pandas.Series.drop¶
-
Series.
drop
(labels=None, axis=0, index=None, columns=None, level=None, inplace=False, errors='raise')[source]¶ 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, default 0
Redundant for application on Series.
index, columns : None
Redundant for application on Series, but index can be used instead of labels.
New in version 0.21.0.
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: - dropped : pandas.Series
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=[['lama', 'cow', 'falcon'], ... ['speed', 'weight', 'length']], ... labels=[[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 lama 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) lama speed 45.0 length 1.2 cow speed 30.0 length 1.5 falcon speed 320.0 length 0.3 dtype: float64