Compare to another DataFrame and show the differences. New in version 1.1.0. Object to compare with. Determine which axis to align the comparison on. with rows drawn alternately from self and other. with columns drawn alternately from self and other. If true, all rows and columns are kept. Otherwise, only the ones with different values are kept. If true, the result keeps values that are equal. Otherwise, equal values are shown as NaNs.
keep_shapebool, default False
Set the dataframes names in the comparison.
New in version 1.5.0.
ReturnsDataFrameDataFrame 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.
RaisesValueErrorWhen the two DataFrames don’t have identical labels or shape.
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