Classe « Series »

Méthode pandas.Series.compare

Signature de la méthode compare

def compare(self, other: 'Series', align_axis: Union[str, int] = 1, keep_shape: bool = False, keep_equal: bool = False) -> Union[ForwardRef('DataFrame'), ForwardRef('Series')] 

Description

compare.__doc__

Compare to another Series and show the differences.

.. versionadded:: 1.1.0

Parameters
----------
other : Series
    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
-------
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.compare : Compare 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