# 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, 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:

  Series

  > Series with specified index labels removed.

  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

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