参考来源：https://github.com/datawhalechina/hands-on-data-analysis
开始之前，导入numpy、pandas包和数据
# 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("data/train-left-up.csv")
| PassengerId | Survived | Pclass | Name |
| 1 | 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 | 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，观察数据的之间的关系
from IPython.core.interactiveshell import InteractiveShell
InteractiveShell.ast_node_interactivity = "all"
text_left_up = pd.read_csv("data/train-left-up.csv")
text_left_up.head()
text_left_down = pd.read_csv("data/train-left-down.csv")
text_left_down.head()
text_right_up = pd.read_csv("data/train-right-up.csv")
text_right_up.head()
text_right_down = pd.read_csv("data/train-right-down.csv")
text_right_down.head()
| PassengerId | Survived | Pclass | Name |
| 1 | 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 | 3 | Allen, Mr. William Henry |
| PassengerId | Survived | Pclass | Name |
| 440 | 2 | Kvillner, Mr. Johan Henrik Johannesson |
| 1 | 441 | 1 | 2 | Hart, Mrs. Benjamin (Esther Ada Bloomfield) |
| 2 | 442 | 3 | Hampe, Mr. Leon |
| 3 | 443 | 3 | Petterson, Mr. Johan Emil |
| 4 | 444 | 1 | 2 | Reynaldo, Ms. Encarnacion |
| Sex | Age | SibSp | Parch | Ticket | Fare | Cabin | Embarked |
| male | 22.0 | 1 | A/5 21171 | 7.2500 | NaN | S |
| 1 | female | 38.0 | 1 | PC 17599 | 71.2833 | C85 | C |
| 2 | female | 26.0 | STON/O2. 3101282 | 7.9250 | NaN | S |
| 3 | female | 35.0 | 1 | 113803 | 53.1000 | C123 | S |
| 4 | male | 35.0 | 373450 | 8.0500 | NaN | S |
| Sex | Age | SibSp | Parch | Ticket | Fare | Cabin | Embarked |
| male | 31.0 | 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 | 345769 | 9.500 | NaN | S |
| 3 | male | 25.0 | 1 | 347076 | 7.775 | NaN | S |
| 4 | female | 28.0 | 230434 | 13.000 | 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
#写入代码
list_up = [text_left_up,text_right_up]
result_up = pd.concat(list_up,axis=1)
result_up.head()
list_down = [text_left_down,text_right_down]
result_down = pd.concat(list_down,axis=1)
result_down.head()
| PassengerId | Survived | Pclass | Name | Sex | Age | SibSp | Parch | Ticket | Fare | Cabin | Embarked |
| 1 | 3 | Braund, Mr. Owen Harris | male | 22.0 | 1 | A/5 21171 | 7.2500 | NaN | S |
| 1 | 2 | 1 | 1 | Cumings, Mrs. John Bradley (Florence Briggs Th... | female | 38.0 | 1 | PC 17599 | 71.2833 | C85 | C |
| 2 | 3 | 1 | 3 | Heikkinen, Miss. Laina | female | 26.0 | STON/O2. 3101282 | 7.9250 | NaN | S |
| 3 | 4 | 1 | 1 | Futrelle, Mrs. Jacques Heath (Lily May Peel) | female | 35.0 | 1 | 113803 | 53.1000 | C123 | S |
| 4 | 5 | 3 | Allen, Mr. William Henry | male | 35.0 | 373450 | 8.0500 | NaN | S |
| PassengerId | Survived | Pclass | Name | Sex | Age | SibSp | Parch | Ticket | Fare | Cabin | Embarked |
| 440 | 2 | Kvillner, Mr. Johan Henrik Johannesson | male | 31.0 | C.A. 18723 | 10.500 | NaN | S |
| 1 | 441 | 1 | 2 | Hart, Mrs. Benjamin (Esther Ada Bloomfield) | female | 45.0 | 1 | 1 | F.C.C. 13529 | 26.250 | NaN | S |
