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:
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
>>> 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 None None 3.0 4.0