第二章：第二节数据重构

其他 2020-09-08 17:12:17 阅读次数: 0

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data文件中的:train-left-up.csv
text = pd.read_csv('./data/train-left-up.csv')
text.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，观察数据的之间的关系

#写入代码
text_left_up = pd.read_csv("data/train-left-up.csv")
text_left_down = pd.read_csv("data/train-left-down.csv")
text_right_up = pd.read_csv("data/train-right-up.csv")
text_right_down = pd.read_csv("data/train-right-down.csv")

#写入代码
display(text_left_up.tail())
display(text_left_down.head())
PassengerId Survived Pclass Name
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
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 
display(text_right_up.tail())
display(text_right_down.head())
Sex Age SibSp Parch Ticket Fare Cabin Embarked
886 male 27.0 0.0 0.0 211536 13.00 NaN S
887 female 19.0 0.0 0.0 112053 30.00 B42 S
888 female NaN 1.0 2.0 W./C. 6607 23.45 NaN S
889 male 26.0 0.0 0.0 111369 30.00 C148 C
890 male 32.0 0.0 0.0 370376 7.75 NaN Q
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([text_left_up,text_right_up.loc[:438]],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 0.0 A/5 21171 7.2500 NaN S
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 3 1 3 Heikkinen, Miss. Laina female 26.0 0.0 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 0.0 113803 53.1000 C123 S
4 5 0 3 Allen, Mr. William Henry male 35.0 0.0 0.0 373450 8.0500 NaN S
... ... ... ... ... ... ... ... ... ... ... ... ...
434 435 0 1 Silvey, Mr. William Baird male 50.0 1.0 0.0 13507 55.9000 E44 S
435 436 1 1 Carter, Miss. Lucile Polk female 14.0 1.0 2.0 113760 120.0000 B96 B98 S
436 437 0 3 Ford, Miss. Doolina Margaret "Daisy" female 21.0 2.0 2.0 W./C. 6608 34.3750 NaN S
437 438 1 2 Richards, Mrs. Sidney (Emily Hocking) female 24.0 2.0 3.0 29106 18.7500 NaN S
438 439 0 1 Fortune, Mr. Mark male 64.0 1.0 4.0 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([text_left_down,text_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

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=text_left_up.join(text_right_up)
result_down=text_left_down.join(text_right_down)
result=result_up.append(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 0.0 A/5 21171 7.2500 NaN S
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 3 1 3 Heikkinen, Miss. Laina female 26.0 0.0 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 0.0 113803 53.1000 C123 S
4 5 0 3 Allen, Mr. William Henry male 35.0 0.0 0.0 373450 8.0500 NaN S
... ... ... ... ... ... ... ... ... ... ... ... ...
447 887 0 2 Montvila, Rev. Juozas male 27.0 0.0 0.0 211536 13.0000 NaN S
448 888 1 1 Graham, Miss. Margaret Edith female 19.0 0.0 0.0 112053 30.0000 B42 S
449 889 0 3 Johnston, Miss. Catherine Helen "Carrie" female NaN 1.0 2.0 W./C. 6607 23.4500 NaN S
450 890 1 1 Behr, Mr. Karl Howell male 26.0 0.0 0.0 111369 30.0000 C148 C
451 891 0 3 Dooley, Mr. Patrick male 32.0 0.0 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(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=result_up.append(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 0.0 A/5 21171 7.2500 NaN S
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 3 1 3 Heikkinen, Miss. Laina female 26.0 0.0 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 0.0 113803 53.1000 C123 S
4 5 0 3 Allen, Mr. William Henry male 35.0 0.0 0.0 373450 8.0500 NaN S
... ... ... ... ... ... ... ... ... ... ... ... ...
447 887 0 2 Montvila, Rev. Juozas male 27.0 0.0 0.0 211536 13.0000 NaN S
448 888 1 1 Graham, Miss. Margaret Edith female 19.0 0.0 0.0 112053 30.0000 B42 S
449 889 0 3 Johnston, Miss. Catherine Helen "Carrie" female NaN 1.0 2.0 W./C. 6607 23.4500 NaN S
450 890 1 1 Behr, Mr. Karl Howell male 26.0 0.0 0.0 111369 30.0000 C148 C
451 891 0 3 Dooley, Mr. Patrick male 32.0 0.0 0.0 370376 7.7500 NaN Q

