【数据分析入门】动手学数据分析Task3：数据重构

其他 2021-01-30 10:42:59 阅读次数: 0

Task3：数据重构

通过导入kaggle上泰坦尼克的数据集，实战数据分析全流程

项目地址: 动手学数据分析

开始之前，导入numpy、pandas包和数据

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。

# 不用print，直接显示结果
from IPython.core.interactiveshell import InteractiveShell
InteractiveShell.ast_node_interactivity = "all"

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

# 载入data文件中的:train-left-up.csv
df_left_up = pd.read_csv("data/train-left-up.csv")
df_left_up.shape
df_left_up.head()

(439, 4)
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
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_down.shape
df_left_down.head()


(452, 4)
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.shape
df_right_up.head()

(439, 8)
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.shape
df_right_down.head()

(452, 8)
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 
df_train = pd.read_csv("train.csv")
df_train.shape
df_train.head()

(891, 12)
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

【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

  • 将train.csv数据集划分为左上、左下、右上、右下四个部分

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
df_result_up = pd.concat([df_left_up, df_right_up], axis=1)
df_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 
df_result_up.to_csv("result_up.csv")

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
# 横向合并
df_result_down = pd.concat([df_left_down, df_right_down], axis = 1)
df_result_down.head()
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 
df_result_down.to_csv("result_down.csv")

# 纵向合并
df_result = pd.concat([df_result_up, df_result_down])
df_result.shape
# 注意这里的索引 因为concat关注索引对齐特性
df_result.reset_index(inplace=True,drop=True)
df_train.head()
df_result.head()

