compare(self, other: 'DataFrame', align_axis: 'Axis' = 1, keep_shape: 'bool' = False, keep_equal: 'bool' = False) -> 'DataFrame'
Matching NaNs will not appear as a difference.
Can only compare identically-labeled (i.e. same shape, identical row and column labels) DataFrames
Object to compare with.
Determine which axis to align the comparison on.
0,or'index'
0, or 'index'
1,or'columns'
1, or 'columns'
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.
When the two DataFrames don't have identical labels or shape.
DataFrame 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.
Compare to another DataFrame and show the differences.
DataFrame.equals
Test whether two objects contain the same elements.
Series.compare
Compare with another Series and show differences.
>>> df = pd.DataFrame(This example is valid syntax, but we were not able to check execution
... {
... "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
This example is valid syntax, but we were not able to check execution>>> df.compare(df2) col1 col3 self other self other 0 a c NaN NaN 2 NaN NaN 3.0 4.0
Stack the differences on rows
This example is valid syntax, but we were not able to check execution>>> 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
This example is valid syntax, but we were not able to check execution>>> 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
This example is valid syntax, but we were not able to check execution>>> 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
This example is valid syntax, but we were not able to check execution>>> 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 5.0See :
The following pages refer to to this document either explicitly or contain code examples using this.
pandas.core.series.Series.compare
Hover to see nodes names; edges to Self not shown, Caped at 50 nodes.
Using a canvas is more power efficient and can get hundred of nodes ; but does not allow hyperlinks; , arrows or text (beyond on hover)
SVG is more flexible but power hungry; and does not scale well to 50 + nodes.
All aboves nodes referred to, (or are referred from) current nodes; Edges from Self to other have been omitted (or all nodes would be connected to the central node "self" which is not useful). Nodes are colored by the library they belong to, and scaled with the number of references pointing them