xorbits.pandas.DataFrame.compare#

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

Compare to another DataFrame and show the differences.

Parameters

  • other (DataFrame) – Object to compare with.

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

    Determine 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.

  • keep_shape (bool, default False) – If true, all rows and columns are kept. Otherwise, only the ones with different values are kept.

  • keep_equal (bool, default False) – If true, the result keeps values that are equal. Otherwise, equal values are shown as NaNs.

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

    Set the dataframes names in the comparison.

    New in version 1.5.0(pandas).

Returns

DataFrame that shows the differences stacked side by side.

The resulting index will be a MultiIndex with ‘self’ and ‘other’ stacked alternately at the inner level.

Return type

DataFrame

Raises

ValueError – When the two DataFrames don’t have identical labels or shape.

See also

Series.compare

Compare with another Series and show differences.

DataFrame.equals

Test whether two objects contain the same elements.

Notes

Matching NaNs will not appear as a difference.

Can only compare identically-labeled (i.e. same shape, identical row and column labels) DataFrames

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

>>> 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

Align the differences on columns

>>> df.compare(df2)  
  col1       col3
  self other self other
0    a     c  NaN   NaN
2  NaN   NaN  3.0   4.0

Assign result_names

>>> df.compare(df2, result_names=("left", "right"))  
  col1       col3
  left right left right
0    a     c  NaN   NaN
2  NaN   NaN  3.0   4.0

Stack the differences on rows

>>> df.compare(df2, align_axis=0)  
        col1  col3
0 self     a   NaN
  other    c   NaN
2 self   NaN   3.0
  other  NaN   4.0

Keep the equal values

>>> df.compare(df2, keep_equal=True)  
  col1       col3
  self other self other
0    a     c  1.0   1.0
2    b     b  3.0   4.0

Keep all original rows and columns

>>> df.compare(df2, keep_shape=True)  
  col1       col2       col3
  self other self other self other
0    a     c  NaN   NaN  NaN   NaN
1  NaN   NaN  NaN   NaN  NaN   NaN
2  NaN   NaN  NaN   NaN  3.0   4.0
3  NaN   NaN  NaN   NaN  NaN   NaN
4  NaN   NaN  NaN   NaN  NaN   NaN

Keep all original rows and columns and also all original values

>>> df.compare(df2, keep_shape=True, keep_equal=True)  
  col1       col2       col3
  self other self other self other
0    a     c  1.0   1.0  1.0   1.0
1    a     a  2.0   2.0  2.0   2.0
2    b     b  3.0   3.0  3.0   4.0
3    b     b  NaN   NaN  4.0   4.0
4    a     a  5.0   5.0  5.0   5.0

Warning

This method has not been implemented yet. Xorbits will try to execute it with pandas.

This docstring was copied from pandas.core.frame.DataFrame.