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# pandas.Series.drop

Series.drop(labels=None, *, axis=0, index=None, columns=None, level=None, inplace=False, errors='raise')

  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