4.8 高级处理-合并

学习目标

目标
- 应用pd.concat实现数据的合并
- 应用pd.merge实现数据的合并
应用
- 无

如果你的数据由多张表组成，那么有时候需要将不同的内容合并在一起分析

4.8.1 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

比如我们将刚才处理好的one-hot编码与原数据合并

股票哑变量合并

# 按照行索引进行
pd.concat([data, dummaries], axis=1)

4.8.2 pd.merge

pd.merge(left, right, how='inner', on=None, left_on=None, right_on=None,left_index=False, right_index=False, sort=True,suffixes=('_x', '_y'), copy=True, indicator=False,validate=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

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

4.8.2.1 pd.merge合并

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'])

内连接

左连接

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

左连接

右连接

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

右连接

外链接

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

外链接

4.8.3 总结

concat进行按照索引的合并
merge进行按照键合并