python数据分析——pandas的拼接操作

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=False

1)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