Chapter 02

2 第二章：数据重构

NotebookPython 330 cells

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。

In [2]python · cell 2

python

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

In [7]python · cell 3

python

# 载入data文件中的:train-left-up.csv
text = pd.read_csv('/Users/chenandong/Documents/datawhale数据分析每个人题目设计/招募阶段/第二章项目集合/data/train-left-up.csv')
text.head()

Output

   PassengerId  Survived  Pclass  \
0            1         0       3   
1            2         1       1   
2            3         1       3   
3            4         1       1   
4            5         0       3   

                                                Name  
0                            Braund, Mr. Owen Harris  
1  Cumings, Mrs. John Bradley (Florence Briggs Th...  
2                             Heikkinen, Miss. Laina  
3       Futrelle, Mrs. Jacques Heath (Lily May Peel)  
4                           Allen, Mr. William Henry

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	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，与之前的原始数据相比，观察他们的之间的关系

In [12]python · cell 7

python

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")

In [13]python · cell 8

python

text_left_up.head()

Output

   PassengerId  Survived  Pclass  \
0            1         0       3   
1            2         1       1   
2            3         1       3   
3            4         1       1   
4            5         0       3   

                                                Name  
0                            Braund, Mr. Owen Harris  
1  Cumings, Mrs. John Bradley (Florence Briggs Th...  
2                             Heikkinen, Miss. Laina  
3       Futrelle, Mrs. Jacques Heath (Lily May Peel)  
4                           Allen, Mr. William Henry

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	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

In [14]python · cell 9

python

text_left_down.head()

Output

   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

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	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

In [15]python · cell 10

python

text_right_down.head()

Output

      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

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	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

In [16]python · cell 11

python

text_right_up.head()

Output

      Sex   Age  SibSp  Parch            Ticket     Fare Cabin Embarked
0    male  22.0    1.0    0.0         A/5 21171   7.2500   NaN        S
1  female  38.0    1.0    0.0          PC 17599  71.2833   C85        C
2  female  26.0    0.0    0.0  STON/O2. 3101282   7.9250   NaN        S
3  female  35.0    1.0    0.0            113803  53.1000  C123        S
4    male  35.0    0.0    0.0            373450   8.0500   NaN        S

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	Sex	Age	SibSp	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

【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

In [17]python · cell 14

python

list_up = [text_left_up,text_right_up]
result_up = pd.concat(list_up,axis=1)
result_up.head()

Output

   PassengerId  Survived  Pclass  \
0          1.0       0.0     3.0   
1          2.0       1.0     1.0   
2          3.0       1.0     3.0   
3          4.0       1.0     1.0   
4          5.0       0.0     3.0   

                                                Name     Sex   Age  SibSp  \
0                            Braund, Mr. Owen Harris    male  22.0    1.0   
1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0    1.0   
2                             Heikkinen, Miss. Laina  female  26.0    0.0   
3       Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0    1.0   
4                           Allen, Mr. William Henry    male  35.0    0.0   

   Parch            Ticket     Fare Cabin Embarked  
0    0.0         A/5 21171   7.2500   NaN        S  
1    0.0          PC 17599  71.2833   C85        C  
2    0.0  STON/O2. 3101282   7.9250   NaN        S  
3    0.0            113803  53.1000  C123        S  
4    0.0            373450   8.0500   NaN        S

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	PassengerId	Survived	Pclass	Name	Sex	Age	SibSp	Ticket	Fare	Cabin	Embarked
0	1.0	0.0	3.0	Braund, Mr. Owen Harris	male	22.0	1.0	A/5 21171	7.2500	NaN	S
1	2.0	1.0	1.0	Cumings, Mrs. John Bradley (Florence Briggs Th...	female	38.0	1.0	PC 17599	71.2833	C85	C
2	3.0	1.0	3.0	Heikkinen, Miss. Laina	female	26.0	0.0	STON/O2. 3101282	7.9250	NaN	S
3	4.0	1.0	1.0	Futrelle, Mrs. Jacques Heath (Lily May Peel)	female	35.0	1.0	113803	53.1000	C123	S
4	5.0	0.0	3.0	Allen, Mr. William Henry	male	35.0	0.0	373450	8.0500	NaN	S

