Chapter 15

1 第一章：pandas基础

NotebookPython 340 cells

复习：数据分析的第一步，加载数据我们已经学习完毕了。当数据展现在我们面前的时候，我们所要做的第一步就是认识他，今天我们要学习的就是了解字段含义以及初步观察数据。

1 第一章：pandas基础

1.4 知道你的数据叫什么

我们学习pandas的基础操作，那么上一节通过pandas加载之后的数据，其数据类型是什么呢？

1.4.1 任务一：pandas中有两个数据类型DateFrame和Series，通过查找简单了解他们。然后自己写一个关于这两个数据类型的小例子🌰[开放题]

In [2]python · cell 5

python

import numpy as np
import pandas as pd

In [3]python · cell 6

python

sdata = {'Ohio': 35000, 'Texas': 71000, 'Oregon': 16000, 'Utah': 5000}
example_1 = pd.Series(sdata)
example_1

Output

Ohio      35000
Texas     71000
Oregon    16000
Utah       5000
dtype: int64

In [4]python · cell 7

python

data = {'state': ['Ohio', 'Ohio', 'Ohio', 'Nevada', 'Nevada', 'Nevada'],
        'year': [2000, 2001, 2002, 2001, 2002, 2003],'pop': [1.5, 1.7, 3.6, 2.4, 2.9, 3.2]}
example_2 = pd.DataFrame(data)
example_2

Output

    state  year  pop
0    Ohio  2000  1.5
1    Ohio  2001  1.7
2    Ohio  2002  3.6
3  Nevada  2001  2.4
4  Nevada  2002  2.9
5  Nevada  2003  3.2

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	state	year	pop
0	Ohio	2000	1.5
1	Ohio	2001	1.7
2	Ohio	2002	3.6
3	Nevada	2001	2.4
4	Nevada	2002	2.9
5	Nevada	2003	3.2

1.4.2 任务二：根据上节课的方法载入"train.csv"文件

In [5]python · cell 9

python

df = pd.read_csv('/Users/chenandong/Documents/datawhale数据分析每个人题目设计/titanic/train.csv')
df.head(3)

Output

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

                                                Name     Sex   Age  SibSp  \
0                            Braund, Mr. Owen Harris    male  22.0      1   
1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   
2                             Heikkinen, Miss. Laina  female  26.0      0   

   Parch            Ticket     Fare Cabin Embarked  
0      0         A/5 21171   7.2500   NaN        S  
1      0          PC 17599  71.2833   C85        C  
2      0  STON/O2. 3101282   7.9250   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	A/5 21171	7.2500	NaN	S
1	2	1	1	Cumings, Mrs. John Bradley (Florence Briggs Th...	female	38.0	1	PC 17599	71.2833	C85	C
2	3	1	3	Heikkinen, Miss. Laina	female	26.0	0	STON/O2. 3101282	7.9250	NaN	S

也可以加载上一节课保存的"train_chinese.csv"文件。

1.4.3 任务三：查看DataFrame数据的每列的名称

In [5]python · cell 11

python

df.columns

Output

Index(['PassengerId', 'Survived', 'Pclass', 'Name', 'Sex', 'Age', 'SibSp',
       'Parch', 'Ticket', 'Fare', 'Cabin', 'Embarked'],
      dtype='object')

1.4.4任务四：查看"Cabin"这列的所有值 [有多种方法]

In [6]python · cell 13

python

df['Cabin'].head(3)

Output

0     NaN
1     C85
2     NaN
3    C123
4     NaN
Name: Cabin, dtype: object

In [7]python · cell 14

python

df.Cabin.head(3)

Output

0     NaN
1     C85
2     NaN
3    C123
4     NaN
Name: Cabin, dtype: object

1.4.5 任务五：加载文件"test_1.csv"，然后对比"train.csv"，看看有哪些多出的列，然后将多出的列删除

经过我们的观察发现一个测试集test_1.csv有一列是多余的，我们需要将这个多余的列删去

In [10]python · cell 16

python

test_1 = pd.read_csv('test_1.csv')
test_1.head(3)

Output

   Unnamed: 0  PassengerId  Survived  Pclass  \
0           0            1         0       3   
1           1            2         1       1   
2           2            3         1       3   

                                                Name     Sex   Age  SibSp  \
0                            Braund, Mr. Owen Harris    male  22.0      1   
1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   
2                             Heikkinen, Miss. Laina  female  26.0      0   

