摘要：
panda的拼接操作分为两种类型：级联：pd.concat，pd.append合并：pd.merge，pd.join1。使用pd.contat（）级联熊猫。使用pd.concat函数，该函数与np.concatenate函数类似，但具有一些附加参数：objsaxis=0keysjiin='outer'/'inner'：它表示级联模式，外部将级联所有项（忽略匹配和不匹配
pandas的拼接操作
pandas的拼接分为两种：
- 级联：pd.concat, pd.append
- 合并：pd.merge, pd.join
1. 使用pd.concat()级联
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=False1)匹配级联
In [1]:
import numpy as np
import pandas as pd
from pandas import Series,DataFrame
In [2]:
df1 = DataFrame(data=np.random.randint(0,100,size=(3,3)),index=['a','b','c'],columns=['A','B','C'])
df2 = DataFrame(data=np.random.randint(0,100,size=(3,3)),index=['a','d','c'],columns=['A','d','C'])
In [7]:
pd.concat((df1,df1),axis=0,join='inner')
Out[7]:
| A | B | C | |
|---|---|---|---|
| a | 59 | 40 | 89 |
| b | 71 | 5 | 76 |
| c | 29 | 34 | 87 |
| a | 59 | 40 | 89 |
| b | 71 | 5 | 76 |
| c | 29 | 34 | 87 |
2) 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
In [11]:
pd.concat((df1,df2),axis=1,join='outer')Out[11]:
| A | B | C | A | d | C | |
|---|---|---|---|---|---|---|
| a | 59.0 | 40.0 | 89.0 | 50.0 | 26.0 | 45.0 |
| b | 71.0 | 5.0 | 76.0 | NaN | NaN | NaN |
| c | 29.0 | 34.0 | 87.0 | 31.0 | 82.0 | 35.0 |
| d | NaN | NaN | NaN | 23.0 | 95.0 | 94.0 |
3) 使用df.append()函数添加
由于在后面级联的使用非常普遍，因此有一个函数append专门用于在后面添加
2. 使用pd.merge()合并
merge与concat的区别在于，merge需要依据某一共同的列来进行合并
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
参数：
- how：out取并集 inner取交集
- on：当有多列相同的时候，可以使用on来指定使用那一列进行合并，on的值为一个列表
1) 一对一合并
In [12]:
df1 = DataFrame({'employee':['Bob','Jake','Lisa'],
'group':['Accounting','Engineering','Engineering'],
})
df1
Out[12]:
| employee | group | |
|---|---|---|
| 0 | Bob | Accounting |
| 1 | Jake | Engineering |
| 2 | Lisa | Engineering |
In [13]:
df2 = DataFrame({'employee':['Lisa','Bob','Jake'],
'hire_date':[2004,2008,2012],
})
df2
Out[13]:
| employee | hire_date | |
|---|---|---|
| 0 | Lisa | 2004 |
| 1 | Bob | 2008 |
| 2 | Jake | 2012 |
In [14]:
pd.merge(df1,df2,how='outer')
Out[14]:
| employee | group | hire_date | |
|---|---|---|---|
| 0 | Bob | Accounting | 2008 |
| 1 | Jake | Engineering | 2012 |
| 2 | Lisa | Engineering | 2004 |
2) 多对一合并
In [15]:
df3 = DataFrame({
'employee':['Lisa','Jake'],
'group':['Accounting','Engineering'],
'hire_date':[2004,2016]})
df3
Out[15]:
| employee | group | hire_date | |
|---|---|---|---|
| 0 | Lisa | Accounting | 2004 |
| 1 | Jake | Engineering | 2016 |
In [16]:
df4 = DataFrame({'group':['Accounting','Engineering','Engineering'],
'supervisor':['Carly','Guido','Steve']
})
df4
Out[16]:
| group | supervisor | |
|---|---|---|
| 0 | Accounting | Carly |
| 1 | Engineering | Guido |
| 2 | Engineering | Steve |
In [17]:
pd.merge(df3,df4)
Out[17]:
| employee | group | hire_date | supervisor | |
|---|---|---|---|---|
| 0 | Lisa | Accounting | 2004 | Carly |