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。

In [18]python · cell 16

python

list_down=[text_left_down,text_right_down]
result_down = pd.concat(list_down,axis=1)
result = pd.concat([result_up,result_down])
result.head()

Output

   PassengerId  Survived  Pclass  \
0          1.0       0.0     3.0   
1          2.0       1.0     1.0   
2          3.0       1.0     3.0   
3          4.0       1.0     1.0   
4          5.0       0.0     3.0   

                                                Name     Sex   Age  SibSp  \
0                            Braund, Mr. Owen Harris    male  22.0    1.0   
1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0    1.0   
2                             Heikkinen, Miss. Laina  female  26.0    0.0   
3       Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0    1.0   
4                           Allen, Mr. William Henry    male  35.0    0.0   

   Parch            Ticket     Fare Cabin Embarked  
0    0.0         A/5 21171   7.2500   NaN        S  
1    0.0          PC 17599  71.2833   C85        C  
2    0.0  STON/O2. 3101282   7.9250   NaN        S  
3    0.0            113803  53.1000  C123        S  
4    0.0            373450   8.0500   NaN        S

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	PassengerId	Survived	Pclass	Name	Sex	Age	SibSp	Ticket	Fare	Cabin	Embarked
0	1.0	0.0	3.0	Braund, Mr. Owen Harris	male	22.0	1.0	A/5 21171	7.2500	NaN	S
1	2.0	1.0	1.0	Cumings, Mrs. John Bradley (Florence Briggs Th...	female	38.0	1.0	PC 17599	71.2833	C85	C
2	3.0	1.0	3.0	Heikkinen, Miss. Laina	female	26.0	0.0	STON/O2. 3101282	7.9250	NaN	S
3	4.0	1.0	1.0	Futrelle, Mrs. Jacques Heath (Lily May Peel)	female	35.0	1.0	113803	53.1000	C123	S
4	5.0	0.0	3.0	Allen, Mr. William Henry	male	35.0	0.0	373450	8.0500	NaN	S

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

In [19]python · cell 18

python

resul_up = text_left_up.join(text_right_up)
result_down = text_left_down.join(text_right_down)
result = result_up.append(result_down)
result.head()

Output

   PassengerId  Survived  Pclass  \
0          1.0       0.0     3.0   
1          2.0       1.0     1.0   
2          3.0       1.0     3.0   
3          4.0       1.0     1.0   
4          5.0       0.0     3.0   

                                                Name     Sex   Age  SibSp  \
0                            Braund, Mr. Owen Harris    male  22.0    1.0   
1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0    1.0   
2                             Heikkinen, Miss. Laina  female  26.0    0.0   
3       Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0    1.0   
4                           Allen, Mr. William Henry    male  35.0    0.0   

   Parch            Ticket     Fare Cabin Embarked  
0    0.0         A/5 21171   7.2500   NaN        S  
1    0.0          PC 17599  71.2833   C85        C  
2    0.0  STON/O2. 3101282   7.9250   NaN        S  
3    0.0            113803  53.1000  C123        S  
4    0.0            373450   8.0500   NaN        S

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	PassengerId	Survived	Pclass	Name	Sex	Age	SibSp	Ticket	Fare	Cabin	Embarked
0	1.0	0.0	3.0	Braund, Mr. Owen Harris	male	22.0	1.0	A/5 21171	7.2500	NaN	S
1	2.0	1.0	1.0	Cumings, Mrs. John Bradley (Florence Briggs Th...	female	38.0	1.0	PC 17599	71.2833	C85	C
2	3.0	1.0	3.0	Heikkinen, Miss. Laina	female	26.0	0.0	STON/O2. 3101282	7.9250	NaN	S
3	4.0	1.0	1.0	Futrelle, Mrs. Jacques Heath (Lily May Peel)	female	35.0	1.0	113803	53.1000	C123	S
4	5.0	0.0	3.0	Allen, Mr. William Henry	male	35.0	0.0	373450	8.0500	NaN	S

