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
原文地址：https://www.cnblogs.com/anhoo/p/9383978.html
时间： 2024-11-02 04:23:57