dataframe 上下拼接_Pandas中DataFrame数据合并、连接（concat、merge、join）之join

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

Column(s) in the caller to join on the index in other, otherwise joins index-on-index. If multiples columns given, the passed DataFrame must have a MultiIndex. Can pass an array as the join key if 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 isspecified) with other frame’s index

inner: form intersection of calling frame’s index (or column ifon is specified) with other frame’s index

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, preserves the index order of the calling (left) DataFrame

Returns:

joined : DataFrame

See also

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

Notes

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

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.==》join on 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