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Classe « DataFrame »

Méthode pandas.DataFrame.stack

Signature de la méthode stack

def stack(self, level: 'IndexLabel' = -1, dropna: 'bool | lib.NoDefault' = <no_default>, sort: 'bool | lib.NoDefault' = <no_default>, future_stack: 'bool' = False) 

Description

help(DataFrame.stack)

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.
sort : bool, default True
    Whether to sort the levels of the resulting MultiIndex.
future_stack : bool, default False
    Whether to use the new implementation that will replace the current
    implementation in pandas 3.0. When True, dropna and sort have no impact
    on the result and must remain unspecified. See :ref:`pandas 2.1.0 Release
    notes <whatsnew_210.enhancements.new_stack>` for more details.

Returns
-------
DataFrame or Series
    Stacked 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).

Reference :ref:`the user guide <reshaping.stacking>` for more examples.

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(future_stack=True)
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(future_stack=True)
            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(future_stack=True)
        weight  height
cat kg     1.0     NaN
    m      NaN     2.0
dog kg     3.0     NaN
    m      NaN     4.0

**Prescribing the level(s) to be stacked**

The first parameter controls which level or levels are stacked:

>>> df_multi_level_cols2.stack(0, future_stack=True)
             kg    m
cat weight  1.0  NaN
    height  NaN  2.0
dog weight  3.0  NaN
    height  NaN  4.0
>>> df_multi_level_cols2.stack([0, 1], future_stack=True)
cat  weight  kg    1.0
     height  m     2.0
dog  weight  kg    3.0
     height  m     4.0
dtype: float64

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