['x', 'y', 'z']})
>>> new_column = pd.Series(['d', 'e'], name='B', index=[0, 2])
>>> df.update(new_column)
>>> df
   A  B
0  a  d
1  b  y
2  c  e
>>> df = pd.DataFrame({'A': ['a', 'b', 'c'],
...                    'B': ['x', 'y', 'z']})
>>> new_df = pd.DataFrame({'B': ['d', 'e']}, index=[1, 2])
>>> df.update(new_df)
>>> df
   A  B
0  a  x
1  b  d
2  c  e

If other contains NaNs the corresponding values are not updated in the original dataframe.

>>> df = pd.DataFrame({'A': [1, 2, 3],
...                    'B': [400, 500, 600]})
>>> new_df = pd.DataFrame({'B': [4, np.nan, 6]})
>>> df.update(new_df)
>>> df
   A      B
0  1    4.0
1  2  500.0
2  3    6.0
value_counts(subset: Optional[Sequence[Optional[Hashable]]] = None, normalize: bool = False, sort: bool = True, ascending: bool = False)

Return a Series containing counts of unique rows in the DataFrame.

New in version 1.1.0.

Parameters
  • subset (list-like, optional) – Columns to use when counting unique combinations.

  • normalize (bool, default False) – Return proportions rather than frequencies.

  • sort (bool, default True) – Sort by frequencies.

  • ascending (bool, default False) – Sort in ascending order.

Returns

Return type

Series

See also

Series.value_counts()

Equivalent method on Series.

Notes

The returned Series will have a MultiIndex with one level per input column. By default, rows that contain any NA values are omitted from the result. By default, the resulting Series will be in descending order so that the first element is the most frequently-occurring row.

Examples

>>> df = pd.DataFrame({'num_legs': [2, 4, 4, 6],
...                    'num_wings': [2, 0, 0, 0]},
...                   index=['falcon', 'dog', 'cat', 'ant'])
>>> df
        num_legs  num_wings
falcon         2          2
dog            4          0
cat            4          0
ant            6          0
>>> df.value_counts()
num_legs  num_wings
4         0            2
6         0            1
2         2            1
dtype: int64
>>> df.value_counts(sort=False)
num_legs  num_wings
2         2            1
4         0            2
6         0            1
dtype: int64
>>> df.value_counts(ascending=True)
num_legs  num_wings
2         2            1
6         0            1
4         0            2
dtype: int64
>>> df.value_counts(normalize=True)
num_legs  num_wings
4         0            0.50
6         0            0.25
2         2            0.25
dtype: float64
property values

Return a Numpy representation of the DataFrame.

Warning

We recommend using DataFrame.to_numpy() instead.

Only the values in the DataFrame will be returned, the axes labels will be removed.

Returns

The values of the DataFrame.

Return type

numpy.ndarray

See also

DataFrame.to_numpy

Recommended alternative to this method.

DataFrame.index

Retrieve the index labels.

DataFrame.columns

Retrieving the column names.

Notes

The dtype will be a lower-common-denominator dtype (implicit upcasting); that is to say if the dtypes (even of numeric types) are mixed, the one that accommodates all will be chosen. Use this with care if you are not dealing with the blocks.

e.g. If the dtypes are float16 and float32, dtype will be upcast to float32. If dtypes are int32 and uint8, dtype will be upcast to int32. By numpy.find_common_type() convention, mixing int64 and uint64 will result in a float64 dtype.

Examples

A DataFrame where all columns are the same type (e.g., int64) results in an array of the same type.

>>> df = pd.DataFrame({'age':    [ 3,  29],
...                    'height': [94, 170],
...                    'weight': [31, 115]})
>>> df
   age  height  weight
0    3      94      31
1   29     170     115
>>> df.dtypes
age       int64
height    int64
weight    int64
dtype: object
>>> df.values
array([[  3,  94,  31],
       [ 29, 170, 115]])

A DataFrame with mixed type columns(e.g., str/object, int64, float32) results in an ndarray of the broadest type that accommodates these mixed types (e.g., object).

>>> df2 = pd.DataFrame([('parrot',   24.0, 'second'),
...                     ('lion',     80.5, 1),
...                     ('monkey', np.nan, None)],
...                   columns=('name', 'max_speed', 'rank'))
>>> df2.dtypes
name          object
max_speed    float64
rank          object
dtype: object
>>> df2.values
array([['parrot', 24.0, 'second'],
       ['lion', 80.5, 1],
       ['monkey', nan, None]], dtype=object)
var(axis=None, skipna=None, level=None, ddof=1, numeric_only=None, **kwargs)

Return unbiased variance over requested axis.