| 1 | Jake | Engineering | 2016 | Guido |
| 2 | Jake | Engineering | 2016 | Steve |
3) 多对多合并
In [18]:
df1 = DataFrame({'employee':['Bob','Jake','Lisa'],
'group':['Accounting','Engineering','Engineering']})
df1
Out[18]:
| employee | group | |
|---|---|---|
| 0 | Bob | Accounting |
| 1 | Jake | Engineering |
| 2 | Lisa | Engineering |
In [19]:
df5 = DataFrame({'group':['Engineering','Engineering','HR'],
'supervisor':['Carly','Guido','Steve']
})
df5
Out[19]:
| group | supervisor | |
|---|---|---|
| 0 | Engineering | Carly |
| 1 | Engineering | Guido |
| 2 | HR | Steve |
In [21]:
pd.merge(df1,df5,how='outer')
Out[21]:
| employee | group | supervisor | |
|---|---|---|---|
| 0 | Bob | Accounting | NaN |
| 1 | Jake | Engineering | Carly |
| 2 | Jake | Engineering | Guido |
| 3 | Lisa | Engineering | Carly |
| 4 | Lisa | Engineering | Guido |
| 5 | NaN | HR | Steve |
- 加载excl数据:pd.read_excel('excl_path',sheetname=1)
4) key的规范化
- 当列冲突时，即有多个列名称相同时，需要使用on=来指定哪一个列作为key，配合suffixes指定冲突列名
In [10]:
df1 = DataFrame({'employee':['Jack',"Summer","Steve"],
'group':['Accounting','Finance','Marketing']})
In [11]:
df2 = DataFrame({'employee':['Jack','Bob',"Jake"],
'hire_date':[2003,2009,2012],
'group':['Accounting','sell','ceo']})
In [22]:
display(df1,df2)
| employee | group | |
|---|---|---|
| 0 | Bob | Accounting |
| 1 | Jake | Engineering |
| 2 | Lisa | Engineering |
| employee | hire_date | |
|---|---|---|
| 0 | Lisa | 2004 |
| 1 | Bob | 2008 |
| 2 | Jake | 2012 |
- 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
In [12]:
df1 = DataFrame({'employee':['Bobs','Linda','Bill'],
'group':['Accounting','Product','Marketing'],
'hire_date':[1998,2017,2018]})
In [13]:
df5 = DataFrame({'name':['Lisa','Bobs','Bill'],
'hire_dates':[1998,2016,2007]})
In [23]:
display(df1,df5)
| employee | group | |
|---|---|---|
| 0 | Bob | Accounting |
| 1 | Jake | Engineering |
| 2 | Lisa | Engineering |
| group | supervisor | |
|---|---|---|
| 0 | Engineering | Carly |
| 1 | Engineering | Guido |
| 2 | HR | Steve |
5) 内合并与外合并:out取并集 inner取交集
- 内合并：只保留两者都有的key（默认模式）
In [25]:
df6 = DataFrame({'name':['Peter','Paul','Mary'],
'food':['fish','beans','bread']}
)
df7 = DataFrame({'name':['Mary','Joseph'],
'drink':['wine','beer']})
In [26]:
display(df6,df7)
| name | food | |
|---|---|---|
| 0 | Peter | fish |
| 1 | Paul | beans |
| 2 | Mary | bread |
| name | drink | |
|---|---|---|
| 0 | Mary | wine |
| 1 | Joseph | beer |
- 外合并 how='outer'：补NaN
In [27]:
df6 = DataFrame({'name':['Peter','Paul','Mary'],
'food':['fish','beans','bread']}
)
df7 = DataFrame({'name':['Mary','Joseph'],
'drink':['wine','beer']})
display(df6,df7)
pd.merge()
| name | food | |
|---|---|---|
| 0 | Peter | fish |
| 1 | Paul | beans |
| 2 | Mary | bread |
| name | drink | |
|---|---|---|
| 0 | Mary | wine |
| 1 | Joseph | beer |