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
    • 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
    • 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。
    • 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
    • 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
    • 2.1.6 完成的数据保存为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
  • 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
    • 2.3.2 计算泰坦尼克号男性与女性的平均票价
    • 2.3.3 统计泰坦尼克号中男女的存活人数
    • 2.3.4 计算客舱不同等级的存活人数
    • 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
    • 2.3.6 将2.3.2和2.3.3的数据合并，并保存到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）

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

标签：重构,...,Mrs,result,Mr,Task03,csv,数据,2.1
来源： https://blog.csdn.net/dhdbzhsj/article/details/118858864