【转】Pandas学习笔记（六）合并 merge

Pandas学习笔记系列：

• Pandas学习笔记（一）基本介绍
• Pandas学习笔记（二）选择数据
• Pandas学习笔记（三）修改&添加值
• Pandas学习笔记（四）处理丢失值
• Pandas学习笔记（五）合并 concat
• Pandas学习笔记（六）合并 merge
• Pandas学习笔记（七）plot画图

原文：https://morvanzhou.github.io/tutorials/data-manipulation/np-pd/3-7-pd-merge/ 本文有删减

要点

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.

依据一组key合并

import pandas as pd

#定义资料集并打印出
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)
"""
   A   B key
0  A0  B0  K0
1  A1  B1  K1
2  A2  B2  K2
3  A3  B3  K3
"""

print(right)
"""
   C   D key
0  C0  D0  K0
1  C1  D1  K1
2  C2  D2  K2
3  C3  D3  K3
"""

#依据key column合并，并打印出
res = pd.merge(left, right, on='key')

print(res)
"""
   A   B key   C   D
0  A0  B0  K0  C0  D0
1  A1  B1  K1  C1  D1
2  A2  B2  K2  C2  D2
3  A3  B3  K3  C3  D3
"""

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'。

import pandas as pd

#定义资料集并打印出
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)
"""
   A   B key1 key2
0  A0  B0   K0   K0
1  A1  B1   K0   K1
2  A2  B2   K1   K0
3  A3  B3   K2   K1
"""

print(right)
"""
   C   D key1 key2
0  C0  D0   K0   K0
1  C1  D1   K1   K0
2  C2  D2   K1   K0
3  C3  D3   K2   K0
"""

依据key1与key2 columns进行合并，并打印出四种结果['left', 'right', 'outer', 'inner']

inner表示如果两个keys对应的value值相等，就交叉合并，否则丢弃，也就是求交集∩

例如left和right都有(K0,K0)和(K1,K0)。其中right有两个(K1,K0),left只有一个，从下面代码中的结果可以看到具有相同key的值会交叉合并，所以此时会生成两个新的(K1,K0)值。

res = pd.merge(left, right, on=['key1', 'key2'], how='inner')
print(res)
"""
   A   B key1 key2   C   D
0  A0  B0   K0   K0  C0  D0
1  A2  B2   K1   K0  C1  D1
2  A2  B2   K1   K0  C2  D2
"""

# outter求并集∪
res = pd.merge(left, right, on=['key1', 'key2'], how='outer')
print(res)
"""
    A    B key1 key2    C    D
0   A0   B0   K0   K0   C0   D0
1   A1   B1   K0   K1  NaN  NaN
2   A2   B2   K1   K0   C1   D1
3   A2   B2   K1   K0   C2   D2
4   A3   B3   K2   K1  NaN  NaN
5  NaN  NaN   K2   K0   C3   D3
"""

# 以左边的key为准，如果右边和左边的值相等
res = pd.merge(left, right, on=['key1', 'key2'], how='left')
print(res)
"""
   A   B key1 key2    C    D
0  A0  B0   K0   K0   C0   D0
1  A1  B1   K0   K1  NaN  NaN
2  A2  B2   K1   K0   C1   D1
3  A2  B2   K1   K0   C2   D2
4  A3  B3   K2   K1  NaN  NaN
"""

res = pd.merge(left, right, on=['key1', 'key2'], how='right')
print(res)
"""
    A    B key1 key2   C   D
0   A0   B0   K0   K0  C0  D0
1   A2   B2   K1   K0  C1  D1
2   A2   B2   K1   K0  C2  D2
3  NaN  NaN   K2   K0  C3  D3
"""

Indicator

indicator=True会将合并的记录放在新的一列。

import pandas as pd

#定义资料集并打印出
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   0.0        a        NaN   left_only
1   1.0        b        2.0        both
2   2.0      NaN        2.0  right_only
3   2.0      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   0.0        a        NaN        left_only
1   1.0        b        2.0             both
2   2.0      NaN        2.0       right_only
3   2.0      NaN        2.0       right_only
"""

依据index合并

import pandas as pd

#定义资料集并打印出
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
"""

解决overlapping的问题

import pandas as pd

#定义资料集
boys = pd.DataFrame({'k': ['K0', 'K1', 'K2'], 'age': [1, 2, 3]})
girls = pd.DataFrame({'k': ['K0', 'K0', 'K3'], 'age': [4, 5, 6]})

#使用suffixes解决overlapping的问题
res = pd.merge(boys, girls, on='k', suffixes=['_boy', '_girl'], how='inner')
print(res)
"""
   age_boy   k  age_girl
0        1  K0         4
1        1  K0         5
"""

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2019-10-30 12:58:20