Normalized by N-1 by default. This can be changed using the ddof argument

Parameters
  • axis ({index (0), columns (1)}) –

  • skipna (bool, default True) – Exclude NA/null values. If an entire row/column is NA, the result will be NA.

  • level (int or level name, default None) – If the axis is a MultiIndex (hierarchical), count along a particular level, collapsing into a Series.

  • ddof (int, default 1) – Delta Degrees of Freedom. The divisor used in calculations is N - ddof, where N represents the number of elements.

  • numeric_only (bool, default None) – Include only float, int, boolean columns. If None, will attempt to use everything, then use only numeric data. Not implemented for Series.

Returns

Return type

Series or DataFrame (if level specified)

where(cond, other=nan, inplace=False, axis=None, level=None, errors='raise', try_cast=False)

Replace values where the condition is False.

Parameters
  • cond (bool Series/DataFrame, array-like, or callable) – Where cond is True, keep the original value. Where False, replace with corresponding value from other. If cond is callable, it is computed on the Series/DataFrame and should return boolean Series/DataFrame or array. The callable must not change input Series/DataFrame (though pandas doesn’t check it).

  • other (scalar, Series/DataFrame, or callable) – Entries where cond is False are replaced with corresponding value from other. If other is callable, it is computed on the Series/DataFrame and should return scalar or Series/DataFrame. The callable must not change input Series/DataFrame (though pandas doesn’t check it).

  • inplace (bool, default False) – Whether to perform the operation in place on the data.

  • axis (int, default None) – Alignment axis if needed.

  • level (int, default None) – Alignment level if needed.

  • errors (str, {'raise', 'ignore'}, default 'raise') –

    Note that currently this parameter won’t affect the results and will always coerce to a suitable dtype.

    • ’raise’ : allow exceptions to be raised.

    • ’ignore’ : suppress exceptions. On error return original object.

  • try_cast (bool, default False) – Try to cast the result back to the input type (if possible).

Returns

Return type

Same type as caller

See also

DataFrame.mask()

Return an object of same shape as self.

Notes

The where method is an application of the if-then idiom. For each element in the calling DataFrame, if cond is True the element is used; otherwise the corresponding element from the DataFrame other is used.

The signature for DataFrame.where() differs from numpy.where(). Roughly df1.where(m, df2) is equivalent to np.where(m, df1, df2).

For further details and examples see the where documentation in indexing.

Examples

>>> s = pd.Series(range(5))
>>> s.where(s > 0)
0    NaN
1    1.0
2    2.0
3    3.0
4    4.0
dtype: float64
>>> s.mask(s > 0)
0    0.0
1    NaN
2    NaN
3    NaN
4    NaN
dtype: float64
>>> s.where(s > 1, 10)
0    10
1    10
2    2
3    3
4    4
dtype: int64
>>> df = pd.DataFrame(np.arange(10).reshape(-1, 2), columns=['A', 'B'])
>>> df
   A  B
0  0  1
1  2  3
2  4  5
3  6  7
4  8  9
>>> m = df % 3 == 0
>>> df.where(m, -df)
   A  B
0  0 -1
1 -2  3
2 -4 -5
3  6 -7
4 -8  9
>>> df.where(m, -df) == np.where(m, df, -df)
      A     B
0  True  True
1  True  True
2  True  True
3  True  True
4  True  True
>>> df.where(m, -df) == df.mask(~m, -df)
      A     B
0  True  True
1  True  True
2  True  True
3  True  True
4  True  True
xs(key, axis=0, level=None, drop_level: bool = True)

Return cross-section from the Series/DataFrame.

This method takes a key argument to select data at a particular level of a MultiIndex.

Parameters
  • key (label or tuple of label) – Label contained in the index, or partially in a MultiIndex.

  • axis ({0 or 'index', 1 or 'columns'}, default 0) – Axis to retrieve cross-section on.

  • level (object, defaults to first n levels (n=1 or len(key))) – In case of a key partially contained in a MultiIndex, indicate which levels are used. Levels can be referred by label or position.

  • drop_level (bool, default True) – If False, returns object with same levels as self.

Returns

Cross-section from the original Series or DataFrame corresponding to the selected index levels.

Return type

Series or DataFrame

See also

DataFrame.loc()

Access a group of rows and columns by label(s) or a boolean array.

DataFrame.iloc()

Purely integer-location based indexing for selection by position.