891 rows × 12 columns

利用merge来做纵列的合并

result_left=pd.merge(text_left_up,text_left_down,how='outer')

text_right_up['key']=0
text_right_down['key']=1
text_right_up
text_right_down
result_right=pd.merge(text_right_up.loc[:438],text_right_down,how='outer')

result=pd.merge(result_left,result_right.iloc[:,:-1],left_index=True,right_index=True)
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 0.0 A/5 21171 7.2500 NaN S
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 3 1 3 Heikkinen, Miss. Laina female 26.0 0.0 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 0.0 113803 53.1000 C123 S
4 5 0 3 Allen, Mr. William Henry male 35.0 0.0 0.0 373450 8.0500 NaN S
... ... ... ... ... ... ... ... ... ... ... ... ...
886 887 0 2 Montvila, Rev. Juozas male 27.0 0.0 0.0 211536 13.0000 NaN S
887 888 1 1 Graham, Miss. Margaret Edith female 19.0 0.0 0.0 112053 30.0000 B42 S
888 889 0 3 Johnston, Miss. Catherine Helen "Carrie" female NaN 1.0 2.0 W./C. 6607 23.4500 NaN S
889 890 1 1 Behr, Mr. Karl Howell male 26.0 0.0 0.0 111369 30.0000 C148 C
890 891 0 3 Dooley, Mr. Patrick male 32.0 0.0 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？

  • append像序列中添加行
  • assign 更像是左连接，一列一列的像序列中添加列
  • combine 求两个表的并集
  • update 把一个表里面的元素参照另一个表进行更新
  • concat 可以将两个表在两个维度进行拼接
  • merge 通过指定一个key来将两个表进行连接
  • join 也可以指定key，默认的话就是按照index来进行连接

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

stack函数可以看做将横向的索引放到纵向，因此功能类似与melt，参数level可指定变化的列索引是哪一层（或哪几层，需要列表）

#写入代码
text = pd.read_csv('result.csv')
text.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 
#写入代码
unit_result=text.stack().head(20)
unit_result


0  Unnamed: 0                                                     0
   PassengerId                                                    1
   Survived                                                       0
   Pclass                                                         3
   Name                                     Braund, Mr. Owen Harris
   Sex                                                         male
   Age                                                           22
   SibSp                                                          1
   Parch                                                          0
   Ticket                                                 A/5 21171
   Fare                                                        7.25
   Embarked                                                       S
1  Unnamed: 0                                                     1
   PassengerId                                                    2
   Survived                                                       1
   Pclass                                                         1
   Name           Cumings, Mrs. John Bradley (Florence Briggs Th...
   Sex                                                       female
   Age                                                           38
   SibSp                                                          1
dtype: object

unit_result.unstack()
Unnamed: 0 PassengerId Survived Pclass Name Sex Age SibSp Parch Ticket Fare Embarked
0 0 1 0 3 Braund, Mr. Owen Harris male 22 1 0 A/5 21171 7.25 S
1 1 2 1 1 Cumings, Mrs. John Bradley (Florence Briggs Th... female 38 1 NaN NaN NaN NaN 
#将代码保存为unit_result,csv
unit_result.to_csv('unit_result.csv')

test = pd.read_csv('unit_result.csv')

test.head()
Unnamed: 0 Unnamed: 1 0
0 0 Unnamed: 0 0
1 0 PassengerId 1
2 0 Survived 0
3 0 Pclass 3
4 0 Name Braund, Mr. Owen Harris 


test.iloc[:,1:]
Unnamed: 1 0
0 Unnamed: 0 0
1 PassengerId 1
2 Survived 0
3 Pclass 3
4 Name Braund, Mr. Owen Harris
5 Sex male
6 Age 22.0
7 SibSp 1.0
8 Parch 0.0
9 Ticket A/5 21171
10 Fare 7.25
11 Embarked S
12 Unnamed: 0 1
13 PassengerId 2
14 Survived 1
15 Pclass 1
16 Name Cumings, Mrs. John Bradley (Florence Briggs Th...
17 Sex female
18 Age 38.0
19 SibSp 1.0 
test.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 20 entries, 0 to 19
Data columns (total 3 columns):
 #   Column      Non-Null Count  Dtype 
---  ------      --------------  ----- 
 0   Unnamed: 0  20 non-null     int64 
 1   Unnamed: 1  20 non-null     object
 2   0           20 non-null     object
dtypes: int64(1), object(2)
memory usage: 608.0+ bytes

# test.pivot_table(index=test.index,columns='Unnamed: 1',values='0')

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

# 载入上一个任务人保存的文件中:result.csv，并查看这个文件
text = pd.read_csv('result.csv')
text.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
text.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
text.groupby('Sex')['Survived'].sum()

Sex
female    233
male      109
Name: Survived, dtype: int64

text.groupby('Sex')['Survived'].count()

Sex
female    314
male      577
Name: Survived, dtype: int64

text.groupby('Sex')['Survived'].sum()/text.groupby('Sex')['Survived'].count()

Sex
female    0.742038
male      0.188908
Name: Survived, dtype: float64

2.4.4：任务四：计算客舱不同等级的存活人数

# 写入代码
text.groupby('Pclass')['Survived'].sum()

Pclass
1    136
2     87
3    119
Name: Survived, dtype: int64

text.groupby('Pclass')['Survived'].count()

Pclass
1    216
2    184
3    491
Name: Survived, dtype: int64

text.groupby('Pclass')['Survived'].sum()/text.groupby('Pclass')['Survived'].count()

