modin.pandas.Series.compare¶

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

Compare to another Series and show the differences.

Parameters:

  • other (Series) – Series 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. Otherwise, only keep rows 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 series’s values from the other’s values in the result.

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

Returns:

If axis is 0 or ‘index’ the result will be a Series. The resulting index will be a MultiIndex with ‘self’ and ‘other’ stacked alternately at the inner level.

If axis is 1 or ‘columns’ the result will be a DataFrame. It will have two columns whose names are result_names.

Return type:

Series or Snowpark pandas DataFrame

See also

DataFrame.compare

Show the differences between two DataFrames.

Notes

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

Examples

>>> s1 = pd.Series(["a", "b", "c", "d", "e"])
>>> s2 = pd.Series(["a", "a", "c", "b", "e"])
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Align the differences on columns

>>> s1.compare(s2)
  self other
1    b     a
3    d     b
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