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. |
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Returns: |
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Raises: |
KeyError If none of the labels are found in the index. |
See also
Series.reindex
Series.dropna
Series.drop_duplicates
DataFrame.drop
>>> 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
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http://pandas.pydata.org/pandas-docs/version/0.23.4/generated/pandas.Series.drop.html