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: Drop specified labels from rows or columns.
  
          Remove rows or columns by specifying label names and corresponding
          axis, or by specifying directly index or column names. When using a
          multi-index, labels on different levels can be removed by specifying
          the level.
  
          Parameters
          ----------
          labels : single label or list-like
              Index or column labels to drop.
          axis : {0 or 'index', 1 or 'columns'}, default 0
              Whether to drop labels from the index (0 or 'index') or
              columns (1 or 'columns').
          index : single label or list-like
              Alternative to specifying axis (``labels, axis=0``
              is equivalent to ``index=labels``).
  
              .. versionadded:: 0.21.0
          columns : single label or list-like
              Alternative to specifying axis (``labels, axis=1``
              is equivalent to ``columns=labels``).
  
              .. versionadded:: 0.21.0
          level : int or level name, optional
              For MultiIndex, level from 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
          -------
          DataFrame
              DataFrame without the removed index or column labels.
  
          Raises
          ------
          KeyError
              If any of the labels is not found in the selected axis.
  
          See Also
          --------
          DataFrame.loc : Label-location based indexer for selection by label.
          DataFrame.dropna : Return DataFrame with labels on given axis omitted
              where (all or any) data are missing.
          DataFrame.drop_duplicates : Return DataFrame with duplicate rows
              removed, optionally only considering certain columns.
          Series.drop : Return Series with specified index labels removed.
  
          Examples
          --------
          >>> df = pd.DataFrame(np.arange(12).reshape(3, 4),
          ...                   columns=['A', 'B', 'C', 'D'])
          >>> df
             A  B   C   D
          0  0  1   2   3
          1  4  5   6   7
          2  8  9  10  11
  
          Drop columns
  
          >>> df.drop(['B', 'C'], axis=1)
             A   D
          0  0   3
          1  4   7
          2  8  11
  
          >>> df.drop(columns=['B', 'C'])
             A   D
          0  0   3
          1  4   7
          2  8  11
  
          Drop a row by index
  
          >>> df.drop([0, 1])
             A  B   C   D
          2  8  9  10  11
  
          Drop columns and/or rows of MultiIndex DataFrame
  
          >>> 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]])
          >>> df = pd.DataFrame(index=midx, columns=['big', 'small'],
          ...                   data=[[45, 30], [200, 100], [1.5, 1], [30, 20],
          ...                         [250, 150], [1.5, 0.8], [320, 250],
          ...                         [1, 0.8], [0.3, 0.2]])
          >>> df
                          big     small
          lama    speed   45.0    30.0
                  weight  200.0   100.0
                  length  1.5     1.0
          cow     speed   30.0    20.0
                  weight  250.0   150.0
                  length  1.5     0.8
          falcon  speed   320.0   250.0
                  weight  1.0     0.8
                  length  0.3     0.2
  
          >>> df.drop(index='cow', columns='small')
                          big
          lama    speed   45.0
                  weight  200.0
                  length  1.5
          falcon  speed   320.0
                  weight  1.0
                  length  0.3
  
          >>> df.drop(index='length', level=1)
                          big     small
          lama    speed   45.0    30.0
                  weight  200.0   100.0
          cow     speed   30.0    20.0
                  weight  250.0   150.0
          falcon  speed   320.0   250.0
                  weight  1.0     0.8
docstring_summary: Drop specified labels from rows or columns.
docstring_tokens: Drop specified labels from rows or columns .
function: def drop(
          self,
          labels=None,
          axis=0,
          index=None,
          columns=None,
          level=None,
          inplace=False,
          errors="raise",
      ):
          """
          Drop specified labels from rows or columns.
  
          Remove rows or columns by specifying label names and corresponding
          axis, or by specifying directly index or column names. When using a
          multi-index, labels on different levels can be removed by specifying
          the level.
  
          Parameters
          ----------
          labels : single label or list-like
              Index or column labels to drop.
          axis : {0 or 'index', 1 or 'columns'}, default 0
              Whether to drop labels from the index (0 or 'index') or
              columns (1 or 'columns').
          index : single label or list-like
              Alternative to specifying axis (``labels, axis=0``
              is equivalent to ``index=labels``).
  
              .. versionadded:: 0.21.0
          columns : single label or list-like
              Alternative to specifying axis (``labels, axis=1``
              is equivalent to ``columns=labels``).
  
              .. versionadded:: 0.21.0
          level : int or level name, optional
              For MultiIndex, level from 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
          -------
          DataFrame
              DataFrame without the removed index or column labels.
  
          Raises
          ------
          KeyError
              If any of the labels is not found in the selected axis.
  
          See Also
          --------
          DataFrame.loc : Label-location based indexer for selection by label.
          DataFrame.dropna : Return DataFrame with labels on given axis omitted
              where (all or any) data are missing.
          DataFrame.drop_duplicates : Return DataFrame with duplicate rows
              removed, optionally only considering certain columns.
          Series.drop : Return Series with specified index labels removed.
  
          Examples
          --------
          >>> df = pd.DataFrame(np.arange(12).reshape(3, 4),
          ...                   columns=['A', 'B', 'C', 'D'])
          >>> df
             A  B   C   D
          0  0  1   2   3
          1  4  5   6   7
          2  8  9  10  11
  
          Drop columns
  
          >>> df.drop(['B', 'C'], axis=1)
             A   D
          0  0   3
          1  4   7
          2  8  11
  
          >>> df.drop(columns=['B', 'C'])
             A   D
          0  0   3
          1  4   7
          2  8  11
  
          Drop a row by index
  
          >>> df.drop([0, 1])
             A  B   C   D
          2  8  9  10  11
  
          Drop columns and/or rows of MultiIndex DataFrame
  
          >>> 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]])
          >>> df = pd.DataFrame(index=midx, columns=['big', 'small'],
          ...                   data=[[45, 30], [200, 100], [1.5, 1], [30, 20],
          ...                         [250, 150], [1.5, 0.8], [320, 250],
          ...                         [1, 0.8], [0.3, 0.2]])
          >>> df
                          big     small
          lama    speed   45.0    30.0
                  weight  200.0   100.0
                  length  1.5     1.0
          cow     speed   30.0    20.0
                  weight  250.0   150.0
                  length  1.5     0.8
          falcon  speed   320.0   250.0
                  weight  1.0     0.8
                  length  0.3     0.2
  
          >>> df.drop(index='cow', columns='small')
                          big
          lama    speed   45.0
                  weight  200.0
                  length  1.5
          falcon  speed   320.0
                  weight  1.0
                  length  0.3
  
          >>> df.drop(index='length', level=1)
                          big     small
          lama    speed   45.0    30.0
                  weight  200.0   100.0
          cow     speed   30.0    20.0
                  weight  250.0   150.0
          falcon  speed   320.0   250.0
                  weight  1.0     0.8
          """
          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" , ) : return super ( ) . drop ( labels = labels , axis = axis , index = index , columns = columns , level = level , inplace = inplace , errors = errors , )