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# pandas.DataFrame.lt

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

  Get Greater than of dataframe and other, element-wise (binary operator lt).

  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