join(self, other: 'DataFrame | Series', on: 'IndexLabel | None' = None, how: 'str' = 'left', lsuffix: 'str' = '', rsuffix: 'str' = '', sort: 'bool' = False) -> '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.
Parameters :None:None:`on`, :None:None:`lsuffix`, and :None:None:`rsuffix` are not supported when passing a list of DataFrame
objects.
Support for specifying index levels as the :None:None:`on` parameter was added in version 0.23.0.
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.
How to handle the operation of the two objects.
left: use calling frame's index (or column if on is specified)
right: use other
's index.
outer: form union of calling frame's index (or column if on is specified) with other
's index, and sort it. lexicographically.
inner: form intersection of calling frame's index (or column if on is specified) with other
's index, preserving the order of the calling's one.
cross: creates the cartesian product from both frames, preserves the order of the left keys.
Suffix to use from left frame's overlapping columns.
Suffix to use from right frame's 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).
A dataframe containing columns from both the caller and other
.
Join columns of another DataFrame.
DataFrame.merge
For column(s)-on-column(s) operations.
>>> df = pd.DataFrame({'key': ['K0', 'K1', 'K2', 'K3', 'K4', 'K5'],This example is valid syntax, but we were not able to check execution
... '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 A5This example is valid syntax, but we were not able to check execution
>>> other = pd.DataFrame({'key': ['K0', 'K1', 'K2'],This example is valid syntax, but we were not able to check execution
... 'B': ['B0', 'B1', 'B2']})
>>> other key B 0 K0 B0 1 K1 B1 2 K2 B2
Join DataFrames using their indexes.
This example is valid syntax, but we were not able to check execution>>> 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 :None:None:`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 :None:None:`on` parameter. DataFrame.join always uses other
's index but we can use any column in :None:None:`df`. This method preserves the original DataFrame's 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.
This example is valid syntax, but we were not able to check execution>>> df = pd.DataFrame({'key': ['K0', 'K1', 'K1', 'K3', 'K0', 'K1'],This example is valid syntax, but we were not able to check execution
... '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 A5This example is valid syntax, but we were not able to check execution
>>> df.join(other.set_index('key'), on='key') 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 B1See :
The following pages refer to to this document either explicitly or contain code examples using this.
pandas.core.reshape.merge.merge
pandas.core.reshape.concat.concat
pandas.core.frame.DataFrame.merge
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