pandas.DataFrame.align   DataFrame.align(other, join='outer', axis=None, level=None, copy=True, fill_value=None, method=None, limit=None, fill_axis=0, broadcast_axis=None)[source]
 
Align two objects on their axes with the specified join method. Join method is specified for each axis Index.  Parameters 
 
other:DataFrame or Series


join:{‘outer’, ‘inner’, ‘left’, ‘right’}, default ‘outer’


axis:allowed axis of the other object, default None


Align on index (0), columns (1), or both (None).  
level:int or level name, default None


Broadcast across a level, matching Index values on the passed MultiIndex level.  
copy:bool, default True


Always returns new objects. If copy=False and no reindexing is required then original objects are returned.  
fill_value:scalar, default np.NaN


Value to use for missing values. Defaults to NaN, but can be any “compatible” value.  
method:{‘backfill’, ‘bfill’, ‘pad’, ‘ffill’, None}, default None


Method to use for filling holes in reindexed Series:  pad / ffill: propagate last valid observation forward to next valid. backfill / bfill: use NEXT valid observation to fill gap.   
limit:int, default None


If method is specified, this is the maximum number of consecutive NaN values to forward/backward fill. In other words, if there is a gap with more than this number of consecutive NaNs, it will only be partially filled. If method is not specified, this is the maximum number of entries along the entire axis where NaNs will be filled. Must be greater than 0 if not None.  
fill_axis:{0 or ‘index’, 1 or ‘columns’}, default 0


Filling axis, method and limit.  
broadcast_axis:{0 or ‘index’, 1 or ‘columns’}, default None


Broadcast values along this axis, if aligning two objects of different dimensions.    Returns 
 
(left, right):(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