Notes

xs can not be used to set values.

MultiIndex Slicers is a generic way to get/set values on any level or levels. It is a superset of xs functionality, see MultiIndex Slicers.

Examples

>>> d = {'num_legs': [4, 4, 2, 2],
...      'num_wings': [0, 0, 2, 2],
...      'class': ['mammal', 'mammal', 'mammal', 'bird'],
...      'animal': ['cat', 'dog', 'bat', 'penguin'],
...      'locomotion': ['walks', 'walks', 'flies', 'walks']}
>>> df = pd.DataFrame(data=d)
>>> df = df.set_index(['class', 'animal', 'locomotion'])
>>> df
                           num_legs  num_wings
class  animal  locomotion
mammal cat     walks              4          0
       dog     walks              4          0
       bat     flies              2          2
bird   penguin walks              2          2

Get values at specified index

>>> df.xs('mammal')
                   num_legs  num_wings
animal locomotion
cat    walks              4          0
dog    walks              4          0
bat    flies              2          2

Get values at several indexes

>>> df.xs(('mammal', 'dog'))
            num_legs  num_wings
locomotion
walks              4          0

Get values at specified index and level

>>> df.xs('cat', level=1)
                   num_legs  num_wings
class  locomotion
mammal walks              4          0

Get values at several indexes and levels

>>> df.xs(('bird', 'walks'),
...       level=[0, 'locomotion'])
         num_legs  num_wings
animal
penguin         2          2

Get values at specified column and axis

>>> df.xs('num_wings', axis=1)
class   animal   locomotion
mammal  cat      walks         0
        dog      walks         0
        bat      flies         2
bird    penguin  walks         2
Name: num_wings, dtype: int64
mdciao.utils.sequence.align_tops_or_seqs(top0, top1, substitutions=None, seq_0_res_idxs=None, seq_1_res_idxs=None, return_DF=True, verbose=False)

Align two sequence-containing objects, i.e. strings and/or Topology objects

Returns a list of n_best AlignmentDataFrame s, an mdciao sub-class of a DataFrame

A list is returned because sometimes there’s more than one alignment with the best possible score (currently it’s limited to 10 alignments)

Relevant methods used under the hood are my_bioalign and alignment_result_to_list_of_dicts, see their docs for more info.

Parameters
  • top0 (:str or obj:~mdtraj.Topology) –

  • top1 (:str or obj:~mdtraj.Topology) –

  • substitutions (dictionary) – dictionary of patterns and replacements, in case some AAs of the topologies

  • seq_0_res_idxs (iterable of integers, default is None) – only use these idxs for alignment in top0

  • seq_1_res_idxs (iterable of integers, default is None) – only use these idxs for alignment in top1

  • return_DF (bool, default is True) – If False, a list of alignment dictionaries instead of AlignmentDataFrame s will be returned

  • verbose (bool, default is False) –

Returns

alignments – These are just normal DataFrames with an extra attribute .alignment_score to be used downstream. If return_DF is False, it’s a list of lists of dicts, see alignment_result_to_list_of_dicts for more info

Return type

list of n_best AlignmentDataFrame s

mdciao.utils.sequence.alignment_result_to_list_of_dicts(ialg, seq_0_res_idxs, seq_1_res_idxs, topology_0=None, topology_1=None, key_AA_code_seq_0='AA_0', key_AA_code_seq_1='AA_1', key_resSeq_seq_0='resSeq_0', key_idx_seq_0='idx_0', key_idx_seq_1='idx_1', key_full_resname_seq_0='fullname_0', key_full_resname_seq_1='fullname_1', verbose=False)

Input an alignment result ialg and return it as a list of per-residue dictionaries with other complementary keys.

This list of dictionaries is very suitable for further operations with pandas.DataFrame.

Parameters
  • ialg (namedtuple) – See return value of obj:my_bioalign for more info

  • topology_0 (mdtraj.Topology object) –

  • seq_0_res_idxs – Zero-indexed residue indices of whatever was in seq_0

  • seq_1_res_idxs – Zero-indexed residue indices of whatever was in seq_1

  • key_AA_code_seq_0 (str, default is AA_0) – The key under which the residues one-letter code will be shown (=the column title in a DataFrame

  • key_AA_code_seq_1 (str, default is AA_1) –

  • key_resSeq_seq_0 (str, default is resSeq_0) –

  • key_idx_seq_1

  • key_full_resname_seq_0

  • seq_0_res_idxs

  • verbose (bool, default is False) –

Returns

alignment_dict – A dictionary containing the aligned sequences with annotated with different information

