pandas.DataFrame.join

DataFrame.join(other, on=None, how='left', lsuffix='', rsuffix='', sort=False)

Join columns with other DataFrame either on index or on a key column. Efficiently Join multiple DataFrame objects by index at once by passing a list.

Parameters:

other : DataFrame, Series with name field set, or list of DataFrame

Index should be similar to one of the columns in this one. If a Series is passed, its name attribute must be set, and that will be used as the column name in the resulting joined DataFrame

on : name, tuple/list of names, or array-like

Column or index level name(s) in the caller to join on the index in other, otherwise joins index-on-index. If multiple values given, the other DataFrame must have a MultiIndex. Can pass an array as the join key if it is not already contained in the calling DataFrame. Like an Excel VLOOKUP operation

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

How to handle the operation of the two objects.

• left: use calling frame’s index (or column if on is specified)
• right: use other frame’s index
• outer: form union of calling frame’s index (or column if on is specified) with other frame’s index, and sort it lexicographically
• inner: form intersection of calling frame’s index (or column if on is specified) with other frame’s index, preserving the order of the calling’s one

lsuffix : string

Suffix to use from left frame’s overlapping columns

rsuffix : string

Suffix to use from right frame’s overlapping columns

sort : boolean, default False

Order result DataFrame lexicographically by the join key. If False, the order of the join key depends on the join type (how keyword)

Returns:

joined : DataFrame

See also

DataFrame.merge

For column(s)-on-columns(s) operations

Notes

on, lsuffix, and rsuffix options are not supported when passing a list of DataFrame objects

Support for specifying index levels as the on parameter was added in version 0.23.0

Examples

>>> caller = pd.DataFrame({'key': ['K0', 'K1', 'K2', 'K3', 'K4', 'K5'],
...                        'A': ['A0', 'A1', 'A2', 'A3', 'A4', 'A5']})

>>> caller
    A key
0  A0  K0
1  A1  K1
2  A2  K2
3  A3  K3
4  A4  K4
5  A5  K5

>>> other = pd.DataFrame({'key': ['K0', 'K1', 'K2'],
...                       'B': ['B0', 'B1', 'B2']})

>>> other
    B key
0  B0  K0
1  B1  K1
2  B2  K2

Join DataFrames using their indexes.

>>> caller.join(other, lsuffix='_caller', rsuffix='_other')

>>>     A key_caller    B key_other
    0  A0         K0   B0        K0
    1  A1         K1   B1        K1
    2  A2         K2   B2        K2
    3  A3         K3  NaN       NaN
    4  A4         K4  NaN       NaN
    5  A5         K5  NaN       NaN

If we want to join using the key columns, we need to set key to be the index in both caller and other. The joined DataFrame will have key as its index.

>>> caller.set_index('key').join(other.set_index('key'))

>>>      A    B
    key
    K0   A0   B0
    K1   A1   B1
    K2   A2   B2
    K3   A3  NaN
    K4   A4  NaN
    K5   A5  NaN

Another option to join using the key columns is to use the on parameter. DataFrame.join always uses other’s index but we can use any column in the caller. This method preserves the original caller’s index in the result.

>>> caller.join(other.set_index('key'), on='key')

>>>     A key    B
    0  A0  K0   B0
    1  A1  K1   B1
    2  A2  K2   B2
    3  A3  K3  NaN
    4  A4  K4  NaN
    5  A5  K5  NaN