pandas.DataFrame.eq   DataFrame.eq(other, axis='columns', level=None)[source]
 
Get 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
  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