Join columns of another DataFrame.
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.
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.
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.
default âleftâ How to handle the operation of the two objects.
left: use calling frameâs index (or column if on is specified)
right: use otherâs index.
outer: form union of calling frameâs index (or column if on is specified) with otherâs index, and sort it lexicographically.
inner: form intersection of calling frameâs index (or column if on is specified) with otherâs index, preserving the order of the callingâs one.
cross: creates the cartesian product from both frames, preserves the order of the left keys.
left_anti: use set difference of calling frameâs index and otherâs index.
right_anti: use set difference of otherâs index and calling frameâs index.
Suffix to use from left frameâs overlapping columns.
Suffix to use from right frameâs overlapping columns.
Order result DataFrame lexicographically by the join key. If False, the order of the join key depends on the join type (how keyword).
If specified, checks if join is of specified type.
âone_to_oneâ or â1:1â: check if join keys are unique in both left and right datasets.
âone_to_manyâ or â1:mâ: check if join keys are unique in left dataset.
âmany_to_oneâ or âm:1â: check if join keys are unique in right dataset.
âmany_to_manyâ or âm:mâ: allowed, but does not result in checks.
Added in version 1.5.0.
A dataframe containing columns from both the caller and other.
Notes
Parameters on, lsuffix, and rsuffix are not supported when passing a list of DataFrame objects.
Examples
>>> df = pd.DataFrame(
... {
... "key": ["K0", "K1", "K2", "K3", "K4", "K5"],
... "A": ["A0", "A1", "A2", "A3", "A4", "A5"],
... }
... )
>>> df key A 0 K0 A0 1 K1 A1 2 K2 A2 3 K3 A3 4 K4 A4 5 K5 A5
>>> other = pd.DataFrame({"key": ["K0", "K1", "K2"], "B": ["B0", "B1", "B2"]})
>>> other key B 0 K0 B0 1 K1 B1 2 K2 B2
Join DataFrames using their indexes.
>>> df.join(other, lsuffix="_caller", rsuffix="_other") key_caller A key_other B 0 K0 A0 K0 B0 1 K1 A1 K1 B1 2 K2 A2 K2 B2 3 K3 A3 NaN NaN 4 K4 A4 NaN NaN 5 K5 A5 NaN NaN
If we want to join using the key columns, we need to set key to be the index in both df and other. The joined DataFrame will have key as its index.
>>> df.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 otherâs index but we can use any column in df. This method preserves the original DataFrameâs index in the result.
>>> df.join(other.set_index("key"), on="key")
key A B
0 K0 A0 B0
1 K1 A1 B1
2 K2 A2 B2
3 K3 A3 NaN
4 K4 A4 NaN
5 K5 A5 NaN
Using non-unique key values shows how they are matched.
>>> df = pd.DataFrame(
... {
... "key": ["K0", "K1", "K1", "K3", "K0", "K1"],
... "A": ["A0", "A1", "A2", "A3", "A4", "A5"],
... }
... )
>>> df key A 0 K0 A0 1 K1 A1 2 K1 A2 3 K3 A3 4 K0 A4 5 K1 A5
>>> df.join(other.set_index("key"), on="key", validate="m:1")
key A B
0 K0 A0 B0
1 K1 A1 B1
2 K1 A2 B1
3 K3 A3 NaN
4 K0 A4 B0
5 K1 A5 B1
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