identify the differences between dataframes in Python

sales2.where(~(sales1==sales2))

###############################################################################
# solution after concatenation:
sales_comp = pd.concat([sales1,sales2], axis = 1, keys = ['Day1','Day2'])

def highlight_diff(data, color='yellow'):
     attr = 'background-color: {}'.format(color)
     other = data.xs('Day1', axis='columns', level=-2)
     return pd.DataFrame(np.where(data.ne(other, level=1), attr, ''),
                         index=data.index, columns=data.columns)

sales_comp.style.apply(highlight_diff, axis=None)


############################################################################
    def diff_pd(df1, df2):
        """Identify differences between two pandas DataFrames"""
        assert (df1.columns == df2.columns).all(), \
            "DataFrame column names are different"
        if any(df1.dtypes != df2.dtypes):
            "Data Types are different, trying to convert"
            df2 = df2.astype(df1.dtypes)
        if df1.equals(df2):
            return None
        else:
            # need to account for np.nan != np.nan returning True
            diff_mask = (df1 != df2) & ~(df1.isnull() & df2.isnull())
            ne_stacked = diff_mask.stack()
            changed = ne_stacked[ne_stacked]
            changed.index.names = ['id', 'col']
            difference_locations = np.where(diff_mask)
            changed_from = df1.values[difference_locations]
            changed_to = df2.values[difference_locations]
            return pd.DataFrame({'from': changed_from, 'to': changed_to},
                                index=changed.index)