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
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import numpy as np
import pandas as pd
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text=pd.read_csv('../第二章项目集合/data/train-left-up.csv')
text.head()
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PassengerId
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Survived
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Pclass
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Name
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0
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1
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0
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3
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Braund, Mr. Owen Harris
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1
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2
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1
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1
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Cumings, Mrs. John Bradley (Florence Briggs Th…
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2
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3
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1
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3
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Heikkinen, Miss. Laina
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3
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4
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1
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1
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Futrelle, Mrs. Jacques Heath (Lily May Peel)
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4
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5
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0
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3
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Allen, Mr. William Henry
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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
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text_left_up=pd.read_csv('../第二章项目集合/data/train-left-up.csv')
text_left_down=pd.read_csv('../第二章项目集合/data/train-left-down.csv')
text_right_up=pd.read_csv('../第二章项目集合/data/train-right-up.csv')
text_right_down=pd.read_csv('../第二章项目集合/data/train-right-down.csv')
text_left_up.head()
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PassengerId
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Survived
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Pclass
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Name
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0
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1
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0
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3
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Braund, Mr. Owen Harris
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1
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2
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1
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1
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Cumings, Mrs. John Bradley (Florence Briggs Th…
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2
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3
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1
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3
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Heikkinen, Miss. Laina
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3
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4
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1
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1
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Futrelle, Mrs. Jacques Heath (Lily May Peel)
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4
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5
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0
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3
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Allen, Mr. William Henry
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【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
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list_up = [text_left_up,text_right_up]
result_up = pd.concat(list_up,axis=1)
result_up.head()
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PassengerId
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Survived
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Pclass
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Name
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Sex
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Age
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SibSp
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Parch
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Ticket
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Fare
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Cabin
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Embarked
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0
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1
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0
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3
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Braund, Mr. Owen Harris
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male
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22.0
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1
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0
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A/5 21171
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7.2500
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NaN
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S
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1
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2
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1
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1
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Cumings, Mrs. John Bradley (Florence Briggs Th…
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female
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38.0
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1
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0
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PC 17599
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71.2833
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C85
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C
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2
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3
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1
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3
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Heikkinen, Miss. Laina
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female
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26.0
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0
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0
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STON/O2. 3101282
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7.9250
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NaN
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S
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3
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4
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1
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1
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Futrelle, Mrs. Jacques Heath (Lily May Peel)
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female
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35.0
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1
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0
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113803
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53.1000
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C123
|
S
|
|
4
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5
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0
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3
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Allen, Mr. William Henry
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male
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35.0
|
0
|
0
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373450
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8.0500
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NaN
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S
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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。
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list_down=[text_left_down,text_right_down]
result_down = pd.concat(list_down,axis=1)
result = pd.concat([result_up,result_down])
result.head()
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PassengerId
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Survived
|
Pclass
|
Name
|
Sex
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Age
|
SibSp
|
Parch
|
Ticket
|
Fare
|
Cabin
|
Embarked
|
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0
|
1
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0
|
3
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Braund, Mr. Owen Harris
|
male
|
22.0
|
1
|
0
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A/5 21171
|
7.2500
|
NaN
|
S
|
|
1
|
2
|
1
|
1
|
Cumings, Mrs. John Bradley (Florence Briggs Th…
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female
|
38.0
|
1
|
0
|
PC 17599
|
71.2833
|
C85
|
C
|
|
2
|
3
|
1
|
3
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Heikkinen, Miss. Laina
|
female
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26.0
|
0
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0
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STON/O2. 3101282
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7.9250
|
NaN
|
S
|
|
3
|
4
|
1
|
1
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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
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35.0
|
0
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0
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373450
|
8.0500
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NaN
|
S
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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
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resul_up = text_left_up.join(text_right_up)
result_down = text_left_down.join(text_right_down)
result = result_up.append(result_down)
result.head()
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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
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'''
该代码使用 pandas.merge 方法，以索引（index）为键，将两个 DataFrame (text_left_up 和 text_right_up) 横向合并。
'''
result_up = pd.merge(text_left_up,text_right_up,left_index=True,right_index=True)
result_down = pd.merge(text_left_down,text_right_down,left_index=True,right_index=True)
result = resul_up.append(result_down)
result.head()
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【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
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result.to_csv('result.csv')
result.head()
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PassengerId
|
Survived
|
Pclass
|
Name
|
Sex
|
Age
|
SibSp
|
Parch
|
Ticket
|
Fare
|
Cabin
|
Embarked
|
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0
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1
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0
|
3
|
Braund, Mr. Owen Harris
|
male
|
22.0
|
1
|
0
|
A/5 21171
|
7.2500
|
NaN
|
S
|
|
1
|
2
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1
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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
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NaN
|
S
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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
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text = pd.read_csv('result.csv')
unit_result=text.stack().head(20)
unit_result.head()
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0 Unnamed: 0 0
PassengerId 1
Survived 0
Pclass 3
Name Braund, Mr. Owen Harris
dtype: object
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unit_result.to_csv('unit_result.csv')
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