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Classe « DataFrame »

Méthode pandas.DataFrame.drop

Signature de la méthode drop

def drop(self, labels: 'IndexLabel | None' = None, *, axis: 'Axis' = 0, index: 'IndexLabel | None' = None, columns: 'IndexLabel | None' = None, level: 'Level | None' = None, inplace: 'bool' = False, errors: 'IgnoreRaise' = 'raise') -> 'DataFrame | None' 

Description

help(DataFrame.drop)

Drop specified labels from rows or columns.

Remove rows or columns by specifying label names and corresponding
axis, or by directly specifying index or column names. When using a
multi-index, labels on different levels can be removed by specifying
the level. See the :ref:`user guide <advanced.shown_levels>`
for more information about the now unused levels.

Parameters
----------
labels : single label or list-like
    Index or column labels to drop. A tuple will be used as a single
    label and not treated as a list-like.
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
    in place and return None.
errors : {'ignore', 'raise'}, default 'raise'
    If 'ignore', suppress error and only existing labels are
    dropped.

Returns
-------
DataFrame or None
    Returns DataFrame or None DataFrame with the specified
    index or column labels removed or None if inplace=True.

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=[['llama', '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
llama   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

Drop a specific index combination from the MultiIndex
DataFrame, i.e., drop the combination ``'falcon'`` and
``'weight'``, which deletes only the corresponding row

>>> df.drop(index=('falcon', 'weight'))
                big     small
llama   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
        length  0.3     0.2

>>> df.drop(index='cow', columns='small')
                big
llama   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
llama   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

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