复习：我们已经在前面学习了Pandas基础，第二章我们开始进入数据分析的业务部分，在第二章第一节的内容中，我们学习了数据的清洗，这部分非常重要。只有当数据变得相对干净时，我们才能更有力地分析数据。在本节中，我们需要做的是数据重构，它仍然属于数据理解（准备）的范围。

开始前，导入numpy、pandas包和数据

import numpy as np import pandas as pd

text=pd.read_csv('result.csv') text.head(2)

	Unnamed: 0	PassengerId	Survived	Pclass	Name	Sex	Age	SibSp	Parch	Ticket	Fare	Cabin	Embarked
0	0	1	0	3	Braund, Mr. Owen Harris	male	22.0	1	0	A/5 21171	7.2500	NaN	S
1	1	2	1	1	Cumings, Mrs. John Bradley (Florence Briggs Th...	female	38.0	1	0	PC 17599	71.2833	C85	C

2 第二章：数据重构

2.4 数据的合并

2.4.1 任务一：将data文件夹中的所有数据都被载入并观察数据之间的关系

text_left_up = pd.read_csv("data/train-left-up.csv") text_left_down = pd.read_csv("data/train-left-down.csv") text_right_up = pd.read_csv("data/train-right-up.csv") text_right_down = pd.read_csv("data/train-right-down.csv")

text_left_up.head(1)

	PassengerId	Survived	Pclass	Name
0	1	0	3	Braund, Mr. Owen Harris

text_left_down.head(1)

	PassengerId	Survived	Pclass	Name
0	440	0	2	Kvillner, Mr. Johan Henrik Johannesson

text_right_up.head(1)

	Age	SibSp	Parch	Ticket	Fare	Cabin	Embarked
0	male	22.0	1	0	A/5 21171	7.25	NaN	S

text_right_down.head(1)

	Sex	Age	SibSp	Parch	Ticket	Fare	Cabin	Embarked
0	male	31.0	0	0	C.A. 18723	10.5	NaN	S

【提示】结合之前我们加载的train.csv数据，大致预测一下上面的数据是什么

2.4.2：任务二：使用concat方法：将数据text-left-up.csv和text-right-up.csv横向合并为一张表，并保存这张表为result_up

result_up = pd.concat([text_left_up,text_right_up],axis=1)
result_up.head(1)

	PassengerId	Survived	Pclass	Name	Sex	Age	SibSp	Parch	Ticket	Fare	Cabin	Embarked
0	1	0	3	Braund, Mr. Owen Harris	male	22.0	1	0	A/5 21171	7.25	NaN	S

2.4.3 任务三：使用concat方法：将text-left-down和text-right-down横向合并为一张表，并保存这张表为result_down。然后将上边的result_up和result_down纵向合并为result。

result_down = pd.concat([text_left_down,text_right_down],axis=1)
result_down.head(1)

	PassengerId	Survived	Pclass	Name	Sex	Age	SibSp	Parch	Ticket	Fare	Cabin	Embarked
0	440	0	2	Kvillner, Mr. Johan Henrik Johannesson	male	31.0	0	0	C.A. 18723	10.5	NaN	S

result = pd.concat([result_up,result_down])
result.head(1)

	PassengerId	Survived	Pclass	Name	Sex	Age	SibSp	Parch	Ticket	Fare	Cabin	Embarked
0	1	0	3	Braund, Mr. Owen Harris	male	22.0	1	0	A/5 21171	7.25	NaN	S

2.4.4 任务四：使用DataFrame自带的方法join方法和append：完成任务二和任务三的任务

#join 
tt1=text_left_up.join(text_right_up) 
tt2=text_left_down.join(text_right_down)

tt1.head(2)

	PassengerId	Survived	Pclass	Name	Sex	Age	SibSp	Parch	Ticket	Fare	Cabin	Embarked
0	1	0	3	Braund, Mr. Owen Harris	male	22.0	1	0	A/5 21171	7.2500	NaN	S
1	2	1	1	Cumings, Mrs. John Bradley (Florence Briggs Th...	female	38.0	1	0	PC 17599	71.2833	C85	C

tt2.head(2)

