Pandas合并

pd.concat实现数据合并

pd.concat([data1, data2], axis=1)
- 按照行或列进行合并axis=0为列索引，axis=1为行索引

pd.merge

pd.merge(left, right, how='inner', on=None, left_on=None, right_on=None)
- 可以指定按照两组数据的共同键值对合并或者左右各自
- left: A DataFrame object
- right: Another DataFrame object
- on: Columns (names) to join on. Must be found in both the left and right DataFrame objects.
- left_on=None, right_on=None：指定左右键

how -- 以何种方式连接
on -- 连接的键的依据是哪几个

Merge method	SQL Join Name	Description
left	LEFT OUTER JOIN	Use keys from left frame only
right	RIGHT OUTER JOIN	Use keys from right frame only
outer	FULL OUTER JOIN	Use union of keys from both frames
inner	INNER JOIN	Use intersection of keys from both frames

left = pd.DataFrame({'key1': ['K0', 'K0', 'K1', 'K2'],
                        'key2': ['K0', 'K1', 'K0', 'K1'],
                        'A': ['A0', 'A1', 'A2', 'A3'],
                        'B': ['B0', 'B1', 'B2', 'B3']})

right = pd.DataFrame({'key1': ['K0', 'K1', 'K1', 'K2'],
                        'key2': ['K0', 'K0', 'K0', 'K0'],
                        'C': ['C0', 'C1', 'C2', 'C3'],
                        'D': ['D0', 'D1', 'D2', 'D3']})

# 默认内连接
result = pd.merge(left, right, on=['key1', 'key2'])

左连接

result = pd.merge(left, right, how='left', on=['key1', 'key2'])

右连接

result = pd.merge(left, right, how='right', on=['key1', 'key2'])

外连接

result = pd.merge(left, right, how='outer', on=['key1', 'key2'])