(891, 12)
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
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
```python df_result.equals(df_train) df_result.index ``` 
True

RangeIndex(start=0, stop=891, step=1)

# 测试合并后的DataFrame与导入的df_train是否相同
from pandas.testing import assert_frame_equal
assert_frame_equal(df_result, df_train)

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
df_join_up = df_left_up.join(df_right_up)
df_join_up.shape
df_join_up.head()

(439, 12)
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 
df_join_down = df_left_down.join(df_right_down)
df_join_down.shape
df_join_down.head()

 (452, 12)
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 
df_join = df_join_up.append(df_join_down)
df_join.shape
df_join.head()
df_join.reset_index(inplace=True,drop=True)
df_join.shape
df_join.head()

(891, 12)
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 
(891, 12)
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 
df_join.equals(df_result)

True

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
# 这里需要注意如果没有指明键值列 会默认选择有重复值的列 没有重复值的列会报错
# left_on right_on 使用某列键值
# left_index right_index 使用索引作为连接键。
df_merge_up = pd.merge(df_left_up, df_right_up, on=[df_left_up.index]).drop("key_0",axis=1)
df_merge_up.shape
df_merge_up.head()

(439, 12)
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 
df_merge_up = pd.merge(df_left_up, df_right_up, left_index=True, right_index=True)
df_merge_up.shape
df_merge_up.head()

(439, 12)
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 
df_merge_down = pd.merge(df_left_down, df_right_down, left_index=True, right_index=True)
df_merge_down.shape
df_merge_down.head()

(452, 12)
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 
df_merge = df_merge_up.append(df_merge_down)
df_merge.shape
df_merge.index
df_merge.reset_index(inplace=True, drop=True)
df_merge.index
df_merge.head()

(891, 12)

Int64Index([  0,   1,   2,   3,   4,   5,   6,   7,   8,   9,
            ...
            442, 443, 444, 445, 446, 447, 448, 449, 450, 451],
           dtype='int64', length=891)






RangeIndex(start=0, stop=891, step=1)
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 
df_merge.equals(df_train)

True

【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？

不可以完成任务四和任务五 merge和join的作用是横向链接pandas对象
使用append方法是纵向连接pandas对象

2.4.6 任务六：完成的数据保存为result.csv

#写入代码
df_merge.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
# 将Dataframe数据变为Series类型的数据，主要用到stack()函数。
# stack即为堆叠，该函数即为实现输入数个数组不同方式的堆叠，返回堆叠后的1个数组。
# 参数：
# arrays：用来作为堆叠的数个形状维度相等的数组
# axis：即指定依照哪个维度进行堆叠，也就是指定哪种方式进行堆叠数组，默认axis=0
# 输出：堆叠后的1个数组
df = pd.read_csv("result.csv")
df.head()
unit_result = df.stack().head()
unit_result.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 A/5 21171 7.2500 NaN S
1 1 2 1 1 Cumings, Mrs. John Bradley (Florence Briggs Th... female 38.0 1 0 PC 17599 71.2833 C85 C
2 2 3 1 3 Heikkinen, Miss. Laina female 26.0 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 113803 53.1000 C123 S
4 4 5 0 3 Allen, Mr. William Henry male 35.0 0 0 373450 8.0500 NaN S 
0  Unnamed: 0                           0
   PassengerId                          1
   Survived                             0
   Pclass                               3
   Name           Braund, Mr. Owen Harris
dtype: object

#写入代码
#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

上面关于stack函数的讲解是对于多个数组堆叠的情况，在dataframe中不存在多个数组。可将其近似看作每个index和columns对应组成一个1x1的array，然后对其进行axis=0的堆叠，每个array按顺序排放起来。也就相当于是对于dataframe中每个值进行依次排列，做了一个dataframe到Series的解雇转换

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
df_result = pd.read_csv("result.csv")
df_result.info()
df_result.head()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 891 entries, 0 to 890
Data columns (total 13 columns):
 #   Column       Non-Null Count  Dtype  
---  ------       --------------  -----  
 0   Unnamed: 0   891 non-null    int64  
 1   PassengerId  891 non-null    int64  
 2   Survived     891 non-null    int64  
 3   Pclass       891 non-null    int64  
 4   Name         891 non-null    object 
 5   Sex          891 non-null    object 
 6   Age          714 non-null    float64
 7   SibSp        891 non-null    int64  
 8   Parch        891 non-null    int64  
 9   Ticket       891 non-null    object 
 10  Fare         891 non-null    float64
 11  Cabin        204 non-null    object 
 12  Embarked     889 non-null    object 
dtypes: float64(2), int64(6), object(5)
memory usage: 90.6+ KB
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 A/5 21171 7.2500 NaN S
1 1 2 1 1 Cumings, Mrs. John Bradley (Florence Briggs Th... female 38.0 1 0 PC 17599 71.2833 C85 C
2 2 3 1 3 Heikkinen, Miss. Laina female 26.0 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 113803 53.1000 C123 S
4 4 5 0 3 Allen, Mr. William Henry male 35.0 0 0 373450 8.0500 NaN S 
df_result['Sex'] = df_result['Sex'].replace("female", 0).replace('male', 1)

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

将数据根据某个（多个）字段划分为不同的群体（group）进行分析，如电商领域将全国的总销售额根据省份进行划分，分析各省销售额的变化情况，社交领域将用户根据画像（性别、年龄）进行细分，研究用户的使用情况和偏好

任何分组(groupby)操作都涉及原始对象的以下操作之一。它们是

  • 分割对象
  • 应用一个函数
  • 结合的结果

在许多情况下，我们将数据分成多个集合，并在每个子集上应用一些函数。在应用函数中，可以执行以下操作

  • 聚合 - 计算汇总统计
  • 转换 - 执行一些特定于组的操作
  • 过滤 - 在某些情况下丢弃数据

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_fare = df_result.groupby(df_result['Sex'])['Fare'].mean()
mean_fare

Sex
0    44.479818
1    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
# 这里不能使用count 因为Survived中的值包含0(死亡)
Survived_Sex_number = df_result.groupby(df_result['Sex'])['Survived'].sum()
Survived_Sex_number

Sex
0    233
1    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_result.groupby(df_result['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

  1. 女性比男性的平均票价更高 说明女性对船舱环境要求高一点，也说明男性对客舱环境要求不高
  2. 由于较好的客舱环境女性存活的机会更大
  3. 客舱的等级高存活的机会大，客舱环境差客人警惕性高

【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？

df_result.groupby(df_result['Sex']).agg({
    
    'Fare':'mean', 'Survived':'sum'})
Fare Survived
Sex
female 44.479818 233
male 25.523893 109 
df_result.groupby(df_result['Pclass']).agg({
    
    'Survived':'sum'})
Survived
Pclass
1 136
2 87
3 119

#思考心得

df_result.groupby('Survived').agg({
    
    'Sex': 'mean', 'Pclass': 'sum'}).rename(columns=
                            {
    
    'Sex': 'mean_sex', 'Pclass': 'count_pclass'})
mean_sex count_pclass
Survived
0 0.852459 1390
1 0.318713 667

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_result.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

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_fare, Survived_Sex_number, left_index=True, right_index=True)
result = pd.merge(mean_fare, Survived_Sex_number, 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
df_survived = df_result.groupby(df_result['Age'])['Survived'].sum()
df_survived

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
df_result.groupby(df_result['Age'])['Survived'].sum().index.max()

80.0

# 写入代码
people = df_result['Survived'].sum()
people

342

# 写入代码
precetn = df_survived.max()/people
precetn

0.043859649122807015

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