数据分析入门-Task02：数据清洗及特征处理

【回顾&引言】那么在这里，我们主要是做数据分析的流程性学习，主要是包括了数据清洗以及数据的特征处理，数据重构以及数据可视化。这些内容是为数据分析最后的建模和模型评价做一个铺垫。

开始之前导入numpy、pandas包和数据

#加载所需的库
import numpy as np
import pandas as pd

#加载数据train.csv
df = pd.read_csv('./train.csv')

第二章：数据清洗及特征处理

我们拿到的数据通常是不干净的，所谓的不干净，就是数据中有缺失值，有一些异常点等，需要经过一定的处理才能继续做后面的分析或建模，所以拿到数据的第一步是进行数据清洗，本章我们将学习缺失值、重复值、字符串和数据转换等操作，将数据清洗成可以分析或建模的样子。

2.1 缺失值观察与处理

我们拿到的数据经常会有很多缺失值，比如我们可以看到Cabin列存在NaN，那其他列还有没有缺失值，这些缺失值要怎么处理呢

2.1.1 缺失值观察

(1) 查看每个特征缺失值个数
(2) 查看Age， Cabin， Embarked列的数据

#写入代码
df.info()

RangeIndex: 891 entries, 0 to 890
Data columns (total 12 columns):
 #   Column       Non-Null Count  Dtype  
---  ------       --------------  -----  
 0   PassengerId  891 non-null    int64  
 1   Survived     891 non-null    int64  
 2   Pclass       891 non-null    int64  
 3   Name         891 non-null    object 
 4   Sex          891 non-null    object 
 5   Age          714 non-null    float64
 6   SibSp        891 non-null    int64  
 7   Parch        891 non-null    int64  
 8   Ticket       891 non-null    object 
 9   Fare         891 non-null    float64
 10  Cabin        204 non-null    object 
 11  Embarked     889 non-null    object 
dtypes: float64(2), int64(5), object(5)
memory usage: 83.7+ KB

#写入代码
df.isnull().sum()

#写入代码
df[['Age','Cabin','Embarked']].head(3)

	Age	Cabin	Embarked
0	22.0	NaN	S
1	38.0	C85	C
2	26.0	NaN	S

2.1.2 对缺失值进行处理

(1)处理缺失值一般有几种思路

(2) 尝试对Age列的数据的缺失值进行处理

(3) 尝试使用不同的方法直接对整张表的缺失值进行处理

#处理缺失值的一般思路：
#提醒：可使用的函数有--->dropna函数与fillna函数
df['Age'].fillna(0)
df['Age'].dropna()

0      22.0
1      38.0
2      26.0
3      35.0
4      35.0
       ... 
886    27.0
887    19.0
888     0.0
889    26.0
890    32.0
Name: Age, Length: 891, dtype: float64

df[df['Age'].isnull()].head(3)
df[np.isnan(df['Age'])].head(3)

	PassengerId	Survived	Pclass	Name	Sex	Age	SibSp	Parch	Ticket	Fare	Cabin	Embarked
5	6	0	3	Moran, Mr. James	male	NaN	0	0	330877	8.4583	NaN	Q
17	18	1	2	Williams, Mr. Charles Eugene	male	NaN	0	0	244373	13.0000	NaN	S
19	20	1	3	Masselmani, Mrs. Fatima	female	NaN	0	0	2649	7.2250	NaN	C

#写入代码
df.dropna().head(3)

	PassengerId	Survived	Pclass	Name	Sex	Age	SibSp	Parch	Ticket	Fare	Cabin	Embarked
1	2	1	1	Cumings, Mrs. John Bradley (Florence Briggs Th...	female	38.0	1	0	PC 17599	71.2833	C85	C
3	4	1	1	Futrelle, Mrs. Jacques Heath (Lily May Peel)	female	35.0	1	0	113803	53.1000	C123	S
6	7	0	1	McCarthy, Mr. Timothy J	male	54.0	0	0	17463	51.8625	E46	S

#写入代码
df.fillna(0).head(3)

	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	0	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	0	S

