pandas.DataFrame.align#

DataFrame.align(other,join='outer',axis=None,level=None,copy=None,fill_value=None,method=<no_default>,limit=<no_default>,fill_axis=<no_default>,broadcast_axis=<no_default>)[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 thepassed MultiIndex level.

copybool, default True

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

Note

Thecopy keyword will change behavior in pandas 3.0.Copy-on-Writewill be enabled by default, which means that all methods with acopy keyword will use a lazy copy mechanism to defer the copy andignore thecopy keyword. Thecopy keyword will be removed in afuture version of pandas.

You can already get the future behavior and improvements throughenabling copy on writepd.options.mode.copy_on_write=True

fill_valuescalar, 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.

Deprecated since version 2.1.

limitint, default None

If method is specified, this is the maximum number of consecutiveNaN values to forward/backward fill. In other words, if there isa gap with more than this number of consecutive NaNs, it will onlybe partially filled. If method is not specified, this is themaximum number of entries along the entire axis where NaNs will befilled. Must be greater than 0 if not None.

Deprecated since version 2.1.

fill_axis{0 or ‘index’} for Series, {0 or ‘index’, 1 or ‘columns’} for DataFrame, default 0

Filling axis, method and limit.

Deprecated since version 2.1.

broadcast_axis{0 or ‘index’} for Series, {0 or ‘index’, 1 or ‘columns’} for DataFrame, default None

Broadcast values along this axis, if aligning two objects ofdifferent dimensions.

Deprecated since version 2.1.

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  A1  1  2  3  42  6  7  8  9>>>other    A    B    C    D2   10   20   30   403   60   70   80   904  600  700  800  900

Align on columns:

>>>left,right=df.align(other,join="outer",axis=1)>>>left   A  B   C  D  E1  4  2 NaN  1  32  9  7 NaN  6  8>>>right    A    B    C    D   E2   10   20   30   40 NaN3   60   70   80   90 NaN4  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    A1  1.0  2.0  3.0  4.02  6.0  7.0  8.0  9.03  NaN  NaN  NaN  NaN4  NaN  NaN  NaN  NaN>>>right    A      B      C      D1    NaN    NaN    NaN    NaN2   10.0   20.0   30.0   40.03   60.0   70.0   80.0   90.04  600.0  700.0  800.0  900.0

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

>>>left,right=df.align(other,join="outer",axis=None)>>>left     A    B   C    D    E1  4.0  2.0 NaN  1.0  3.02  9.0  7.0 NaN  6.0  8.03  NaN  NaN NaN  NaN  NaN4  NaN  NaN NaN  NaN  NaN>>>right       A      B      C      D   E1    NaN    NaN    NaN    NaN NaN2   10.0   20.0   30.0   40.0 NaN3   60.0   70.0   80.0   90.0 NaN4  600.0  700.0  800.0  900.0 NaN