5.8 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**

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, dummies], axis=1)

2 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
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

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

3 总结

  • pd.concat([数据1, 数据2], axis=**)【知道】
  • pd.merge(left, right, how=, on=)【知道】
    • how -- 以何种方式连接
    • on -- 连接的键的依据是哪几个