modin.pandas.Series.drop¶
- Series.drop(labels: IndexLabel = None, axis: Axis = 0, index: IndexLabel = None, columns: IndexLabel = None, level: Level = None, inplace: bool = False, errors: IgnoreRaise = 'raise') BasePandasDataset | None [source]¶
Return Series with specified index labels removed.
Remove elements of a Series based on specifying the index labels. When using a MultiIndex, 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 with specified index labels removed or None if
inplace=True
.- Return type:
Snowpark pandas
Series
or None- 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']], ... 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 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