2.2 重复值观察与处理

由于这样那样的原因，数据中会不会存在重复值呢，如果存在要怎样处理呢

2.2.1 查看数据中的重复值

#写入代码
df[df.duplicated()]

	PassengerId	Survived	Pclass	Name	Sex	Age	SibSp	Parch	Ticket	Fare	Cabin	Embarked

2.2.2 对重复值进行处理

(1)重复值有哪些处理方式呢？

(2)处理我们数据的重复值

#重复值有哪些处理方式：
df.drop_duplicates().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

2.2.3 将前面清洗的数据保存为csv格式

#写入代码
df.to_csv('test_clear.csv')

2.3 特征观察与处理

我们对特征进行一下观察，可以把特征大概分为两大类：
数值型特征：Survived ，Pclass， Age ，SibSp， Parch， Fare，其中Survived， Pclass为离散型数值特征，Age，SibSp， Parch， Fare为连续型数值特征
文本型特征：Name， Sex， Cabin，Embarked， Ticket，其中Sex， Cabin， Embarked， Ticket为类别型文本特征，数值型特征一般可以直接用于模型的训练，但有时候为了模型的稳定性及鲁棒性会对连续变量进行离散化。文本型特征往往需要转换成数值型特征才能用于建模分析。

2.3.1 对年龄进行分箱（离散化）处理

(1) 分箱操作是什么？

(2) 将连续变量Age平均分箱成5个年龄段，并分别用类别变量12345表示

(3) 将连续变量Age划分为[0,5) [5,15) [15,30) [30,50) [50,80)五个年龄段，并分别用类别变量12345表示

(4) 将连续变量Age按10% 30% 50 70% 90%五个年龄段，并用分类变量12345表示

(5) 将上面的获得的数据分别进行保存，保存为csv格式

#分箱操作是什么：
df['AgeBand'] = pd.cut(df['Age'], 5,labels = ['1','2','3','4','5'])
df.head()

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

df['AgeBand'] = pd.cut(df['Age'],[0,5,15,30,50,80] ,labels = ['1','2','3','4','5'])
df.head()

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

df['AgeBand'] = pd.qcut(df['Age'], [0,0.1,0.3,0.5,0.7,0.9],labels = ['1','2','3','4','5'])
df.head()

	PassengerId	Survived	Pclass	Name	Sex	Age	SibSp	Parch	Ticket	Fare	Cabin	Embarked	AgeBand
0	1	0	3	Braund, Mr. Owen Harris	male	22.0	1	0	A/5 21171	7.2500	NaN	S	2
1	2	1	1	Cumings, Mrs. John Bradley (Florence Briggs Th...	female	38.0	1	0	PC 17599	71.2833	C85	C	5
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	4

【参考】https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.cut.html

【参考】https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.qcut.html

2.3.2 任务二：对文本变量进行转换

(1) 查看文本变量名及种类
(2) 将文本变量Sex， Cabin ，Embarked用数值变量12345表示
(3) 将文本变量Sex， Cabin， Embarked用one-hot编码表示

df['Sex'].value_counts()
df['Sex'].unique()
df['Sex'].nunique()

#方法一: replace
df['Sex_num'] = df['Sex'].replace(['male','female'],[1,2])
df.head()
#方法二: map
df['Sex_num'] = df['Sex'].map({'male': 1, 'female': 2})
df.head()

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

#将类别文本转换为one-hot编码

for feat in ["Age", "Embarked"]:
#     x = pd.get_dummies(df["Age"] // 6)
#     x = pd.get_dummies(pd.cut(df['Age'],5))
    x = pd.get_dummies(df[feat], prefix=feat)
    df = pd.concat([df, x], axis=1)
    #df[feat] = pd.get_dummies(df[feat], prefix=feat)
    
df.head()

2.3.3 从纯文本Name特征里提取出Titles的特征(所谓的Titles就是Mr,Miss,Mrs等)

#保存最终你完成的已经清理好的数据
df['Title'] = df.Name.str.extract('([A-Za-z]+)\.', expand=False)
df.head()

• 正则化匹配规则
• Series.str：字符串处理