pandas.Series.align#

Series.align(other, join='outer', axis=None, level=None, copy=_NoDefault.no_default, fill_value=None)[source]#

Align two objects on their axes with the specified join method.

Join method is specified for each axis Index.

Parameters:
otherDataFrame or Series
join{‘outer’, ‘inner’, ‘left’, ‘right’}, default ‘outer’

Type of alignment to be performed.

  • left: use only keys from left frame, preserve key order.

  • right: use only keys from right frame, preserve key order.

  • outer: use union of keys from both frames, sort keys lexicographically.

  • inner: use intersection of keys from both frames, preserve the order of the left keys.

axisallowed axis of the other object, default None

Align on index (0), columns (1), or both (None).

levelint or level name, default None

Broadcast across a level, matching Index values on the passed MultiIndex level.

copybool, default False

Always returns new objects. If copy=False and no reindexing is required then original objects are returned.

Note

The copy keyword will change behavior in pandas 3.0. Copy-on-Write will be enabled by default, which means that all methods with a copy keyword will use a lazy copy mechanism to defer the copy and ignore the copy keyword. The copy keyword will be removed in a future version of pandas.

You can already get the future behavior and improvements through enabling copy on write pd.options.mode.copy_on_write = True

Deprecated since version 3.0.0.

fill_valuescalar, default np.nan

Value to use for missing values. Defaults to NaN, but can be any “compatible” value.

Returns:
tuple of (Series/DataFrame, type of other)

Aligned objects.

Examples

>>> df = pd.DataFrame(
...     [[1, 2, 3, 4], [6, 7, 8, 9]], columns=["D", "B", "E", "A"], index=[1, 2]
... )
>>> other = pd.DataFrame(
...     [[10, 20, 30, 40], [60, 70, 80, 90], [600, 700, 800, 900]],
...     columns=["A", "B", "C", "D"],
...     index=[2, 3, 4],
... )
>>> df
   D  B  E  A
1  1  2  3  4
2  6  7  8  9
>>> other
    A    B    C    D
2   10   20   30   40
3   60   70   80   90
4  600  700  800  900

Align on columns:

>>> left, right = df.align(other, join="outer", axis=1)
>>> left
   A  B   C  D  E
1  4  2 NaN  1  3
2  9  7 NaN  6  8
>>> right
    A    B    C    D   E
2   10   20   30   40 NaN
3   60   70   80   90 NaN
4  600  700  800  900 NaN

We can also align on the index:

>>> left, right = df.align(other, join="outer", axis=0)
>>> left
    D    B    E    A
1  1.0  2.0  3.0  4.0
2  6.0  7.0  8.0  9.0
3  NaN  NaN  NaN  NaN
4  NaN  NaN  NaN  NaN
>>> right
    A      B      C      D
1    NaN    NaN    NaN    NaN
2   10.0   20.0   30.0   40.0
3   60.0   70.0   80.0   90.0
4  600.0  700.0  800.0  900.0

Finally, the default axis=None will align on both index and columns:

>>> left, right = df.align(other, join="outer", axis=None)
>>> left
     A    B   C    D    E
1  4.0  2.0 NaN  1.0  3.0
2  9.0  7.0 NaN  6.0  8.0
3  NaN  NaN NaN  NaN  NaN
4  NaN  NaN NaN  NaN  NaN
>>> right
       A      B      C      D   E
1    NaN    NaN    NaN    NaN NaN
2   10.0   20.0   30.0   40.0 NaN
3   60.0   70.0   80.0   90.0 NaN
4  600.0  700.0  800.0  900.0 NaN