Return type

dictionary

mdciao.utils.sequence.df2maps(df, allow_nonmatch=True)

Map the columns “idx_0” and “idx_1” of an alignment (a pandas.DataFrame)

Parameters
  • df (pandas.DataFrame) – Typically comes from align_tops_or_seqs

  • allow_nonmatch (bool, default is True) – Allow to map between ranges of residues that don’t match, as long as nonmatching ranges are equal in length, s.t. A A A A B D B D C C C C maps BB to DD

  • first or last ranges will never be (Non-matching) –

  • mapped

Returns

  • top0_to_top1 (dict) – top0_to_top1[10] = 20

  • top1_to_top0 (dict) – top1_to_top0[20] = 10

mdciao.utils.sequence.maptops(top0, top1, allow_nonmatch=False)

map residues between topologies or sequences via their serial indices a sequence alignment

Parameters
  • top0 (Topology or str) –

  • top1 (Topology or str) –

  • allow_nonmatch (bool, default is False) – If true, non-matches of equal length will be considered matches

Returns

  • top0_to_top1 (dict) – top0_to_top1[10] = 20

  • top1_to_top0 (dict) – top1_to_top0[20] = 10

mdciao.utils.sequence.my_bioalign(seq1, seq2, method='global', match=1, mismatch=0, open_gap_score=-1, extend_gap_score=-0.05, n_max=1000)

Align two sequences using Bio.Align.PairwiseAligner

Note

The intention is to only use this method throughout mdciao, and change here any alignment parameters s.t. alignment is done using always the same parameters.

See https://biopython.org/docs/1.75/api/Bio.Align.html?#Bio.Align.PairwiseAligner for more info

Parameters
  • seq1 (str, any length) –

  • seq2 (str, any length) –

  • method (str, default is "global") – Gets passed as argument “mode” to the underlying :obj:~`Bio.Align.PairwiseAligner` At the moment, any other value will raise NotImplementedError.

  • match (int or float, default is 1) – Score value for a match. At the moment, any other value will raise NotImplementedError.

  • mismatch (int or float, default is 0) – Penalty value (non-positve score) for a mismatch. At the moment, any other value will raise NotImplementedError.

  • open_gap_score (int or float, default is -1) – Penalty value (non-positve score) for opening a gap. At the moment, any other value will raise NotImplementedError.

  • extend_gap_score (int or float, default is 0.05) – Penalty value (non-positve score) for extending a gap. At the moment, any other value will raise NotImplementedError.

  • n_max (int, default is 1000) – The maximum number of returned alignments.

Returns

alignments – A list of namedtuples, each containing seq1,seq2,score.

Return type

list

mdciao.utils.sequence.print_verbose_dataframe(df)

Print the full dataframe no matter how big

Parameters

df

mdciao.utils.sequence.re_match_df(df)

Return a copy of an alignment pandas.Dataframe with True ‘match’-values for non-matching blocks that have equal length.

For instance,

A A True A A True B D False B D False C C True C C True

gets re_matched to:

A A True A A True B D True B D True C C True C C True

The input DataFrame is left untouched and only a copy is returned

Parameters

df (pandas.DataFrame) – Typically comes from align_tops_or_seqs

Returns

_df – A re_matched copy of df

Return type

pandas.DataFrame

mdciao.utils.sequence.superpose_w_CA_align(geom, ref, res_indices=None, ref_res_indices=None, verbose=False, allow_nonmatch=False)

Pre align on CA-atoms before calling mdtraj.Trajectory.superpose

Changes geom in place and returns it as well

Parameters
  • geom (Trajectory) –

  • ref (Trajectory) –

  • res_indices (iterable of ints, default is None) – Use only these indices for the sequence alignment

  • ref_res_indices (iterable of ints, default is None) – Use only these indices for the sequence alignment

  • allow_nonmatch (bool, default is True) – Allow to map between ranges of residues that don’t match, as long as nonmatching ranges are equal in length, s.t. A A A A B D B D C C C C maps BB to DD

  • first or last ranges will never be (Non-matching) –

  • mapped

Returns

geom

Return type

Trajectory

mdciao.utils.sequence.top2seq(top, replacement_letter='X')

Return the AA sequence of top as a string

Parameters
  • top (mdtraj.Topology) –

  • replacement_letter (str, default is "X") – If the AA has no one-letter-code, return this letter instead has to be a str of len(1)

Returns

seq

Return type

str of len top.n_residues