5 dataframe级联与合并操作 

import pandas as pd
import numpy as np

级联操作

  • pd.concat, pd.append

pandas使用pd.concat函数，与np.concatenate函数类似，只是多了一些参数：

objs
axis=0
keys
join='outer' / 'inner':表示的是级联的方式，outer会将所有的项进行级联（忽略匹配和不匹配），而inner只会将匹配的项级联到一起，不匹配的不级联
ignore_index=False
  • 匹配级联
df1 = pd.DataFrame(data=np.random.randint(0,100,size=(5,3)),columns=['A','B','C'])
df2 = pd.DataFrame(data=np.random.randint(0,100,size=(5,3)),columns=['A','D','C'])

pd.concat((df1,df1),axis=1) #行列索引都一致的级联叫做匹配级联
A B C A B C
0 26 63 95 26 63 95
1 66 86 35 66 86 35
2 74 3 4 74 3 4
3 85 0 67 85 0 67
4 59 28 65 59 28 65
  • 不匹配级联
    • 不匹配指的是级联的维度的索引不一致。例如纵向级联时列索引不一致，横向级联时行索引不一致
    • 有2种连接方式：
      • 外连接：补NaN（默认模式）
      • 内连接：只连接匹配的项
pd.concat((df1,df2),axis=0)

/Users/bobo/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:1: FutureWarning: Sorting because non-concatenation axis is not aligned. A future version
of pandas will change to not sort by default.

To accept the future behavior, pass 'sort=False'.

To retain the current behavior and silence the warning, pass 'sort=True'.

  """Entry point for launching an IPython kernel.
A B C D
0 26 63.0 95 NaN
1 66 86.0 35 NaN
2 74 3.0 4 NaN
3 85 0.0 67 NaN
4 59 28.0 65 NaN
0 98 NaN 87 2.0
1 24 NaN 3 52.0
2 68 NaN 61 65.0
3 57 NaN 29 29.0
4 99 NaN 41 51.0 
pd.concat((df1,df2),axis=0,join='inner') #inner直把可以级联的级联不能级联不处理
A C
0 26 95
1 66 35
2 74 4
3 85 67
4 59 65
0 98 87
1 24 3
2 68 61
3 57 29
4 99 41

  • 如果想要保留数据的完整性必须使用outer（外连接）

  • append函数的使用

df1.append(df1)
A B C
0 26 63 95
1 66 86 35
2 74 3 4
3 85 0 67
4 59 28 65
0 26 63 95
1 66 86 35
2 74 3 4
3 85 0 67
4 59 28 65

合并操作

  • merge与concat的区别在于，merge需要依据某一共同列来进行合并

  • 使用pd.merge()合并时，会自动根据两者相同column名称的那一列，作为key来进行合并。

  • 注意每一列元素的顺序不要求一致

一对一合并

from pandas import DataFrame

df1 = DataFrame({'employee':['Bob','Jake','Lisa'],
                'group':['Accounting','Engineering','Engineering'],
                })
df1
employee group
0 Bob Accounting
1 Jake Engineering
2 Lisa Engineering 
df2 = DataFrame({'employee':['Lisa','Bob','Jake'],
                'hire_date':[2004,2008,2012],
                })
df2
employee hire_date
0 Lisa 2004
1 Bob 2008
2 Jake 2012 
pd.merge(df1,df2,on='employee')
employee group hire_date
0 Bob Accounting 2008
1 Jake Engineering 2012
2 Lisa Engineering 2004

一对多合并

df3 = DataFrame({
    'employee':['Lisa','Jake'],
    'group':['Accounting','Engineering'],
    'hire_date':[2004,2016]})
df3
employee group hire_date
0 Lisa Accounting 2004
1 Jake Engineering 2016 
df4 = DataFrame({'group':['Accounting','Engineering','Engineering'],
                       'supervisor':['Carly','Guido','Steve']
                })
df4
group supervisor
0 Accounting Carly
1 Engineering Guido
2 Engineering Steve 
pd.merge(df3,df4)#on如果不写，默认情况下使用两表中公有的列作为合并条件
employee group hire_date supervisor
0 Lisa Accounting 2004 Carly
1 Jake Engineering 2016 Guido
2 Jake Engineering 2016 Steve

多对多合并

df1 = DataFrame({'employee':['Bob','Jake','Lisa'],
                 'group':['Accounting','Engineering','Engineering']})
df1
employee group
0 Bob Accounting
1 Jake Engineering
2 Lisa Engineering 
df5 = DataFrame({'group':['Engineering','Engineering','HR'],
                'supervisor':['Carly','Guido','Steve']
                })
df5
group supervisor
0 Engineering Carly
1 Engineering Guido
2 HR Steve 
pd.merge(df1,df5,how='right')
employee group supervisor
0 Jake Engineering Carly
1 Lisa Engineering Carly
2 Jake Engineering Guido
3 Lisa Engineering Guido
4 NaN HR Steve 
pd.merge(df1,df5,how='right')
employee group supervisor
0 Jake Engineering Carly
1 Lisa Engineering Carly
2 Jake Engineering Guido
3 Lisa Engineering Guido
4 NaN HR Steve

key的规范化

  • 当两张表没有可进行连接的列时，可使用left_on和right_on手动指定merge中左右两边的哪一列列作为连接的列
df1 = DataFrame({'employee':['Bobs','Linda','Bill'],
                'group':['Accounting','Product','Marketing'],
               'hire_date':[1998,2017,2018]})
df1
employee group hire_date
0 Bobs Accounting 1998
1 Linda Product 2017
2 Bill Marketing 2018 
df5 = DataFrame({'name':['Lisa','Bobs','Bill'],
                'hire_dates':[1998,2016,2007]})
df5
name hire_dates
0 Lisa 1998
1 Bobs 2016
2 Bill 2007 
pd.merge(df1,df5,left_on='employee',right_on='name')
employee group hire_date name hire_dates
0 Bobs Accounting 1998 Bobs 2016
1 Bill Marketing 2018 Bill 2007

内合并与外合并:out取并集 inner取交集

df6 = DataFrame({'name':['Peter','Paul','Mary'],
               'food':['fish','beans','bread']}
               )
df7 = DataFrame({'name':['Mary','Joseph'],
                'drink':['wine','beer']})




df6 = DataFrame({'name':['Peter','Paul','Mary'],
               'food':['fish','beans','bread']}
               )
df7 = DataFrame({'name':['Mary','Joseph'],
                'drink':['wine','beer']})
posted @ 2021-06-16 13:23  风hua  阅读(151)  评论(0)  编辑  收藏  举报