   Parch            Ticket     Fare Cabin Embarked    a  
0      0         A/5 21171   7.2500   NaN        S  100  
1      0          PC 17599  71.2833   C85        C  100  
2      0  STON/O2. 3101282   7.9250   NaN        S  100

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	Unnamed: 0	PassengerId	Survived	Pclass	Name	Sex	Age	SibSp	Ticket	Fare	Cabin	Embarked	a
0	0	1	0	3	Braund, Mr. Owen Harris	male	22.0	1	A/5 21171	7.2500	NaN	S	100
1	1	2	1	1	Cumings, Mrs. John Bradley (Florence Briggs Th...	female	38.0	1	PC 17599	71.2833	C85	C	100
2	2	3	1	3	Heikkinen, Miss. Laina	female	26.0	0	STON/O2. 3101282	7.9250	NaN	S	100

In [11]python · cell 17

python

# 删除多余的列
del test_1['a']
test_1.head(3)

Output

   Unnamed: 0  PassengerId  Survived  Pclass  \
0           0            1         0       3   
1           1            2         1       1   
2           2            3         1       3   

                                                Name     Sex   Age  SibSp  \
0                            Braund, Mr. Owen Harris    male  22.0      1   
1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   
2                             Heikkinen, Miss. Laina  female  26.0      0   

   Parch            Ticket     Fare Cabin Embarked  
0      0         A/5 21171   7.2500   NaN        S  
1      0          PC 17599  71.2833   C85        C  
2      0  STON/O2. 3101282   7.9250   NaN        S

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	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	A/5 21171	7.2500	NaN	S
1	1	2	1	1	Cumings, Mrs. John Bradley (Florence Briggs Th...	female	38.0	1	PC 17599	71.2833	C85	C
2	2	3	1	3	Heikkinen, Miss. Laina	female	26.0	0	STON/O2. 3101282	7.9250	NaN	S

【思考】还有其他的删除多余的列的方式吗？

In [12]python · cell 19

python

#思考回答

1.4.6 任务六：将['PassengerId','Name','Age','Ticket']这几个列元素隐藏，只观察其他几个列元素

In [13]python · cell 21

python

df.drop(['PassengerId','Name','Age','Ticket'],axis=1).head(3)

Output

   Survived  Pclass     Sex  SibSp  Parch     Fare Cabin Embarked
0         0       3    male      1      0   7.2500   NaN        S
1         1       1  female      1      0  71.2833   C85        C
2         1       3  female      0      0   7.9250   NaN        S

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	Survived	Pclass	Sex	SibSp	Fare	Cabin	Embarked
0	0	3	male	1	7.2500	NaN	S
1	1	1	female	1	71.2833	C85	C
2	1	3	female	0	7.9250	NaN	S

【思考】对比任务五和任务六，是不是使用了不一样的方法(函数)，如果使用一样的函数如何完成上面的不同的要求呢？

【思考回答】

如果想要完全的删除你的数据结构，使用inplace=True，因为使用inplace就将原数据覆盖了，所以这里没有用

In [14]python · cell 24

python

# 思考回答
df.head(3)

Output

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

                                                Name     Sex   Age  SibSp  \
0                            Braund, Mr. Owen Harris    male  22.0      1   
1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   
2                             Heikkinen, Miss. Laina  female  26.0      0   

   Parch            Ticket     Fare Cabin Embarked  
0      0         A/5 21171   7.2500   NaN        S  
1      0          PC 17599  71.2833   C85        C  
2      0  STON/O2. 3101282   7.9250   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	A/5 21171	7.2500	NaN	S
1	2	1	1	Cumings, Mrs. John Bradley (Florence Briggs Th...	female	38.0	1	PC 17599	71.2833	C85	C
2	3	1	3	Heikkinen, Miss. Laina	female	26.0	0	STON/O2. 3101282	7.9250	NaN	S

1.5 筛选的逻辑

表格数据中，最重要的一个功能就是要具有可筛选的能力，选出我所需要的信息，丢弃无用的信息。

下面我们还是用实战来学习pandas这个功能。

1.5.1 任务一：我们以"Age"为筛选条件，显示年龄在10岁以下的乘客信息。

In [15]python · cell 28

python

df[df["Age"]<10].head(3)

Output

    PassengerId  Survived  Pclass                             Name     Sex  \
7             8         0       3   Palsson, Master. Gosta Leonard    male   
10           11         1       3  Sandstrom, Miss. Marguerite Rut  female   
16           17         0       3             Rice, Master. Eugene    male   

