parameters: (
          self,
          labels=None,
          axis=0,
          index=None,
          columns=None,
          level=None,
          inplace=False,
          errors="raise",
      )
return_statement: return super().drop(
              labels=labels,
              axis=axis,
              index=index,
              columns=columns,
              level=level,
              inplace=inplace,
              errors=errors,
          )
docstring: 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 : 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=[['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
docstring_summary: Return Series with specified index labels removed.
docstring_tokens: Return Series with specified index labels removed .
function: def drop(
          self,
          labels=None,
          axis=0,
          index=None,
          columns=None,
          level=None,
          inplace=False,
          errors="raise",
      ) -> Series:
          """
          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 : 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=[['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
          """
          return super().drop(
              labels=labels,
              axis=axis,
              index=index,
              columns=columns,
              level=level,
              inplace=inplace,
              errors=errors,
          )
function_tokens: def drop ( self , labels = None , axis = 0 , index = None , columns = None , level = None , inplace = False , errors = "raise" , ) -> Series : return super ( ) . drop ( labels = labels , axis = axis , index = index , columns = columns , level = level , inplace = inplace , errors = errors , )