# dask.dataframe.DataFrame.ne

DataFrame.ne(other, axis='columns', level=None)

  Get Not equal to of dataframe and other, element-wise (binary operator ne).

  This docstring was copied from pandas.core.frame.DataFrame.ne.

  Some inconsistencies with the Dask version may exist.

  Among flexible wrappers (eq, ne, le, lt, ge, gt) to comparison operators.

  Equivalent to ==, !=, <=, <, >=, > with support to choose axis (rows or columns) and level for comparison.

  Parameters:
    other scalar, sequence, Series, or DataFrame

      Any single or multiple element data structure, or list-like object.

    axis{0 or ‘index’, 1 or ‘columns’}, default ‘columns’

      Whether to compare by the index (0 or ‘index’) or columns (1 or ‘columns’).

    level int or label

      Broadcast across a level, matching Index values on the passed MultiIndex level.

  Returns:
    DataFrame of bool

      Result of the comparison.

  See also

  `DataFrame.eq`

    Compare DataFrames for equality elementwise.

  `DataFrame.ne`

    Compare DataFrames for inequality elementwise.

  `DataFrame.le`

    Compare DataFrames for less than inequality or equality elementwise.

  `DataFrame.lt`

    Compare DataFrames for strictly less than inequality elementwise.

  `DataFrame.ge`

    Compare DataFrames for greater than inequality or equality elementwise.

  `DataFrame.gt`

    Compare DataFrames for strictly greater than inequality elementwise.

  Notes

  Mismatched indices will be unioned together. NaN values are considered different (i.e. NaN != NaN).

  Examples

      >>> df = pd.DataFrame({'cost': [250, 150, 100],
      ...                    'revenue': [100, 250, 300]},
      ...                   index=['A', 'B', 'C'])
      >>> df
         cost  revenue
      A   250      100
      B   150      250
      C   100      300

  Comparison with a scalar, using either the operator or method:

      >>> df == 100
          cost  revenue
      A  False     True
      B  False    False
      C   True    False

      >>> df.eq(100)
          cost  revenue
      A  False     True
      B  False    False
      C   True    False

  When other is a `Series`, the columns of a DataFrame are aligned with the index of other and broadcast:

      >>> df != pd.Series([100, 250], index=["cost", "revenue"])
          cost  revenue
      A   True     True
      B   True    False
      C  False     True

  Use the method to control the broadcast axis:

      >>> df.ne(pd.Series([100, 300], index=["A", "D"]), axis='index')
         cost  revenue
      A  True    False
      B  True     True
      C  True     True
      D  True     True

  When comparing to an arbitrary sequence, the number of columns must match the number elements in other:

      >>> df == [250, 100]
          cost  revenue
      A   True     True
      B  False    False
      C  False    False

  Use the method to control the axis:

      >>> df.eq([250, 250, 100], axis='index')
          cost  revenue
      A   True    False
      B  False     True
      C   True    False

  Compare to a DataFrame of different shape.

      >>> other = pd.DataFrame({'revenue': [300, 250, 100, 150]},
      ...                      index=['A', 'B', 'C', 'D'])
      >>> other
         revenue
      A      300
      B      250
      C      100
      D      150

      >>> df.gt(other)
          cost  revenue
      A  False    False
      B  False    False
      C  False     True
      D  False    False

  Compare to a MultiIndex by level.

      >>> df_multindex = pd.DataFrame({'cost': [250, 150, 100, 150, 300, 220],
      ...                              'revenue': [100, 250, 300, 200, 175, 225]},
      ...                             index=[['Q1', 'Q1', 'Q1', 'Q2', 'Q2', 'Q2'],
      ...                                    ['A', 'B', 'C', 'A', 'B', 'C']])
      >>> df_multindex
            cost  revenue
      Q1 A   250      100
         B   150      250
         C   100      300
      Q2 A   150      200
         B   300      175
         C   220      225

      >>> df.le(df_multindex, level=1)
             cost  revenue
      Q1 A   True     True
         B   True     True
         C   True     True
      Q2 A  False     True
         B   True    False
         C   True    False