DataWhale_数据分析训练营task2（第二章part1数据清洗与特征处理）

【回顾&引言】前面一章的内容大家可以感觉到我们主要是对基础知识做一个梳理，让大家了解数据分析的一些操作，主要做了数据的各个角度的观察。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。

第二章part1：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亚子。

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')
df.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	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

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 任务一：缺失值观察

(1) 请查看每个特征缺失值个数
(2) 请查看Age， Cabin， Embarked列的数据
以上方式都有多种方式，所以大家多多益善

#写入代码
df.info()

RangeIndex: 891 entries, 0 to 890
Data columns (total 12 columns):
PassengerId    891 non-null int64
Survived       891 non-null int64
Pclass         891 non-null int64
Name           891 non-null object
Sex            891 non-null object
Age            714 non-null float64
SibSp          891 non-null int64
Parch          891 non-null int64
Ticket         891 non-null object
Fare           891 non-null float64
Cabin          204 non-null object
Embarked       889 non-null object
dtypes: float64(2), int64(5), object(5)
memory usage: 83.6+ KB

#写入代码
df.isnull().sum()

PassengerId      0
Survived         0
Pclass           0
Name             0
Sex              0
Age            177
SibSp            0
Parch            0
Ticket           0
Fare             0
Cabin          687
Embarked         2
dtype: int64

#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 任务二：对缺失值进行处理(对df做的处理只要inplace=False就不会改变df)

(1)处理缺失值一般有几种思路

(2) 请尝试对Age列的数据的缺失值进行处理

(3) 请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.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

#写入代码
df[df['Age']==None]=0
df.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	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

【思考1】dropna和fillna有哪些参数，分别如何使用呢?
DataFrame.dropna(axis=0, how=‘any’, thresh=None, subset=None, inplace=False)返回DataFrame

• axis为轴 axis=0删除有nan的行，axis=1删除有nan的列
• how代表方式 包括any和all，any方式下存在任意nan值就删除，all方式下整行/列为nan才删除
• thresh代表删除阈值 行/列超过thresh个非nan的值才不删除
• subset代表只针对某些列/行进行搜索丢失值
• inplace代表是否替换原df

DataFrame.fillna(value=None, method=None, axis=None, inplace=False, limit=None, downcast=None)

• value代表在nan值处要替换的数值
• method代表填充方式{‘backfill’, ‘bfill’, ‘pad’, ‘ffill’, None} 如ffill代表前向传播非nan值，遇到第一个非nan值后，该行/列后边的nan用该值填充
• axis代表填充沿着的轴
• limit代表指定method后，沿轴填充nan的最大数量
• downcast代表改变数据类型。
• inplace代表是否替换原df

【思考2】检索空缺值用np.nan要比用None好，这是为什么？

#思考回答

pandas的底层是numpy，很多操作支持nan但不支持None。

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

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

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 任务一：请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 任务二：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

方法多多益善

#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 任务三：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 任务一：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：
将连续特征按照数值大小拆分为n段。

#写入代码
# 平均分为5段
df['AgeBand'] = pd.cut(df['Age'], 5, labels=['1','2','3','4','5'])
df.head(5)

	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

#写入代码
df.to_csv('./test_ave.csv')

# 写入代码
# 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
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
0	1	0	3	Braund, Mr. Owen Harris	male	22.0	1	0	A/5 21171	7.2500	NaN	S	3
1	2	1	1	Cumings, Mrs. John Bradley (Florence Briggs Th...	female	38.0	1	0	PC 17599	71.2833	C85	C	4
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

df.to_csv('./test_cut.csv')

# 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
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

df.to_csv('test_pr.csv')

【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

(1) 查看文本变量名及种类

方法一：value_counts

#写入代码
df['Sex'].value_counts()

male      577
female    314
Name: Sex, dtype: int64

#写入代码
df['Cabin'].value_counts()

B96 B98            4
G6                 4
C23 C25 C27        4
D                  3
F33                3
C22 C26            3
F2                 3
E101               3
B18                2
D20                2
C68                2
C126               2
F4                 2
B49                2
B57 B59 B63 B66    2
C52                2
C123               2
E67                2
E44                2
E8                 2
E33                2
B35                2
C125               2
D26                2
D17                2
C124               2
E121               2
D33                2
C93                2
C2                 2
                  ..
B4                 1
E34                1
C49                1
A31                1
C99                1
B82 B84            1
E58                1
D10 D12            1
D30                1
E63                1
D47                1
C106               1
A16                1
T                  1
B71                1
C90                1
B69                1
D56                1
D46                1
A7                 1
D7                 1
C118               1
B80                1
B42                1
C47                1
C110               1
C82                1
A34                1
A5                 1
E36                1
Name: Cabin, Length: 147, dtype: int64

#写入代码
df['Embarked'].value_counts()