Pclass
1    0.629630
2    0.472826
3    0.242363
Name: Survived, dtype: float64

统计不同等级客舱中男女比例

text.groupby(['Pclass','Sex']).head(1)
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
6 6 7 0 1 McCarthy, Mr. Timothy J male 54.0 0.0 0.0 17463 51.8625 E46 S
9 9 10 1 2 Nasser, Mrs. Nicholas (Adele Achem) female 14.0 1.0 0.0 237736 30.0708 NaN C
17 17 18 1 2 Williams, Mr. Charles Eugene male NaN 0.0 0.0 244373 13.0000 NaN S 
text.groupby(['Pclass','Sex'])['PassengerId'].count()/[94+76+144,122+108+347,94+76+144,122+108+347,94+76+144,122+108+347]#['Sex']=='male'

Pclass  Sex   
1       female    0.299363
        male      0.211438
2       female    0.242038
        male      0.187175
3       female    0.458599
        male      0.601386
Name: PassengerId, dtype: float64

【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
女性更倾向与买贵一些的票，同时女性的存活率显著的比男性高，对比不同仓位中男女所占的比例也可以看出，相比男性在女性的群体中
更倾向于住好一些的客舱，这可能也就是存活率高的原因。

【思考】从任务二到任务四中，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
text.groupby('Sex').agg({'Fare':[('rename_mean','mean')],'Survived':[('rename_sum','sum')]})
Fare Survived
rename_mean rename_sum
Sex
female 44.479818 233
male 25.523893 109 
text.groupby('Pclass').agg({'Survived':['sum']}).rename(columns={'sum':'rename_sum'})
Survived
rename_sum
Pclass
1 136
2 87
3 119

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
text.groupby(['Pclass','Age'])['Fare'].mean()
# text['P_A_Fare_mean']=text.groupby(['Pclass','Age'])['Fare'].mean()

Pclass  Age  
1       0.92     151.5500
        2.00     151.5500
        4.00      81.8583
        11.00    120.0000
        14.00    120.0000
                   ...   
3       61.00      6.2375
        63.00      9.5875
        65.00      7.7500
        70.50      7.7500
        74.00      7.7750
Name: Fare, Length: 182, dtype: float64

text['P_A_Fare_mean']=text['Fare']
for name ,group in text.groupby(['Pclass','Age']):
    text.loc[group.index,'P_A_Fare_mean']= pd.Series(group['Fare'].mean(),index=group.index,name='P_A_Fare_mean')
#     display(text.loc[group.index]['P_A_Fare_mean'])
text.head()
Unnamed: 0 PassengerId Survived Pclass Name Sex Age SibSp Parch Ticket Fare Cabin Embarked P_A_Fare_mean
0 0 1 0 3 Braund, Mr. Owen Harris male 22.0 1.0 0.0 A/5 21171 7.2500 NaN S 7.988330
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 103.711800
2 2 3 1 3 Heikkinen, Miss. Laina female 26.0 0.0 0.0 STON/O2. 3101282 7.9250 NaN S 14.158036
3 3 4 1 1 Futrelle, Mrs. Jacques Heath (Lily May Peel) female 35.0 1.0 0.0 113803 53.1000 C123 S 165.744911
4 4 5 0 3 Allen, Mr. William Henry male 35.0 0.0 0.0 373450 8.0500 NaN S 9.736800

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
text.groupby('Sex').agg({'Fare':[('rename_mean','mean')],'Survived':[('rename_sum','sum')]})
Fare Survived
rename_mean rename_sum
Sex
female 44.479818 233
male 25.523893 109 
mean=text.groupby('Sex')['Fare'].mean()
sur=text.groupby('Sex')['Survived'].sum()
display(mean)
display(sur)
display(pd.merge(mean,sur,on='Sex'))
display(mean.to_frame().join(sur))
display(pd.concat([mean.to_frame(),sur.to_frame()],axis=1))

Sex
female    44.479818
male      25.523893
Name: Fare, dtype: float64



Sex
female    233
male      109
Name: Survived, dtype: int64
Fare Survived
Sex
female 44.479818 233
male 25.523893 109
Fare Survived
Sex
female 44.479818 233
male 25.523893 109
Fare Survived
Sex
female 44.479818 233
male 25.523893 109 
result =pd.merge(mean,sur,on='Sex')
result.to_csv('sex_fare_survived.csv')

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
text.groupby('Age')['Survived'].sum()#.max()

Age
0.42     1
0.67     1
0.75     2
0.83     2
0.92     1
        ..
70.00    0
70.50    0
71.00    0
74.00    0
80.00    1
Name: Survived, Length: 88, dtype: int64

# 写入代码
#存活人数的最高的年龄
text.groupby('Age')['Survived'].sum().idxmax()

24.0

# 写入代码
# 存活人数的最高的年龄的存活人数
text.groupby('Age')['Survived'].sum().loc[24.0]

15

# 写入代码
#最后计算存活人数最高的存活率（存活人数/总人数）
text.groupby('Age')['Survived'].sum().loc[24.0]/text.groupby('Age')['Survived'].count().loc[24.0]

0.5

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