学习资料:
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']
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
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
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
