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

df = pd.DataFrame([[1.4, np.nan],[np.nan, 2]],index=['a','b'],columns=['one','two'])
df
one two
a 1.4 NaN
b NaN 2.0 
df.one + df.two

a   NaN
b   NaN
dtype: float64

df.sum(skipna=False)

one   NaN
two   NaN
dtype: float64

df.iloc[0,0]+df.iloc[1,0]

nan

# 载入data文件中的:train-left-up.csv
df10 = pd.read_csv(r'data/train-left-up.csv')
df10
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
df_left_up = pd.read_csv("data/train-left-up.csv")
df_left_down = pd.read_csv("data/train-left-down.csv")
df_right_up = pd.read_csv("data/train-right-up.csv")
df_right_down = pd.read_csv("data/train-right-down.csv")
#写入代码
df_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 
df_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 
df_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 
df_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

【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
result_up = pd.concat([df_left_up,df_right_up],axis =1)
result_up
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
result_down = pd.concat([df_left_down,df_right_down],axis=1)
result_down
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 10.500 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 0 3 Hampe, Mr. Leon male 20.0 0 0 345769 9.500 NaN S
3 443 0 3 Petterson, Mr. Johan Emil male 25.0 1 0 347076 7.775 NaN S
4 444 1 2 Reynaldo, Ms. Encarnacion female 28.0 0 0 230434 13.000 NaN S
... ... ... ... ... ... ... ... ... ... ... ... ...
447 887 0 2 Montvila, Rev. Juozas male 27.0 0 0 211536 13.000 NaN S
448 888 1 1 Graham, Miss. Margaret Edith female 19.0 0 0 112053 30.000 B42 S
449 889 0 3 Johnston, Miss. Catherine Helen "Carrie" female NaN 1 2 W./C. 6607 23.450 NaN S
450 890 1 1 Behr, Mr. Karl Howell male 26.0 0 0 111369 30.000 C148 C
451 891 0 3 Dooley, Mr. Patrick male 32.0 0 0 370376 7.750 NaN Q

452 rows × 12 columns

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

2.4.4 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
result_up = df_left_up.join(df_right_up)
result_down = df_left_down.join(df_right_down)

result = result_up.append(result_down)
result

C:\Users\Ji-Luo\AppData\Local\Temp\ipykernel_11888\552922610.py:1: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.result = result_up.append(result_down)
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

2.4.5 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
result_up = pd.merge(df_left_up,df_right_up,left_index=True,right_index=True)
result_up
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

result_down = pd.merge(df_left_down,df_right_down,left_index=True,right_index=True)
result_down
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 10.500 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 0 3 Hampe, Mr. Leon male 20.0 0 0 345769 9.500 NaN S
3 443 0 3 Petterson, Mr. Johan Emil male 25.0 1 0 347076 7.775 NaN S
4 444 1 2 Reynaldo, Ms. Encarnacion female 28.0 0 0 230434 13.000 NaN S
... ... ... ... ... ... ... ... ... ... ... ... ...
447 887 0 2 Montvila, Rev. Juozas male 27.0 0 0 211536 13.000 NaN S
448 888 1 1 Graham, Miss. Margaret Edith female 19.0 0 0 112053 30.000 B42 S
449 889 0 3 Johnston, Miss. Catherine Helen "Carrie" female NaN 1 2 W./C. 6607 23.450 NaN S
450 890 1 1 Behr, Mr. Karl Howell male 26.0 0 0 111369 30.000 C148 C
451 891 0 3 Dooley, Mr. Patrick male 32.0 0 0 370376 7.750 NaN Q

452 rows × 12 columns

result = result_up.append(result_down)
result

C:\Users\Ji-Luo\AppData\Local\Temp\ipykernel_11888\552922610.py:1: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.result = result_up.append(result_down)
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

【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
result.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

#写入代码
unit_result=result.stack().head(20)
unit_result
0  PassengerId                                                    1Survived                                                       0Pclass                                                         3Name                                     Braund, Mr. Owen HarrisSex                                                         maleAge                                                         22.0SibSp                                                          1Parch                                                          0Ticket                                                 A/5 21171Fare                                                        7.25Embarked                                                       S
1  PassengerId                                                    2Survived                                                       1Pclass                                                         1Name           Cumings, Mrs. John Bradley (Florence Briggs Th...Sex                                                       femaleAge                                                         38.0SibSp                                                          1Parch                                                          0Ticket                                                  PC 17599
dtype: object

#写入代码

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('result.csv')
df.head()
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 0.0 A/5 21171 7.2500 NaN S
1 1 2 1 1 Cumings, Mrs. John Bradley (Florence Briggs Th... female 38.0 1.0 0.0 PC 17599 71.2833 C85 C
2 2 3 1 3 Heikkinen, Miss. Laina female 26.0 0.0 0.0 STON/O2. 3101282 7.9250 NaN S
3 3 4 1 1 Futrelle, Mrs. Jacques Heath (Lily May Peel) female 35.0 1.0 0.0 113803 53.1000 C123 S
4 4 5 0 3 Allen, Mr. William Henry male 35.0 0.0 0.0 373450 8.0500 NaN S

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

#写入心得

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
df.groupby('Sex')['Fare'].mean()

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

# 写入代码
df.groupby('Sex')['Survived'].sum()

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
df.groupby('Pclass')['Survived'].sum()

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

#思考心得
df.groupby('Pclass')['Survived'].apply(lambda x: x.sum() / x.count())
Pclass
1    0.629630
2    0.472826
3    0.242363
Name: Survived, dtype: float64

【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？

#思考心得
df.groupby('Sex').agg({'Fare':'mean','Pclass':'count'}).rename(columns = {'Fare':'mean_fare','Pclass':'count_pclass'})
mean_fare count_pclass
Sex
female 44.479818 314
male 25.523893 577

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()

Pclass  Age
1       0.92     151.55002.00     151.55004.00      81.858311.00    120.000014.00    120.0000...
3       61.00      6.237563.00      9.587565.00      7.750070.50      7.750074.00      7.7750
Name: Fare, Length: 182, 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
df1 = df.groupby('Sex')['Fare'].mean()
df2 = df.groupby('Sex')['Survived'].sum()
pd.merge(df1,df2,on='Sex')
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，然后找出存活人数最多的年龄段，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
df['Age2'] = pd.cut(df['Age'],[0,5,15,30,50,80])
chrs = df.groupby('Age2')['Survived'].sum()
chrs

Age2
(0, 5]       44
(5, 15]      39
(15, 30]    326
(30, 50]    241
(50, 80]     64
Name: Survived, dtype: int64

# 写入代码
chrs.idxmax()

Interval(15, 30, closed='right')

# 写入代码# 各年龄段/各年龄段总人数存活率
df.groupby('Age2')['Survived'].apply(lambda x:x.sum() / x.count())

Age2
(0, 5]      0.704545
(5, 15]     0.461538
(15, 30]    0.358896
(30, 50]    0.423237
(50, 80]    0.343750
Name: Survived, dtype: float64

# 写入代码
# 总人数
df.shape[0]
# 存活人数# 各年龄段/总人数存活率
df.groupby('Age2')['Survived'].apply(lambda x:x.sum() / df.shape[0])
Age2
(0, 5]      0.034792
(5, 15]     0.020202
(15, 30]    0.131313
(30, 50]    0.114478
(50, 80]    0.024691
Name: Survived, dtype: float64

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