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Classe « Series »

Méthode pandas.Series.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') -> 'Series | None' 

Description

help(Series.drop)

Return Series with specified index labels removed.

Remove elements of a Series based on specifying the index labels.
When using a multi-index, labels on different levels can be removed
by specifying the level.

Parameters
----------
labels : single label or list-like
    Index labels to drop.
axis : {0 or 'index'}
    Unused. Parameter needed for compatibility with DataFrame.
index : single label or list-like
    Redundant for application on Series, but 'index' can be used instead
    of 'labels'.
columns : single label or list-like
    No change is made to the Series; use 'index' or 'labels' instead.
level : int or level name, optional
    For MultiIndex, level for which the labels will be removed.
inplace : bool, default False
    If True, do operation inplace and return None.
errors : {'ignore', 'raise'}, default 'raise'
    If 'ignore', suppress error and only existing labels are dropped.

Returns
-------
Series or None
    Series with specified index labels removed or None if ``inplace=True``.

Raises
------
KeyError
    If none of the labels are found in the index.

See Also
--------
Series.reindex : Return only specified index labels of Series.
Series.dropna : Return series without null values.
Series.drop_duplicates : Return Series with duplicate values removed.
DataFrame.drop : Drop specified labels from rows or columns.

Examples
--------
>>> s = pd.Series(data=np.arange(3), index=['A', 'B', 'C'])
>>> s
A  0
B  1
C  2
dtype: int64

Drop labels B en C

>>> s.drop(labels=['B', 'C'])
A  0
dtype: int64

Drop 2nd level label in MultiIndex Series

>>> 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]])
>>> s = pd.Series([45, 200, 1.2, 30, 250, 1.5, 320, 1, 0.3],
...               index=midx)
>>> s
llama   speed      45.0
        weight    200.0
        length      1.2
cow     speed      30.0
        weight    250.0
        length      1.5
falcon  speed     320.0
        weight      1.0
        length      0.3
dtype: float64

>>> s.drop(labels='weight', level=1)
llama   speed      45.0
        length      1.2
cow     speed      30.0
        length      1.5
falcon  speed     320.0
        length      0.3
dtype: float64

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