泰坦尼克号 数据重构 + 可视化

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。

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

# 载入data文件中的:train-left-up.csv
df = pd.read_csv('DataWhale教程/data/train-left-up.csv')
df.head()

	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

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，与之前的原始数据相比，观察他们的之间的关系

# 四个边角的数据
left_up = pd.read_csv("DataWhale教程/data/train-left-up.csv")
left_down = pd.read_csv("DataWhale教程/data/train-left-down.csv")
right_up = pd.read_csv("DataWhale教程/data/train-right-up.csv")
right_down = pd.read_csv("DataWhale教程/data/train-right-down.csv")

left_up.head()

	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

left_down.head()

	PassengerId	Survived	Pclass	Name
0	440	0	2	Kvillner, Mr. Johan Henrik Johannesson
1	441	1	2	Hart, Mrs. Benjamin (Esther Ada Bloomfield)
2	442	0	3	Hampe, Mr. Leon
3	443	0	3	Petterson, Mr. Johan Emil
4	444	1	2	Reynaldo, Ms. Encarnacion

right_down.head()

	Sex	Age	SibSp	Parch	Ticket	Fare	Cabin	Embarked
0	male	31.0	0	0	C.A. 18723	10.500	NaN	S
1	female	45.0	1	1	F.C.C. 13529	26.250	NaN	S
2	male	20.0	0	0	345769	9.500	NaN	S
3	male	25.0	1	0	347076	7.775	NaN	S
4	female	28.0	0	0	230434	13.000	NaN	S

right_up.head()

	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

【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

# 将左上和右上合并
result_up = pd.concat([left_up,right_up],axis=1)
result_up.head()

	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

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。

# 将左下和右下合并
result_down = pd.concat([left_down,right_down],axis=1)
# 将完整的上和下进行纵向合并
result = pd.concat([result_up,result_down],axis=0)
result.shape

(891, 12)

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

resul_up = left_up.join(right_up) # 左右合并
result_down = left_down.join(right_down) # 左右合并
result = result_up.append(result_down) # 上下合并
result.shape

(891, 12)

1. join 把列横向合并 左右并
2. append 把行纵向合并 上下并

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