S    644
C    168
Q     77
Name: Embarked, dtype: int64

方法二：unique

df['Sex'].unique()

array(['male', 'female'], dtype=object)

df['Sex'].nunique()

2

(2) 将文本变量Sex， Cabin ，Embarked用数值变量12345表示

方法1:replace

df['Sex_num']=df['Sex'].replace(['male','female'],['1','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

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

方法三: 使用sklearn.preprocessing的LabelEncoder

from sklearn.preprocessing import LabelEncoder
for feat in ['Cabin','Ticket']:
    lb1 = LabelEncoder()
    # 建立特征与标签的映射
    label_dict = dict(zip(df[feat].unique(),range(df[feat].nunique())))
    df[feat+'_labelencode'] = df[feat].map(label_dict)
    df[feat+'_labelencode'] = lb1.fit_transform(df[feat].astype(str))
df.head()

	PassengerId	Survived	Pclass	Name	Sex	Age	SibSp	Parch	Ticket	Fare	Cabin	Embarked	AgeBand	Sex_num	Cabin_labelencode	Ticket_labelencode
0	1	0	3	Braund, Mr. Owen Harris	male	22.0	1	0	A/5 21171	7.2500	NaN	S	2	1	147	523
1	2	1	1	Cumings, Mrs. John Bradley (Florence Briggs Th...	female	38.0	1	0	PC 17599	71.2833	C85	C	5	2	81	596
2	3	1	3	Heikkinen, Miss. Laina	female	26.0	0	0	STON/O2. 3101282	7.9250	NaN	S	3	2	147	669
3	4	1	1	Futrelle, Mrs. Jacques Heath (Lily May Peel)	female	35.0	1	0	113803	53.1000	C123	S	4	2	55	49
4	5	0	3	Allen, Mr. William Henry	male	35.0	0	0	373450	8.0500	NaN	S	4	1	147	472

(3) 将文本变量Sex， Cabin， Embarked用one-hot编码表示

for feat in ['Age','Cabin','Embarked']:
    x = pd.get_dummies(df[feat],prefix=feat)
    df = pd.concat([df,x],axis=1)
df.head()

	PassengerId	Survived	Pclass	Name	Sex	Age	SibSp	Parch	Ticket	Fare	...	Cabin_F G73	Cabin_F2	Cabin_F33	Cabin_F38	Cabin_F4	Cabin_G6	Cabin_T	Embarked_C	Embarked_Q	Embarked_S
0	1	0	3	Braund, Mr. Owen Harris	male	22.0	1	0	A/5 21171	7.2500	...	0	0	0	0	0	0	0	0	0	1
1	2	1	1	Cumings, Mrs. John Bradley (Florence Briggs Th...	female	38.0	1	0	PC 17599	71.2833	...	0	0	0	0	0	0	0	1	0	0
2	3	1	3	Heikkinen, Miss. Laina	female	26.0	0	0	STON/O2. 3101282	7.9250	...	0	0	0	0	0	0	0	0	0	1
3	4	1	1	Futrelle, Mrs. Jacques Heath (Lily May Peel)	female	35.0	1	0	113803	53.1000	...	0	0	0	0	0	0	0	0	0	1
4	5	0	3	Allen, Mr. William Henry	male	35.0	0	0	373450	8.0500	...	0	0	0	0	0	0	0	0	0	1

5 rows × 254 columns

2.3.3 任务三：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)

#写入代码

df['title']=df.Name.str.extract('([A-Za-z]+)\.', expand=False)
df.head()

	PassengerId	Survived	Pclass	Name	Sex	Age	SibSp	Parch	Ticket	Fare	...	Cabin_F2	Cabin_F33	Cabin_F38	Cabin_F4	Cabin_G6	Cabin_T	Embarked_C	Embarked_Q	Embarked_S	title
0	1	0	3	Braund, Mr. Owen Harris	male	22.0	1	0	A/5 21171	7.2500	...	0	0	0	0	0	0	0	0	1	Mr
1	2	1	1	Cumings, Mrs. John Bradley (Florence Briggs Th...	female	38.0	1	0	PC 17599	71.2833	...	0	0	0	0	0	0	1	0	0	Mrs
2	3	1	3	Heikkinen, Miss. Laina	female	26.0	0	0	STON/O2. 3101282	7.9250	...	0	0	0	0	0	0	0	0	1	Miss
3	4	1	1	Futrelle, Mrs. Jacques Heath (Lily May Peel)	female	35.0	1	0	113803	53.1000	...	0	0	0	0	0	0	0	0	1	Mrs
4	5	0	3	Allen, Mr. William Henry	male	35.0	0	0	373450	8.0500	...	0	0	0	0	0	0	0	0	1	Mr

5 rows × 255 columns

#保存最终你完成的已经清理好的数据
df.to_csv('test_fin.csv')