Task03：数据重构

2024-02-23 12:27:34

第二章

2 数据重构

2.1 数据的合并

2.1.1 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

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

# 载入data文件中的:train-left-up.csv
df1=pd.read_csv('./data/train-left-up.csv')
df1.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 
df2=pd.read_csv('./data/train-left-down.csv')
df2.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 
df3=pd.read_csv('./data/train-right-up.csv')
df3.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 
df4=pd.read_csv('./data/train-right-down.csv')
df4.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

2.1.2 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

result_up=pd.concat([df1,df3],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

2.1.3 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。

result_down=pd.concat([df2,df4],axis=1)
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 
result=pd.concat([result_up,result_down],axis=0)
result.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

2.1.4 使用DataFrame自带的方法join方法和append：实现2.1.2和2.1.3

result_up=df1.join(df3)
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 
result_down=df2.join(df4)
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 
result=result_up.append(result_down)
result.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

2.1.5 使用Panads的merge方法和DataFrame的append方法：实现2.1.2和2.1.3

参数补充
how：指的是连接方式有inner(内连接),left(左外连接),right(右外连接),outer(全外连接);默认为inner！
left_index：使用左则DataFrame中的行索引做为连接键
right_index：使用右则DataFrame中的行索引做为连接键
suffixes：字符串值组成的元组，用于指定当左右DataFrame存在相同列名时在列名后面附加的后缀名称，默认为(’_x’,’_y’)

result_up=pd.merge(df1,df3,left_index=True,right_index=True)
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 
result_down=pd.merge(df2,df4,left_index=True,right_index=True)
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 
result=result_up.append(result_down)
result.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

【思考】对比merge、join以及concat的方法的不同以及相同。思考一下在2.1.4和2.15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2.1.4和2.15呢？

#用merge完成2.1.4
result=pd.merge(result_up,result_down,how='left')
result.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 
#用join完成2.1.4
#上面提过列名相同需要修改
result=result_up.join(result_down,rsuffix='_2')
result.head()
PassengerId Survived Pclass Name Sex Age SibSp Parch Ticket Fare ... Pclass_2 Name_2 Sex_2 Age_2 SibSp_2 Parch_2 Ticket_2 Fare_2 Cabin_2 Embarked_2
0 1 0 3 Braund, Mr. Owen Harris male 22.0 1 0 A/5 21171 7.2500 ... 2 Kvillner, Mr. Johan Henrik Johannesson male 31.0 0 0 C.A. 18723 10.500 NaN S
1 2 1 1 Cumings, Mrs. John Bradley (Florence Briggs Th... female 38.0 1 0 PC 17599 71.2833 ... 2 Hart, Mrs. Benjamin (Esther Ada Bloomfield) female 45.0 1 1 F.C.C. 13529 26.250 NaN S
2 3 1 3 Heikkinen, Miss. Laina female 26.0 0 0 STON/O2. 3101282 7.9250 ... 3 Hampe, Mr. Leon male 20.0 0 0 345769 9.500 NaN S
3 4 1 1 Futrelle, Mrs. Jacques Heath (Lily May Peel) female 35.0 1 0 113803 53.1000 ... 3 Petterson, Mr. Johan Emil male 25.0 1 0 347076 7.775 NaN S
4 5 0 3 Allen, Mr. William Henry male 35.0 0 0 373450 8.0500 ... 2 Reynaldo, Ms. Encarnacion female 28.0 0 0 230434 13.000 NaN S

5 rows × 24 columns

思考回答：
merge默认以重叠的列名为连接键，上面d1和d3是完全两个不同的表，所以在连接的时候就要指定left_index和right_index。
join当两个表中列名不同时，不加任何参数就可以直接用，有重名列时要通过参数lsuffix, rsuffix。
concat基于轴向连接，关键参数为axis。
append可以很方便连接两个相同列名的dataframe且不用加参数。