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

In [20]python · cell 20

python

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 = resul_up.append(result_down)
result.head()

Output

   PassengerId  Survived  Pclass  \
0            1         0       3   
1            2         1       1   
2            3         1       3   
3            4         1       1   
4            5         0       3   

                                                Name     Sex   Age  SibSp  \
0                            Braund, Mr. Owen Harris    male  22.0    1.0   
1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0    1.0   
2                             Heikkinen, Miss. Laina  female  26.0    0.0   
3       Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0    1.0   
4                           Allen, Mr. William Henry    male  35.0    0.0   

   Parch            Ticket     Fare Cabin Embarked  
0    0.0         A/5 21171   7.2500   NaN        S  
1    0.0          PC 17599  71.2833   C85        C  
2    0.0  STON/O2. 3101282   7.9250   NaN        S  
3    0.0            113803  53.1000  C123        S  
4    0.0            373450   8.0500   NaN        S

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	PassengerId	Survived	Pclass	Name	Sex	Age	SibSp	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

In [21]python · cell 23

python

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

这个stack函数是干什么的？

In [22]python · cell 27

python

# 将完整的数据加载出来
text = pd.read_csv('result.csv')
text.head()
# 代码写在这里
unit_result=text.stack().head(20)
unit_result.head()

Output

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

In [23]python · cell 28

python

#将代码保存为unit_result,csv
unit_result.to_csv('unit_result.csv')

In [24]python · cell 29

python

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

In [23]python · cell 30

python

test.head()

Output

   0   Unnamed: 0                      0.1
0  0  PassengerId                        1
1  0     Survived                        0
2  0       Pclass                        3
3  0         Name  Braund, Mr. Owen Harris
4  0          Sex                     male

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	Unnamed: 0	0.1
0	PassengerId	1
1	Survived	0
2	Pclass	3
3	Name	Braund, Mr. Owen Harris
4	Sex	male

Chapter 02

2 第二章：数据重构

NotebookPython 330 cells

In [2]python · cell 2

python

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

In [7]python · cell 3

python

# 载入data文件中的:train-left-up.csv
text = pd.read_csv('/Users/chenandong/Documents/datawhale数据分析每个人题目设计/招募阶段/第二章项目集合/data/train-left-up.csv')
text.head()

Output

   PassengerId  Survived  Pclass  \
0            1         0       3   
1            2         1       1   
2            3         1       3   
3            4         1       1   
4            5         0       3   

                                                Name  
0                            Braund, Mr. Owen Harris  
1  Cumings, Mrs. John Bradley (Florence Briggs Th...  
2                             Heikkinen, Miss. Laina  
3       Futrelle, Mrs. Jacques Heath (Lily May Peel)  
4                           Allen, Mr. William Henry

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	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，与之前的原始数据相比，观察他们的之间的关系

In [12]python · cell 7

python

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")

In [13]python · cell 8

python

text_left_up.head()

Output

   PassengerId  Survived  Pclass  \
0            1         0       3   
1            2         1       1   
2            3         1       3   
3            4         1       1   
4            5         0       3   

                                                Name  
0                            Braund, Mr. Owen Harris  
1  Cumings, Mrs. John Bradley (Florence Briggs Th...  
2                             Heikkinen, Miss. Laina  
3       Futrelle, Mrs. Jacques Heath (Lily May Peel)  
4                           Allen, Mr. William Henry

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	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

In [14]python · cell 9

python

text_left_down.head()

Output

   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

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	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

In [15]python · cell 10

python

text_right_down.head()

Output

      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

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	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

In [16]python · cell 11

python

text_right_up.head()

Output

      Sex   Age  SibSp  Parch            Ticket     Fare Cabin Embarked
0    male  22.0    1.0    0.0         A/5 21171   7.2500   NaN        S
1  female  38.0    1.0    0.0          PC 17599  71.2833   C85        C
2  female  26.0    0.0    0.0  STON/O2. 3101282   7.9250   NaN        S
3  female  35.0    1.0    0.0            113803  53.1000  C123        S
4    male  35.0    0.0    0.0            373450   8.0500   NaN        S

