第二章：第二节数据重构1-课程

复习：在前面我们已经学习了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，这一部分十分重要，只有数据变得相对干净，我们之后对数据的分析才可以更有力。而这一节，我们要做的是数据重构，数据重构依旧属于数据理解（准备）的范围。

开始之前，导入numpy、pandas包和数据

# 导入基本库
import pandas as pd 
import numpy as np

# 载入data文件中的:train-left-up.csv
train_left_up=pd.read_csv(r"./data/train-left-up.csv",encoding="utf_8_sig")
train_left_up

	PassengerId	Survived	Pclass	Name
0	1	0	3	Braund, Mr. Owen Harris
1	2	1	1	Cumings, Mrs. John Bradley (Florence Briggs Th...
2	3	1	3	Heikkinen, Miss. Laina
3	4	1	1	Futrelle, Mrs. Jacques Heath (Lily May Peel)
4	5	0	3	Allen, Mr. William Henry
...	...	...	...	...
434	435	0	1	Silvey, Mr. William Baird
435	436	1	1	Carter, Miss. Lucile Polk
436	437	0	3	Ford, Miss. Doolina Margaret "Daisy"
437	438	1	2	Richards, Mrs. Sidney (Emily Hocking)
438	439	0	1	Fortune, Mr. Mark

439 rows × 4 columns

2 第二章：数据重构

2.4 数据的合并

2.4.1 任务一：将data文件夹里面的所有数据都载入，观察数据的之间的关系

#写入代码
train_left_down=pd.read_csv(r"./data/train-left-down.csv",encoding="utf_8_sig")
train_right_down=pd.read_csv(r"./data/train-right-down.csv",encoding="utf_8_sig")
train_right_up=pd.read_csv(r"./data/train-right-up.csv",encoding="utf_8_sig")

train_left_up

	PassengerId	Survived	Pclass	Name
0	1	0	3	Braund, Mr. Owen Harris
1	2	1	1	Cumings, Mrs. John Bradley (Florence Briggs Th...
2	3	1	3	Heikkinen, Miss. Laina
3	4	1	1	Futrelle, Mrs. Jacques Heath (Lily May Peel)
4	5	0	3	Allen, Mr. William Henry
...	...	...	...	...
434	435	0	1	Silvey, Mr. William Baird
435	436	1	1	Carter, Miss. Lucile Polk
436	437	0	3	Ford, Miss. Doolina Margaret "Daisy"
437	438	1	2	Richards, Mrs. Sidney (Emily Hocking)
438	439	0	1	Fortune, Mr. Mark

439 rows × 4 columns

【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

train_right_up

	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
2	female	26.0	0	0	STON/O2. 3101282	7.9250	NaN	S
3	female	35.0	1	0	113803	53.1000	C123	S
4	male	35.0	0	0	373450	8.0500	NaN	S
...	...	...	...	...	...	...	...	...
434	male	50.0	1	0	13507	55.9000	E44	S
435	female	14.0	1	2	113760	120.0000	B96 B98	S
436	female	21.0	2	2	W./C. 6608	34.3750	NaN	S
437	female	24.0	2	3	29106	18.7500	NaN	S
438	male	64.0	1	4	19950	263.0000	C23 C25 C27	S

439 rows × 8 columns

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

axis： 0是行，1是列

data1=pd.concat([train_left_up,train_right_up],axis=1)
data1

	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
2	3	1	3	Heikkinen, Miss. Laina	female	26.0	0	0	STON/O2. 3101282	7.9250	NaN	S
3	4	1	1	Futrelle, Mrs. Jacques Heath (Lily May Peel)	female	35.0	1	0	113803	53.1000	C123	S
4	5	0	3	Allen, Mr. William Henry	male	35.0	0	0	373450	8.0500	NaN	S
...	...	...	...	...	...	...	...	...	...	...	...	...
434	435	0	1	Silvey, Mr. William Baird	male	50.0	1	0	13507	55.9000	E44	S
435	436	1	1	Carter, Miss. Lucile Polk	female	14.0	1	2	113760	120.0000	B96 B98	S
436	437	0	3	Ford, Miss. Doolina Margaret "Daisy"	female	21.0	2	2	W./C. 6608	34.3750	NaN	S
437	438	1	2	Richards, Mrs. Sidney (Emily Hocking)	female	24.0	2	3	29106	18.7500	NaN	S
438	439	0	1	Fortune, Mr. Mark	male	64.0	1	4	19950	263.0000	C23 C25 C27	S

439 rows × 12 columns

2.4.3 任务三：使用concat方法：将train-left-down和train-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。

#写入代码
data2=pd.concat([train_left_down,train_right_down],axis=1)

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

#写入代码
# data1 = train_left_up.join(train_right_up)
# data1 = pd.merge(train_left_up,train_right_up,left_index=True,right_index=True)
data=pd.concat([data1,data2],axis=0)
data

	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
2	3	1	3	Heikkinen, Miss. Laina	female	26.0	0	0	STON/O2. 3101282	7.9250	NaN	S
3	4	1	1	Futrelle, Mrs. Jacques Heath (Lily May Peel)	female	35.0	1	0	113803	53.1000	C123	S
4	5	0	3	Allen, Mr. William Henry	male	35.0	0	0	373450	8.0500	NaN	S
...	...	...	...	...	...	...	...	...	...	...	...	...
447	887	0	2	Montvila, Rev. Juozas	male	27.0	0	0	211536	13.0000	NaN	S
448	888	1	1	Graham, Miss. Margaret Edith	female	19.0	0	0	112053	30.0000	B42	S
449	889	0	3	Johnston, Miss. Catherine Helen "Carrie"	female	NaN	1	2	W./C. 6607	23.4500	NaN	S
450	890	1	1	Behr, Mr. Karl Howell	male	26.0	0	0	111369	30.0000	C148	C
451	891	0	3	Dooley, Mr. Patrick	male	32.0	0	0	370376	7.7500	NaN	Q

891 rows × 12 columns

2.4.6 任务六：完成的数据保存为result.csv

#写入代码
data.to_csv(r"./result.csv",encoding="utf_8_sig")

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

等同于行专列 不过注意的是Serie是一维度数据

#写入代码
data.stack()

0    PassengerId                          1
     Survived                             0
     Pclass                               3
     Name           Braund, Mr. Owen Harris
     Sex                               male
                             ...           
451  SibSp                                0
     Parch                                0
     Ticket                          370376
     Fare                              7.75
     Embarked                             Q
Length: 9826, dtype: object

#写入代码