pandas.DataFrame.eq

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

Equal to of dataframe and other, element-wise (binary operator eq).

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

Compare to a scalar and operator version which return the same results.

>>> 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

Compare to a list and Series by axis and operator version. As shown, for list axis is by default ‘index’, but for Series axis is by default ‘columns’.

>>> df != [100, 250, 300]
   cost  revenue
A  True    False
B  True    False
C  True    False

>>> df.ne([100, 250, 300], axis='index')
   cost  revenue
A  True    False
B  True    False
C  True    False

>>> df != pd.Series([100, 250, 300])
   cost  revenue     0     1     2
A  True     True  True  True  True
B  True     True  True  True  True
C  True     True  True  True  True

>>> df.ne(pd.Series([100, 250, 300]), axis='columns')
   cost  revenue     0     1     2
A  True     True  True  True  True
B  True     True  True  True  True
C  True     True  True  True  True

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