2.1.6 完成的数据保存为result.csv

result.to_csv('result.csv')

2.2 换一种角度看数据

2.2.1 将我们的数据变为Series类型的数据

df = pd.read_csv('result.csv')
df.head()
Unnamed: 0 PassengerId Survived Pclass Name Sex Age SibSp Parch Ticket ... Pclass_2 Name_2 Sex_2 Age_2 SibSp_2 Parch_2 Ticket_2 Fare_2 Cabin_2 Embarked_2
0 0 1 0 3 Braund, Mr. Owen Harris male 22.0 1 0 A/5 21171 ... 2 Kvillner, Mr. Johan Henrik Johannesson male 31.0 0 0 C.A. 18723 10.500 NaN S
1 1 2 1 1 Cumings, Mrs. John Bradley (Florence Briggs Th... female 38.0 1 0 PC 17599 ... 2 Hart, Mrs. Benjamin (Esther Ada Bloomfield) female 45.0 1 1 F.C.C. 13529 26.250 NaN S
2 2 3 1 3 Heikkinen, Miss. Laina female 26.0 0 0 STON/O2. 3101282 ... 3 Hampe, Mr. Leon male 20.0 0 0 345769 9.500 NaN S
3 3 4 1 1 Futrelle, Mrs. Jacques Heath (Lily May Peel) female 35.0 1 0 113803 ... 3 Petterson, Mr. Johan Emil male 25.0 1 0 347076 7.775 NaN S
4 4 5 0 3 Allen, Mr. William Henry male 35.0 0 0 373450 ... 2 Reynaldo, Ms. Encarnacion female 28.0 0 0 230434 13.000 NaN S

5 rows × 25 columns

out=df.stack()
out.head()

0  Unnamed: 0                           0
   PassengerId                          1
   Survived                             0
   Pclass                               3
   Name           Braund, Mr. Owen Harris
dtype: object

out.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 
# 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 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

2.3 数据聚合与运算

2.3.1 通过教材《Python for Data Analysis》P303、Google or anything来学习了解GroupBy机制

按照分组键进行分组，再按照某列进行应用，产生一个新Series

2.3.2 计算泰坦尼克号男性与女性的平均票价

df1=df['Fare'].groupby(df['Sex'])
means=df1.mean()
means

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

2.3.3 统计泰坦尼克号中男女的存活人数

# 存活的记为1，死亡记为0，存活的通过sum相加
df2=df['Survived'].groupby(df['Sex'])
sums=df2.sum()
sums

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

2.3.4 计算客舱不同等级的存活人数

df3=df['Survived'].groupby(df['Pclass'])
sums=df3.sum()
sums

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

【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 ：
女性的平均票价比男性的贵，一定概率说明女性更多的购买了高等级客舱票，而且女性的存活人数是男性的两倍，也可以看出越高等级客舱存活率越高

【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', 'Survived': 'sum'}).rename(columns=
                            {'Fare': 'mean_fare', 'Survived': 'sum_pclass'})
Sex mean_fare sum_pclass
female 44.479818 233
male 25.523893 109

2.3.5 统计在不同等级的票中的不同年龄的船票花费的平均值

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

2.3.6 将2.3.2和2.3.3的数据合并，并保存到sex_fare_survived.csv

df=pd.merge(means,sums,left_index=True,right_index=True)
df.head()
Fare Survived
Sex
female 44.479818 233
male 25.523893 109 
df.to_csv('sex_fare_survived.csv')

2.3.7 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）

a=df['Survived'].groupby(df['Age'])
b=a.sum()
b.head()

Age
0.42    1
0.67    1
0.75    2
0.83    2
0.92    1
Name: Survived, dtype: int64

b[b.values==b.max()]

Age
24.0    15
Name: Survived, dtype: int64

sums=df['Survived'].sum()
sums

342

survival_rate=b.max()/sums
survival_rate

0.043859649122807015
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