Classe « DataFrame »

Méthode pandas.DataFrame.compare

Signature de la méthode compare

def compare(self, other: 'DataFrame', align_axis: 'Axis' = 1, keep_shape: 'bool' = False, keep_equal: 'bool' = False) -> 'DataFrame' 

Description

compare.__doc__

Compare to another DataFrame and show the differences.

.. versionadded:: 1.1.0

Parameters
----------
other : DataFrame
    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_shape : bool, default False
    If true, all rows and columns are kept.
    Otherwise, only the ones with different values are kept.

keep_equal : bool, default False
    If true, the result keeps values that are equal.
    Otherwise, equal values are shown as NaNs.

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

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