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	Sex	Age	SibSp	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

【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

In [17]python · cell 14

python

list_up = [text_left_up,text_right_up]
result_up = pd.concat(list_up,axis=1)
result_up.head()

Output

   PassengerId  Survived  Pclass  \
0          1.0       0.0     3.0   
1          2.0       1.0     1.0   
2          3.0       1.0     3.0   
3          4.0       1.0     1.0   
4          5.0       0.0     3.0   

                                                Name     Sex   Age  SibSp  \
0                            Braund, Mr. Owen Harris    male  22.0    1.0   
1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0    1.0   
2                             Heikkinen, Miss. Laina  female  26.0    0.0   
3       Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0    1.0   
4                           Allen, Mr. William Henry    male  35.0    0.0   

   Parch            Ticket     Fare Cabin Embarked  
0    0.0         A/5 21171   7.2500   NaN        S  
1    0.0          PC 17599  71.2833   C85        C  
2    0.0  STON/O2. 3101282   7.9250   NaN        S  
3    0.0            113803  53.1000  C123        S  
4    0.0            373450   8.0500   NaN        S

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	PassengerId	Survived	Pclass	Name	Sex	Age	SibSp	Ticket	Fare	Cabin	Embarked
0	1.0	0.0	3.0	Braund, Mr. Owen Harris	male	22.0	1.0	A/5 21171	7.2500	NaN	S
1	2.0	1.0	1.0	Cumings, Mrs. John Bradley (Florence Briggs Th...	female	38.0	1.0	PC 17599	71.2833	C85	C
2	3.0	1.0	3.0	Heikkinen, Miss. Laina	female	26.0	0.0	STON/O2. 3101282	7.9250	NaN	S
3	4.0	1.0	1.0	Futrelle, Mrs. Jacques Heath (Lily May Peel)	female	35.0	1.0	113803	53.1000	C123	S
4	5.0	0.0	3.0	Allen, Mr. William Henry	male	35.0	0.0	373450	8.0500	NaN	S

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。

In [18]python · cell 16

python

list_down=[text_left_down,text_right_down]
result_down = pd.concat(list_down,axis=1)
result = pd.concat([result_up,result_down])
result.head()

Output

   PassengerId  Survived  Pclass  \
0          1.0       0.0     3.0   
1          2.0       1.0     1.0   
2          3.0       1.0     3.0   
3          4.0       1.0     1.0   
4          5.0       0.0     3.0   

                                                Name     Sex   Age  SibSp  \
0                            Braund, Mr. Owen Harris    male  22.0    1.0   
1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0    1.0   
2                             Heikkinen, Miss. Laina  female  26.0    0.0   
3       Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0    1.0   
4                           Allen, Mr. William Henry    male  35.0    0.0   

   Parch            Ticket     Fare Cabin Embarked  
0    0.0         A/5 21171   7.2500   NaN        S  
1    0.0          PC 17599  71.2833   C85        C  
2    0.0  STON/O2. 3101282   7.9250   NaN        S  
3    0.0            113803  53.1000  C123        S  
4    0.0            373450   8.0500   NaN        S

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	PassengerId	Survived	Pclass	Name	Sex	Age	SibSp	Ticket	Fare	Cabin	Embarked
0	1.0	0.0	3.0	Braund, Mr. Owen Harris	male	22.0	1.0	A/5 21171	7.2500	NaN	S
1	2.0	1.0	1.0	Cumings, Mrs. John Bradley (Florence Briggs Th...	female	38.0	1.0	PC 17599	71.2833	C85	C
2	3.0	1.0	3.0	Heikkinen, Miss. Laina	female	26.0	0.0	STON/O2. 3101282	7.9250	NaN	S
3	4.0	1.0	1.0	Futrelle, Mrs. Jacques Heath (Lily May Peel)	female	35.0	1.0	113803	53.1000	C123	S
4	5.0	0.0	3.0	Allen, Mr. William Henry	male	35.0	0.0	373450	8.0500	NaN	S

