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.
New in version 1.1.0.
- 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.
New 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.compareCompare 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