    Age  SibSp  Parch   Ticket    Fare Cabin Embarked  
7   2.0      3      1   349909  21.075   NaN        S  
10  4.0      1      1  PP 9549  16.700    G6        S  
16  2.0      4      1   382652  29.125   NaN        Q

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	PassengerId	Survived	Pclass	Name	Sex	Age	SibSp	Parch	Ticket	Fare	Cabin	Embarked
7	8	0	3	Palsson, Master. Gosta Leonard	male	2.0	3	1	349909	21.075	NaN	S
10	11	1	3	Sandstrom, Miss. Marguerite Rut	female	4.0	1	1	PP 9549	16.700	G6	S
16	17	0	3	Rice, Master. Eugene	male	2.0	4	1	382652	29.125	NaN	Q

1.5.2 任务二：以"Age"为条件，将年龄在10岁以上和50岁以下的乘客信息显示出来，并将这个数据命名为midage

In [27]python · cell 30

python

midage = df[(df["Age"]>10)& (df["Age"]<50)]
midage.head(3)

Output

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

                                                Name     Sex   Age  SibSp  \
0                            Braund, Mr. Owen Harris    male  22.0      1   
1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   
2                             Heikkinen, Miss. Laina  female  26.0      0   

   Parch            Ticket     Fare Cabin Embarked  
0      0         A/5 21171   7.2500   NaN        S  
1      0          PC 17599  71.2833   C85        C  
2      0  STON/O2. 3101282   7.9250   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	A/5 21171	7.2500	NaN	S
1	2	1	1	Cumings, Mrs. John Bradley (Florence Briggs Th...	female	38.0	1	PC 17599	71.2833	C85	C
2	3	1	3	Heikkinen, Miss. Laina	female	26.0	0	STON/O2. 3101282	7.9250	NaN	S

【提示】了解pandas的条件筛选方式以及如何使用交集和并集操作

1.5.3 任务三：将midage的数据中第100行的"Pclass"和"Sex"的数据显示出来

In [28]python · cell 33

python

midage = midage.reset_index(drop=True)
midage.head(3)

Output

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

                                                Name     Sex   Age  SibSp  \
0                            Braund, Mr. Owen Harris    male  22.0      1   
1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   
2                             Heikkinen, Miss. Laina  female  26.0      0   

   Parch            Ticket     Fare Cabin Embarked  
0      0         A/5 21171   7.2500   NaN        S  
1      0          PC 17599  71.2833   C85        C  
2      0  STON/O2. 3101282   7.9250   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	A/5 21171	7.2500	NaN	S
1	2	1	1	Cumings, Mrs. John Bradley (Florence Briggs Th...	female	38.0	1	PC 17599	71.2833	C85	C
2	3	1	3	Heikkinen, Miss. Laina	female	26.0	0	STON/O2. 3101282	7.9250	NaN	S

【思考】这个reset_index()函数的作用是什么？如果不用这个函数，下面的任务会出现什么情况？

In [31]python · cell 35

python

midage.loc[[100],['Pclass','Sex']]

Output

     Pclass   Sex
100       2  male

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	Pclass	Sex
100	2	male

1.5.4 任务四：使用loc方法将midage的数据中第100，105，108行的"Pclass"，"Name"和"Sex"的数据显示出来

In [32]python · cell 37

python

midage.loc[[100,105,108],['Pclass','Name','Sex']]

Output

     Pclass                               Name   Sex
100       2  Byles, Rev. Thomas Roussel Davids  male
105       3           Cribb, Mr. John Hatfield  male
108       3                    Calic, Mr. Jovo  male

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	Pclass	Name	Sex
100	2	Byles, Rev. Thomas Roussel Davids	male
105	3	Cribb, Mr. John Hatfield	male
108	3	Calic, Mr. Jovo	male

1.5.5 任务五：使用iloc方法将midage的数据中第100，105，108行的"Pclass"，"Name"和"Sex"的数据显示出来

In [33]python · cell 39

python

midage.iloc[[100,105,108],[2,3,4]]

Output

     Pclass                               Name   Sex
100       2  Byles, Rev. Thomas Roussel Davids  male
105       3           Cribb, Mr. John Hatfield  male
108       3                    Calic, Mr. Jovo  male

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	Pclass	Name	Sex
100	2	Byles, Rev. Thomas Roussel Davids	male
105	3	Cribb, Mr. John Hatfield	male
108	3	Calic, Mr. Jovo	male

【思考】对比iloc和loc的异同

Chapter 15

1 第一章：pandas基础

NotebookPython 340 cells