组队学习-动手学数据分析-第二章第2、3节_数据分析 .data文件-优快云博客

本文链接：https://blog.youkuaiyun.com/luoji29/article/details/126908037

本文介绍了在数据分析中数据重构的重要性，并详细讲解了如何使用Pandas的concat、join、merge方法进行数据合并。通过实际操作，包括合并不同文件、计算男女平均票价、统计存活人数等任务，深入理解数据聚合与运算。最后，强调了数据清洗对于后续分析的必要性。

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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	Ticket	Fare	Cabin	Embarked
0	male	22.0	1	A/5 21171	7.2500	NaN	S
1	female	38.0	1	PC 17599	71.2833	C85	C
2	female	26.0	0	STON/O2. 3101282	7.9250	NaN	S
3	female	35.0	1	113803	53.1000	C123	S
4	male	35.0	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                                                    1
   Survived                                                       0
   Pclass                                                         3
   Name                                     Braund, Mr. Owen Harris
   Sex                                                         male
   Age                                                         22.0
   SibSp                                                          1
   Parch                                                          0
   Ticket                                                 A/5 21171
   Fare                                                        7.25
   Embarked                                                       S
1  PassengerId                                                    2
   Survived                                                       1
   Pclass                                                         1
   Name           Cumings, Mrs. John Bradley (Florence Briggs Th...
   Sex                                                       female
   Age                                                         38.0
   SibSp                                                          1
   Parch                                                          0
   Ticket                                                  PC 17599
dtype: object

#写入代码

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	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 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     151.5500
        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
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