Classe « DataFrame »

Méthode pandas.DataFrame.stack

Signature de la méthode stack

def stack(self, level=-1, dropna=True) 

Description

stack.__doc__

        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
        -------
        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).

        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