modin.pandas.DataFrame.compare¶

DataFrame.compare(other, align_axis=1, keep_shape: bool = False, keep_equal: bool = False, result_names=('self', 'other')) → DataFrame[source]¶

Compare to another DataFrame and show the differences.

Parameters:

• other (DataFrame) – DataFrame to compare with.

• align_axis ({{0 or 'index', 1 or 'columns'}}, default 1) –

  Which axis to align the comparison on.

  • 0, or ‘index’Resulting differences are stacked vertically

    with rows drawn alternately from self and other.

  • 1, or ‘columns’Resulting differences are aligned horizontally

    with columns drawn alternately from self and other.

  Snowpark pandas does not yet support 1 / ‘columns’.

• keep_shape (bool, default False) –

  If true, keep all rows and columns. Otherwise, only keep rows and columns with different values.

  Snowpark pandas does not yet support keep_shape = True.

• keep_equal (bool, default False) –

  If true, keep values that are equal. Otherwise, show equal values as nulls.

  Snowpark pandas does not yet support keep_equal = True.

• result_names (tuple, default ('self', 'other')) –

  How to distinguish this dataframe’s values from the other’s values in the result.

  Snowpark pandas does not yet support names other than the default.

Returns:

The result of the comparison.

Return type:

DataFrame

See also

Series.compare

Show the differences between two Series.

DataFrame.equals

Test whether two DataFrames contain the same elements.

Notes

Matching null values, such as None and NaN, will not appear as a difference.

Examples

>>> df = pd.DataFrame(
...     {
...         "col1": ["a", "a", "b", "b", "a"],
...         "col2": [1.0, 2.0, 3.0, np.nan, 5.0],
...         "col3": [1.0, 2.0, 3.0, 4.0, 5.0]
...     },
...     columns=["col1", "col2", "col3"],
... )
>>> df
  col1  col2  col3
0    a   1.0   1.0
1    a   2.0   2.0
2    b   3.0   3.0
3    b   NaN   4.0
4    a   5.0   5.0

Copy

>>> df2 = df.copy()
>>> df2.loc[0, 'col1'] = 'c'
>>> df2.loc[2, 'col3'] = 4.0
>>> df2
  col1  col2  col3
0    c   1.0   1.0
1    a   2.0   2.0
2    b   3.0   4.0
3    b   NaN   4.0
4    a   5.0   5.0

Copy

Align the differences on columns

>>> df.compare(df2) 
   col1       col3
   self other self other
0     a     c  NaN   NaN
2  None  None  3.0   4.0

Copy