Pandas 合并merge

pandas中的merge和concat类似,但主要是用于两组有key column的数据,统一索引的数据. 通常也被用在Database的处理当中.

1、依据一组key合并

>>> import pandas as pd
>>> left = pd.DataFrame({'key': ['K0', 'K1', 'K2', 'K3'],
...                              'A': ['A0', 'A1', 'A2', 'A3'],
...                              'B': ['B0', 'B1', 'B2', 'B3']})
>>> right = pd.DataFrame({'key': ['K0', 'K1', 'K2', 'K3'],
...                               'C': ['C0', 'C1', 'C2', 'C3'],
...                               'D': ['D0', 'D1', 'D2', 'D3']})
>>> print(left)
  key   A   B
0  K0  A0  B0
1  K1  A1  B1
2  K2  A2  B2
3  K3  A3  B3
>>> print(right)
  key   C   D
0  K0  C0  D0
1  K1  C1  D1
2  K2  C2  D2
3  K3  C3  D3

#依据key column合并，并打印出
>>> res = pd.merge(left, right, on='key')
>>> print(res)
  key   A   B   C   D
0  K0  A0  B0  C0  D0
1  K1  A1  B1  C1  D1
2  K2  A2  B2  C2  D2
3  K3  A3  B3  C3  D3

2、依据两组key合并

合并时有4种方法how = ['left', 'right', 'outer', 'inner']，预设值how='inner'

>>> 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']})
>>> print(left)
  key1 key2   A   B
0   K0   K0  A0  B0
1   K0   K1  A1  B1
2   K1   K0  A2  B2
3   K2   K1  A3  B3
>>> print(right)
  key1 key2   C   D
0   K0   K0  C0  D0
1   K1   K0  C1  D1
2   K1   K0  C2  D2
3   K2   K0  C3  D3

##依据key1与key2 columns进行合并，并打印出四种结果['left', 'right', 'outer', 'inner']
>>> res = pd.merge(left, right, on=['key1', 'key2'], how='inner')
>>> print(res)
  key1 key2   A   B   C   D
0   K0   K0  A0  B0  C0  D0
1   K1   K0  A2  B2  C1  D1
2   K1   K0  A2  B2  C2  D2
>>> res = pd.merge(left, right, on=['key1', 'key2'], how='outer')
>>> print(res)
  key1 key2    A    B    C    D
0   K0   K0   A0   B0   C0   D0
1   K0   K1   A1   B1  NaN  NaN
2   K1   K0   A2   B2   C1   D1
3   K1   K0   A2   B2   C2   D2
4   K2   K1   A3   B3  NaN  NaN
5   K2   K0  NaN  NaN   C3   D3
>>> res = pd.merge(left, right, on=['key1', 'key2'], how='left')
>>> print(res)
  key1 key2   A   B    C    D
0   K0   K0  A0  B0   C0   D0
1   K0   K1  A1  B1  NaN  NaN
2   K1   K0  A2  B2   C1   D1
3   K1   K0  A2  B2   C2   D2
4   K2   K1  A3  B3  NaN  NaN
>>> res = pd.merge(left, right, on=['key1', 'key2'], how='right')
>>> print(res)
  key1 key2    A    B   C   D
0   K0   K0   A0   B0  C0  D0
1   K1   K0   A2   B2  C1  D1
2   K1   K0   A2   B2  C2  D2
3   K2   K0  NaN  NaN  C3  D3

3、Indicator

indicator=True会将合并的记录放在新的一列。

>>> df1 = pd.DataFrame({'col1':[0,1], 'col_left':['a','b']})
>>> df2 = pd.DataFrame({'col1':[1,2,2],'col_right':[2,2,2]})
>>> print(df1)
   col1 col_left
0     0        a
1     1        b
>>> print(df2)
   col1  col_right
0     1          2
1     2          2
2     2          2

# 依据col1进行合并，并启用indicator=True，最后打印出
>>> res = pd.merge(df1, df2, on='col1', how='outer', indicator=True)
>>> print(res)
   col1 col_left  col_right      _merge
0     0        a        NaN   left_only
1     1        b        2.0        both
2     2      NaN        2.0  right_only
3     2      NaN        2.0  right_only

# 自定indicator column的名称，并打印出
>>> res = pd.merge(df1, df2, on='col1', how='outer', indicator='indicator_column')
>>> print(res)
   col1 col_left  col_right indicator_column
0     0        a        NaN        left_only
1     1        b        2.0             both
2     2      NaN        2.0       right_only
3     2      NaN        2.0       right_only

4、依据index合并

>>> left = pd.DataFrame({'A': ['A0', 'A1', 'A2'],
...                      'B': ['B0', 'B1', 'B2']},
...                      index=['K0', 'K1', 'K2'])
>>> right = pd.DataFrame({'C': ['C0', 'C2', 'C3'],
...                       'D': ['D0', 'D2', 'D3']},
...                      index=['K0', 'K2', 'K3'])
>>> print(left)
     A   B
K0  A0  B0
K1  A1  B1
K2  A2  B2
>>> print(right)
     C   D
K0  C0  D0
K2  C2  D2
K3  C3  D3
#依据左右资料集的index进行合并，how='outer',并打印出
>>> res = pd.merge(left, right, left_index=True, right_index=True, how='outer')
>>> print(res)
      A    B    C    D
K0   A0   B0   C0   D0
K1   A1   B1  NaN  NaN
K2   A2   B2   C2   D2
K3  NaN  NaN   C3   D3
#依据左右资料集的index进行合并，how='inner',并打印出
>>> res = pd.merge(left, right, left_index=True, right_index=True, how='inner')
>>> print(res)
     A   B   C   D
K0  A0  B0  C0  D0
K2  A2  B2  C2  D2

5、解决overlapping的问题

>>> boys = pd.DataFrame({'k': ['K0', 'K1', 'K2'], 'age': [1, 2, 3]})
>>> girls = pd.DataFrame({'k': ['K0', 'K0', 'K3'], 'age': [4, 5, 6]})
>>> print(boys)
    k  age
0  K0    1
1  K1    2
2  K2    3
>>> print(girls)
    k  age
0  K0    4
1  K0    5
2  K3    6
#使用suffixes解决overlapping的问题
>>> res = pd.merge(boys, girls, on='k', suffixes=['_boy', '_girl'], how='inner')
>>> print(res)
    k  age_boy  age_girl
0  K0        1         4
1  K0        1         5