	PassengerId	Survived	Pclass	Name	Sex	Age	SibSp	Parch	Ticket	Fare	Cabin	Embarked
0	440	0	2	Kvillner, Mr. Johan Henrik Johannesson	male	31.0	0	0	C.A. 18723	10.50	NaN	S
1	441	1	2	Hart, Mrs. Benjamin (Esther Ada Bloomfield)	female	45.0	1	1	F.C.C. 13529	26.25	NaN	S

# append
tt2_whole= tt1.append(tt2)
tt2_whole.describe()

	PassengerId	Survived	Pclass	Age	SibSp	Parch	Fare
count	891.000000	891.000000	891.000000	714.000000	891.000000	891.000000	891.000000
mean	446.000000	0.383838	2.308642	29.699118	0.523008	0.381594	32.204208
std	257.353842	0.486592	0.836071	14.526497	1.102743	0.806057	49.693429
min	1.000000	0.000000	1.000000	0.420000	0.000000	0.000000	0.000000
25%	223.500000	0.000000	2.000000	20.125000	0.000000	0.000000	7.910400
50%	446.000000	0.000000	3.000000	28.000000	0.000000	0.000000	14.454200
75%	668.500000	1.000000	3.000000	38.000000	1.000000	0.000000	31.000000
max	891.000000	1.000000	3.000000	80.000000	8.000000	6.000000	512.329200

2.4.5 任务五：使用Panads的merge方法和DataFrame的append方法：完成任务二和任务三的任务

text_left_up.head(2)

	PassengerId	Survived	Pclass	Name
0	1	0	3	Braund, Mr. Owen Harris
1	2	1	1	Cumings, Mrs. John Bradley (Florence Briggs Th...

text_right_up.head(2)

	Sex	Age	SibSp	Parch	Ticket	Fare	Cabin	Embarked
0	male	22.0	1	0	A/5 21171	7.2500	NaN	S
1	female	38.0	1	0	PC 17599	71.2833	C85	C

ddup = pd.merge(text_left_up,text_right_up,left_index=True,right_index=True) #同时将行索引作为连接键
dddown = pd.merge(text_left_down,text_right_down,left_index=True,right_index=True)
dd_whole = ddup.append(dddown)
dd_whole.head(2)

	PassengerId	Survived	Pclass	Name	Sex	Age	SibSp	Parch	Ticket	Fare	Cabin	Embarked
0	1	0	3	Braund, Mr. Owen Harris	male	22.0	1	0	A/5 21171	7.2500	NaN	S
1	2	1	1	Cumings, Mrs. John Bradley (Florence Briggs Th...	female	38.0	1	0	PC 17599	71.2833	C85	C

【思考】对比merge、join以及concat的方法的不同以及相同。思考一下在任务四和任务五的情况下，为什么都要求使用DataFrame的append方法，如何只要求使用merge或者join可不可以完成任务四和任务五呢？

2.4.6 任务六：完成的数据保存为result.csv

#写入代码
dd_whole.to_csv('result.csv')

2.5 换一种角度看数据

2.5.1 任务一：将我们的数据变为Series类型的数据

df_whole = pd.read_csv('result.csv')
df_whole.head(2)

	Unnamed: 0	PassengerId	Survived	Pclass	Name	Sex	Age	SibSp	Parch	Ticket	Fare	Cabin	Embarked
0	0	1	0	3	Braund, Mr. Owen Harris	male	22.0	1	0	A/5 21171	7.2500	NaN	S
1	1	2	1	1	Cumings, Mrs. John Bradley (Florence Briggs Th...	female	38.0	1	0	PC 17599	71.2833	C85	C

news = df_whole.stack().head(20)
news

0  Unnamed: 0                                                     0
   PassengerId                                                    1
   Survived                                                       0
   Pclass                                                         3
   Name                                     Braund, Mr. Owen Harris
   Sex                                                         male
   Age                                                         22.0
   SibSp                                                          1
   Parch                                                          0
   Ticket                                                 A/5 21171
   Fare                                                        7.25
   Embarked                                                       S
1  Unnamed: 0                                                     1
   PassengerId                                                    2
   Survived                                                       1
   Pclass                                                         1
   Name           Cumings, Mrs. John Bradley (Florence Briggs Th...
   Sex                                                       female
   Age                                                         38.0
   SibSp                                                          1
dtype: object

这个stack（）函数很复杂，需要更加具体应用场景来理解。

资讯详情

Datawhale数据分析第二章第二节：数据重构1