| 2 | 442 | 3 | Hampe, Mr. Leon | male | 20.0 | 345769 | 9.500 | NaN | S |
| 3 | 443 | 3 | Petterson, Mr. Johan Emil | male | 25.0 | 1 | 347076 | 7.775 | NaN | S |
| 4 | 444 | 1 | 2 | Reynaldo, Ms. Encarnacion | female | 28.0 | 230434 | 13.000 | 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。
#写入代码
result = pd.concat([result_up,result_down])
result.head()
| PassengerId | Survived | Pclass | Name | Sex | Age | SibSp | Parch | Ticket | Fare | Cabin | Embarked |
| 1 | 3 | Braund, Mr. Owen Harris | male | 22.0 | 1 | A/5 21171 | 7.2500 | NaN | S |
| 1 | 2 | 1 | 1 | Cumings, Mrs. John Bradley (Florence Briggs Th... | female | 38.0 | 1 | PC 17599 | 71.2833 | C85 | C |
| 2 | 3 | 1 | 3 | Heikkinen, Miss. Laina | female | 26.0 | STON/O2. 3101282 | 7.9250 | NaN | S |
| 3 | 4 | 1 | 1 | Futrelle, Mrs. Jacques Heath (Lily May Peel) | female | 35.0 | 1 | 113803 | 53.1000 | C123 | S |
| 4 | 5 | 3 | Allen, Mr. William Henry | male | 35.0 | 373450 | 8.0500 | 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
#写入代码
result2_up = text_left_up.join(text_right_up)
result2_down = text_left_down.join(text_right_down)
result2 = result2_up.append(result2_down)
result2.head()
| PassengerId | Survived | Pclass | Name | Sex | Age | SibSp | Parch | Ticket | Fare | Cabin | Embarked |
| 1 | 3 | Braund, Mr. Owen Harris | male | 22.0 | 1 | A/5 21171 | 7.2500 | NaN | S |
| 1 | 2 | 1 | 1 | Cumings, Mrs. John Bradley (Florence Briggs Th... | female | 38.0 | 1 | PC 17599 | 71.2833 | C85 | C |
| 2 | 3 | 1 | 3 | Heikkinen, Miss. Laina | female | 26.0 | STON/O2. 3101282 | 7.9250 | NaN | S |
| 3 | 4 | 1 | 1 | Futrelle, Mrs. Jacques Heath (Lily May Peel) | female | 35.0 | 1 | 113803 | 53.1000 | C123 | S |
| 4 | 5 | 3 | Allen, Mr. William Henry | male | 35.0 | 373450 | 8.0500 | NaN | S |
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
#写入代码
result3_up = pd.merge(text_left_up,text_right_up,left_index=True,right_index=True)
result3_down = pd.merge(text_left_down,text_right_down,left_index=True,right_index=True)
result3 = result3_up.append(result3_down)
result3.head()
| PassengerId | Survived | Pclass | Name | Sex | Age | SibSp | Parch | Ticket | Fare | Cabin | Embarked |
| 1 | 3 | Braund, Mr. Owen Harris | male | 22.0 | 1 | A/5 21171 | 7.2500 | NaN | S |
| 1 | 2 | 1 | 1 | Cumings, Mrs. John Bradley (Florence Briggs Th... | female | 38.0 | 1 | PC 17599 | 71.2833 | C85 | C |
| 2 | 3 | 1 | 3 | Heikkinen, Miss. Laina | female | 26.0 | STON/O2. 3101282 | 7.9250 | NaN | S |
| 3 | 4 | 1 | 1 | Futrelle, Mrs. Jacques Heath (Lily May Peel) | female | 35.0 | 1 | 113803 | 53.1000 | C123 | S |
| 4 | 5 | 3 | Allen, Mr. William Henry | male | 35.0 | 373450 | 8.0500 | NaN | S |
【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？
【答】merge是连接，类似sql中的join，有内连，左连，右连和全连接几种。join是根据相同index进行连接，对于名称相同的属性，会保留。concat是两个数据集直接合并，可以在列上合并，也可在行上合并。
2.4.6 任务六：完成的数据保存为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
