modin.pandas.DataFrame.drop

DataFrame.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] (https://github.com/snowflakedb/snowpark-python/blob/v1.26.0/snowpark-python/src/snowflake/snowpark/modin/plugin/extensions/base_overrides.py#L2079-L2119)

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
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Drop labels B en C

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