pandas.Series.compare#

Series.compare(other, align_axis=1, keep_shape=False, keep_equal=False, result_names=('self', 'other'))[source] #

Compare to another Series and show the differences.

Parameters:

otherSeries

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_shapebool, default False

If true, all rows and columns are kept. Otherwise, only the ones with different values are kept.

keep_equalbool, default False

If true, the result keeps values that are equal. Otherwise, equal values are shown as NaNs.

result_namestuple, default (‘self’, ‘other’)

Set the dataframes names in the comparison.

Added in version 1.5.0.

Returns:

Series or DataFrame

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 namely ‘self’ and ‘other’.

See also

DataFrame.compare

Compare with another DataFrame and show differences.

Notes

Matching NaNs will not appear as a difference.

Examples

>>> s1 = pd.Series(["a", "b", "c", "d", "e"])
>>> s2 = pd.Series(["a", "a", "c", "b", "e"])

Align the differences on columns

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

Stack the differences on indices

>>> s1.compare(s2, align_axis=0)
1 self b
 other a
3 self d
 other b
dtype: object

Keep all original rows

>>> s1.compare(s2, keep_shape=True)
 self other
0 NaN NaN
1 b a
2 NaN NaN
3 d b
4 NaN NaN

Keep all original rows and also all original values

>>> s1.compare(s2, keep_shape=True, keep_equal=True)
 self other
0 a a
1 b a
2 c c
3 d b
4 e e