stack函数会将数据从”表格结构“变成”花括号结构“，即将其行索引变成列索引，反之，unstack函数将数据从”花括号结构“变成”表格结构“，即要将其中一层的列索引变成行索引。
text = pd.read_csv('data/result.csv')
text.head()
unit_result=text.stack().head(20)
unit_result.head()
0 Unnamed: 0 0
PassengerId 1
Survived 0
Pclass 3
Name Braund, Mr. Owen Harris
dtype: object
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 numpy as np
import pandas as pd
# 载入上一个任务人保存的文件中:result.csv，并查看这个文件
df = pd.read_csv("data/result.csv")
df.head()
| Unnamed: 0 | PassengerId | Survived | Pclass | Name | Sex | Age | SibSp | Parch | Ticket | Fare | Cabin | Embarked |
| 1 | 3 | Braund, Mr. Owen Harris | male | 22.0 | 1 | A/5 21171 | 7.2500 | NaN | S |
| 1 | 1 | 2 | 1 | 1 | Cumings, Mrs. John Bradley (Florence Briggs Th... | female | 38.0 | 1 | PC 17599 | 71.2833 | C85 | C |
| 2 | 2 | 3 | 1 | 3 | Heikkinen, Miss. Laina | female | 26.0 | STON/O2. 3101282 | 7.9250 | NaN | S |
| 3 | 3 | 4 | 1 | 1 | Futrelle, Mrs. Jacques Heath (Lily May Peel) | female | 35.0 | 1 | 113803 | 53.1000 | C123 | S |
| 4 | 4 | 5 | 3 | Allen, Mr. William Henry | male | 35.0 | 373450 | 8.0500 | NaN | S |
0 608
1 209
2 28
4 18
3 16
8 7
5 5
Name: SibSp, dtype: int64
2 第二章：数据重构
第一部分：数据聚合与运算
2.6 数据运用
2.6.1 任务一：通过教材《Python for Data Analysis》P303、Google or anything来学习了解GroupBy机制
2.4.2：任务二：计算泰坦尼克号男性与女性的平均票价
# 写入代码
mean = df['Fare'].groupby(df['Sex']).mean()
mean.head()
Sex
female 44.479818
male 25.523893
Name: Fare, dtype: float64
在了解GroupBy机制之后，运用这个机制完成一系列的操作，来达到我们的目的。
下面通过几个任务来熟悉GroupBy机制。
2.4.3：任务三：统计泰坦尼克号中男女的存活人数
survived_num = df['Survived'].groupby(df['Sex']).sum()
survived_num.head()
Sex
female 233
male 109
Name: Survived, dtype: int64
2.4.4：任务四：计算客舱不同等级的存活人数
# 写入代码
survived_class = df['Survived'].groupby(df['Pclass']).sum()
survived_class.head()
Pclass
1 136
2 87
3 119
Name: Survived, dtype: int64
【提示：】表中的存活那一栏，可以发现如果还活着记为1，死亡记为0
【思考】从数据分析的角度，上面的统计结果可以得出那些结论
【思考】从任务二到任务三中，这些运算可以通过agg()函数来同时计算。并且可以使用rename函数修改列名。你可以按照提示写出这个过程吗？
2.4.5：任务五：统计在不同等级的票中的不同年龄的船票花费的平均值
# 写入代码
df.groupby(['Pclass','Age'])['Fare'].mean().head()
Pclass Age
1 0.92 151.5500
2.00 151.5500
4.00 81.8583
11.00 120.0000
14.00 120.0000
Name: Fare, dtype: float64
2.4.6：任务六：将任务二和任务三的数据合并，并保存到sex_fare_survived.csv
# 写入代码
result = pd.merge(mean,survived_num,on='Sex')
result.head()
| Fare | Survived |
| Sex |
| female | 44.479818 | 233 |
| male | 25.523893 | 109 |
2.4.7：任务七：得出不同年龄的总的存活人数，然后找出存活人数的最高的年龄，最后计算存活人数最高的存活率（存活人数/总人数）
# 写入代码
survived_age = df['Survived'].groupby(df['Age']).sum()
survived_age.head()
Age
0.42 1
0.67 1
0.75 2
0.83 2
0.92 1
Name: Survived, dtype: int64
# 写入代码
survived_age[survived_age.values==survived_age.max()]
Age
24.0 15
Name: Survived, dtype: int64
# 写入代码
_sum = df['Survived'].sum()
print(_sum)
342
# 写入代码
_sum = df['Survived'].sum()
print("sum of person:"+str(_sum))
precetn =survived_age.max()/_sum
print("最大存活率："+str(precetn))
sum of person:342
最大存活率：0.043859649122807015
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