Pandas中DataFrame數據合並、連接（concat、merge、join）之join

pandas.DataFrame.join

自己弄了很久，一看官網。感覺自己宛如智障。不要臉了，直接抄

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

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

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