Python数据分析入门（十一）：数据合并
数据合并(pd.merge)
-
根据单个或多个键将不同DataFrame的行连接起来
-
类似数据库的连接操作
-
pd.merge:(left, right, how='inner',on=None,left_on=None, right_on=None )
left:合并时左边的DataFrame
right:合并时右边的DataFrame
how:合并的方式,默认'inner', 'outer', 'left', 'right'
on:需要合并的列名,必须两边都有的列名，并以 left 和 right 中的列名的交集作为连接键
left_on: left Dataframe中用作连接键的列
right_on: right Dataframe中用作连接键的列 -
内连接 inner:对两张表都有的键的交集进行联合
-
全连接 outer：对两者表的都有的键的并集进行联合
-
左连接 left：对所有左表的键进行联合
-
右连接 right：对所有右表的键进行联合
示例代码：
import pandas as pd import numpy as np 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']}) pd.merge(left,right,on='key') #指定连接键key
运行结果：
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
示例代码：
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']})
pd.merge(left,right,on=['key1','key2']) #指定多个键，进行合并
运行结果：
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
#指定左连接 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']}) pd.merge(left, right, how='left', on=['key1', 'key2']) 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
#指定右连接 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']}) pd.merge(left, right, how='right', on=['key1', 'key2']) 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
默认是“内连接”(inner)，即结果中的键是交集
how指定连接方式
“外连接”(outer)，结果中的键是并集
示例代码：
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']})
pd.merge(left,right,how='outer',on=['key1','key2'])
运行结果：
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
处理重复列名
参数suffixes：默认为_x, _y
示例代码：
# 处理重复列名 df_obj1 = pd.DataFrame({'key': ['b', 'b', 'a', 'c', 'a', 'a', 'b'], 'data' : np.random.randint(0,10,7)}) df_obj2 = pd.DataFrame({'key': ['a', 'b', 'd'], 'data' : np.random.randint(0,10,3)}) print(pd.merge(df_obj1, df_obj2, on='key', suffixes=('_left', '_right')))
运行结果：
data_left key data_right
0 9 b 1
1 5 b 1
2 1 b 1
3 2 a 8
4 2 a 8
5 5 a 8
按索引连接
参数left_index=True或right_index=True
示例代码：
# 按索引连接 df_obj1 = pd.DataFrame({'key': ['b', 'b', 'a', 'c', 'a', 'a', 'b'], 'data1' : np.random.randint(0,10,7)}) df_obj2 = pd.DataFrame({'data2' : np.random.randint(0,10,3)}, index=['a', 'b', 'd']) print(pd.merge(df_obj1, df_obj2, left_on='key', right_index=True))
运行结果：
data1 key data2 0 3 b 6 1 4 b 6 6 8 b 6 2 6 a 0 4 3 a 0 5 0 a 0
数据合并(pd.concat)
沿轴方向将多个对象合并到一起
1. NumPy的concat
np.concatenate
示例代码：
import numpy as np import pandas as pd arr1 = np.random.randint(0, 10, (3, 4)) arr2 = np.random.randint(0, 10, (3, 4)) print(arr1) print(arr2) print(np.concatenate([arr1, arr2])) print(np.concatenate([arr1, arr2], axis=1))
运行结果：
# print(arr1) [[3 3 0 8] [2 0 3 1] [4 8 8 2]] # print(arr2) [[6 8 7 3] [1 6 8 7] [1 4 7 1]] # print(np.concatenate([arr1, arr2])) [[3 3 0 8] [2 0 3 1] [4 8 8 2] [6 8 7 3] [1 6 8 7] [1 4 7 1]] # print(np.concatenate([arr1, arr2], axis=1)) [[3 3 0 8 6 8 7 3] [2 0 3 1 1 6 8 7] [4 8 8 2 1 4 7 1]]
2. pd.concat
- 注意指定轴方向，默认axis=0
- join指定合并方式，默认为outer
- Series合并时查看行索引有无重复
df1 = pd.DataFrame(np.arange(6).reshape(3,2),index=list('abc'),columns=['one','two']) df2 = pd.DataFrame(np.arange(4).reshape(2,2)+5,index=list('ac'),columns=['three','four']) pd.concat([df1,df2]) #默认外连接，axis=0 four one three two a NaN 0.0 NaN 1.0 b NaN 2.0 NaN 3.0 c NaN 4.0 NaN 5.0 a 6.0 NaN 5.0 NaN c 8.0 NaN 7.0 NaN pd.concat([df1,df2],axis='columns') #指定axis=1连接 one two three four a 0 1 5.0 6.0 b 2 3 NaN NaN c 4 5 7.0 8.0 #同样我们也可以指定连接的方式为inner pd.concat([df1,df2],axis=1,join='inner') one two three four a 0 1 5 6 c 4 5 7 8