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

In [19]python · cell 18

python

resul_up = text_left_up.join(text_right_up)
result_down = text_left_down.join(text_right_down)
result = result_up.append(result_down)
result.head()

Output

   PassengerId  Survived  Pclass  \
0          1.0       0.0     3.0   
1          2.0       1.0     1.0   
2          3.0       1.0     3.0   
3          4.0       1.0     1.0   
4          5.0       0.0     3.0   

                                                Name     Sex   Age  SibSp  \
0                            Braund, Mr. Owen Harris    male  22.0    1.0   
1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0    1.0   
2                             Heikkinen, Miss. Laina  female  26.0    0.0   
3       Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0    1.0   
4                           Allen, Mr. William Henry    male  35.0    0.0   

   Parch            Ticket     Fare Cabin Embarked  
0    0.0         A/5 21171   7.2500   NaN        S  
1    0.0          PC 17599  71.2833   C85        C  
2    0.0  STON/O2. 3101282   7.9250   NaN        S  
3    0.0            113803  53.1000  C123        S  
4    0.0            373450   8.0500   NaN        S

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	PassengerId	Survived	Pclass	Name	Sex	Age	SibSp	Ticket	Fare	Cabin	Embarked
0	1.0	0.0	3.0	Braund, Mr. Owen Harris	male	22.0	1.0	A/5 21171	7.2500	NaN	S
1	2.0	1.0	1.0	Cumings, Mrs. John Bradley (Florence Briggs Th...	female	38.0	1.0	PC 17599	71.2833	C85	C
2	3.0	1.0	3.0	Heikkinen, Miss. Laina	female	26.0	0.0	STON/O2. 3101282	7.9250	NaN	S
3	4.0	1.0	1.0	Futrelle, Mrs. Jacques Heath (Lily May Peel)	female	35.0	1.0	113803	53.1000	C123	S
4	5.0	0.0	3.0	Allen, Mr. William Henry	male	35.0	0.0	373450	8.0500	NaN	S

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

In [20]python · cell 20

python

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 = resul_up.append(result_down)
result.head()

Output

   PassengerId  Survived  Pclass  \
0            1         0       3   
1            2         1       1   
2            3         1       3   
3            4         1       1   
4            5         0       3   

                                                Name     Sex   Age  SibSp  \
0                            Braund, Mr. Owen Harris    male  22.0    1.0   
1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0    1.0   
2                             Heikkinen, Miss. Laina  female  26.0    0.0   
3       Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0    1.0   
4                           Allen, Mr. William Henry    male  35.0    0.0   

   Parch            Ticket     Fare Cabin Embarked  
0    0.0         A/5 21171   7.2500   NaN        S  
1    0.0          PC 17599  71.2833   C85        C  
2    0.0  STON/O2. 3101282   7.9250   NaN        S  
3    0.0            113803  53.1000  C123        S  
4    0.0            373450   8.0500   NaN        S

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	PassengerId	Survived	Pclass	Name	Sex	Age	SibSp	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

2.4.6 任务六：完成的数据保存为result.csv

In [21]python · cell 23

python

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

这个stack函数是干什么的？

In [22]python · cell 27

python

# 将完整的数据加载出来
text = pd.read_csv('result.csv')
text.head()
# 代码写在这里
unit_result=text.stack().head(20)
unit_result.head()

Output

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

In [23]python · cell 28

python

#将代码保存为unit_result,csv
unit_result.to_csv('unit_result.csv')

In [24]python · cell 29

python

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

In [23]python · cell 30

python

test.head()

Output

   0   Unnamed: 0                      0.1
0  0  PassengerId                        1
1  0     Survived                        0
2  0       Pclass                        3
3  0         Name  Braund, Mr. Owen Harris
4  0          Sex                     male

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	Unnamed: 0	0.1
0	PassengerId	1
1	Survived	0
2	Pclass	3
3	Name	Braund, Mr. Owen Harris
4	Sex	male