pandas.DataFrame.compare#

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

Compare to another DataFrame and show the differences.

Parameters:

otherDataFrame

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:

DataFrame

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.

Raises:

ValueError

When the two DataFrames don’t have identical labels or shape.

See also

Series.compare

Compare with another Series and show differences.

DataFrame.equals

Test whether two objects contain the same elements.

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