第二章：第一节数据清洗及特征处理-课程

【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。

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()

	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 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亚子。

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 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.isna().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.info()

<class 'pandas.core.frame.DataFrame'>
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[df.Age.isna()]

	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
26	27	0	3	Emir, Mr. Farred Chehab	male	NaN	0	0	2631	7.2250	NaN	C
28	29	1	3	O'Dwyer, Miss. Ellen "Nellie"	female	NaN	0	0	330959	7.8792	NaN	Q
...	...	...	...	...	...	...	...	...	...	...	...	...
859	860	0	3	Razi, Mr. Raihed	male	NaN	0	0	2629	7.2292	NaN	C
863	864	0	3	Sage, Miss. Dorothy Edith "Dolly"	female	NaN	8	2	CA. 2343	69.5500	NaN	S
868	869	0	3	van Melkebeke, Mr. Philemon	male	NaN	0	0	345777	9.5000	NaN	S
878	879	0	3	Laleff, Mr. Kristo	male	NaN	0	0	349217	7.8958	NaN	S
888	889	0	3	Johnston, Miss. Catherine Helen "Carrie"	female	NaN	1	2	W./C. 6607	23.4500	NaN	S

177 rows × 12 columns

df[df.Cabin.isna()]

	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
2	3	1	3	Heikkinen, Miss. Laina	female	26.0	0	0	STON/O2. 3101282	7.9250	NaN	S
4	5	0	3	Allen, Mr. William Henry	male	35.0	0	0	373450	8.0500	NaN	S
5	6	0	3	Moran, Mr. James	male	NaN	0	0	330877	8.4583	NaN	Q
7	8	0	3	Palsson, Master. Gosta Leonard	male	2.0	3	1	349909	21.0750	NaN	S
...	...	...	...	...	...	...	...	...	...	...	...	...
884	885	0	3	Sutehall, Mr. Henry Jr	male	25.0	0	0	SOTON/OQ 392076	7.0500	NaN	S
885	886	0	3	Rice, Mrs. William (Margaret Norton)	female	39.0	0	5	382652	29.1250	NaN	Q
886	887	0	2	Montvila, Rev. Juozas	male	27.0	0	0	211536	13.0000	NaN	S
888	889	0	3	Johnston, Miss. Catherine Helen "Carrie"	female	NaN	1	2	W./C. 6607	23.4500	NaN	S
890	891	0	3	Dooley, Mr. Patrick	male	32.0	0	0	370376	7.7500	NaN	Q

687 rows × 12 columns

df[df.Embarked.isna()]

	PassengerId	Survived	Pclass	Name	Sex	Age	SibSp	Parch	Ticket	Fare	Cabin	Embarked
61	62	1	1	Icard, Miss. Amelie	female	38.0	0	0	113572	80.0	B28	NaN
829	830	1	1	Stone, Mrs. George Nelson (Martha Evelyn)	female	62.0	0	0	113572	80.0	B28	NaN

2.1.2 任务二：对缺失值进行处理

(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_drop=df.dropna(subset=['Age'])
df_drop[df_drop.Age.isna()]

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

#写入代码
df_ffill=df.copy()
df_ffill.Age.fillna(method='ffill',inplace=True)
df_ffill[df_ffill.Age.isna()]
# df.Age.fillna(method='ffill')

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

#写入代码
df_bfill=df.copy()
df_bfill.Age.fillna(method='bfill',inplace=True)
df_bfill[df_bfill.Age.isna()]

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

#写入代码
df_meanfill=df.copy()
df_meanfill.Age.mean()
df_meanfill.Age.fillna(df_meanfill.Age.mean(),inplace=True)
df_meanfill[df_meanfill.Age.isna()]

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

df_meanfill.Age.mean()

29.699117647058763

【思考1】dropna和fillna有哪些参数，分别如何使用呢?

dropna

• （a）axis参数
• （b）how参数（可以选all或者any，表示全为缺失去除和存在缺失去除）
• （c）subset参数（即在某一组列范围中搜索缺失值）

fillna

• （a）值填充与前后向填充（分别与ffill方法和bfill方法等价）method=

【思考2】检索空缺值用np.nan要比用None好，这是为什么？

扫描二维码关注公众号，回复： 11622722 查看本文章

#思考回答

• 在所有的表格读取后，无论列是存放什么类型的数据，默认的缺失值全为np.nan类型
• 但这里存在一个问题就是np.nan!=np.nan

【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

• .duplicated()可选参数keep默认为first，即首次出现设为不重复，若为last，则最后一次设为不重复，若为False，则所有重复项为True

df.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

df[df.duplicated()]

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

df.drop_duplicates()

	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
...	...	...	...	...	...	...	...	...	...	...	...	...
886	887	0	2	Montvila, Rev. Juozas	male	27.0	0	0	211536	13.0000	NaN	S
887	888	1	1	Graham, Miss. Margaret Edith	female	19.0	0	0	112053	30.0000	B42	S
888	889	0	3	Johnston, Miss. Catherine Helen "Carrie"	female	NaN	1	2	W./C. 6607	23.4500	NaN	S
889	890	1	1	Behr, Mr. Karl Howell	male	26.0	0	0	111369	30.0000	C148	C
890	891	0	3	Dooley, Mr. Patrick	male	32.0	0	0	370376	7.7500	NaN	Q

891 rows × 12 columns

df[df.duplicated('Ticket')]

	PassengerId	Survived	Pclass	Name	Sex	Age	SibSp	Parch	Ticket	Fare	Cabin	Embarked
24	25	0	3	Palsson, Miss. Torborg Danira	female	8.0	3	1	349909	21.0750	NaN	S
71	72	0	3	Goodwin, Miss. Lillian Amy	female	16.0	5	2	CA 2144	46.9000	NaN	S
88	89	1	1	Fortune, Miss. Mabel Helen	female	23.0	3	2	19950	263.0000	C23 C25 C27	S
117	118	0	2	Turpin, Mr. William John Robert	male	29.0	1	0	11668	21.0000	NaN	S
119	120	0	3	Andersson, Miss. Ellis Anna Maria	female	2.0	4	2	347082	31.2750	NaN	S
...	...	...	...	...	...	...	...	...	...	...	...	...
876	877	0	3	Gustafsson, Mr. Alfred Ossian	male	20.0	0	0	7534	9.8458	NaN	S
879	880	1	1	Potter, Mrs. Thomas Jr (Lily Alexenia Wilson)	female	56.0	0	1	11767	83.1583	C50	C
880	881	1	2	Shelley, Mrs. William (Imanita Parrish Hall)	female	25.0	0	1	230433	26.0000	NaN	S
885	886	0	3	Rice, Mrs. William (Margaret Norton)	female	39.0	0	5	382652	29.1250	NaN	Q
888	889	0	3	Johnston, Miss. Catherine Helen "Carrie"	female	NaN	1	2	W./C. 6607	23.4500	NaN	S

210 rows × 12 columns

#写入代码

for name ,group in df.groupby('Ticket'):
    print(name)
    display(group)
    break

110152

	PassengerId	Survived	Pclass	Name	Sex	Age	SibSp	Parch	Ticket	Fare	Cabin	Embarked
257	258	1	1	Cherry, Miss. Gladys	female	30.0	0	0	110152	86.5	B77	S
504	505	1	1	Maioni, Miss. Roberta	female	16.0	0	0	110152	86.5	B79	S
759	760	1	1	Rothes, the Countess. of (Lucy Noel Martha Dye...	female	33.0	0	0	110152	86.5	B77	S

可以发现有很多人是使用了同样的票上船的，如果是要求一人一票的话那么可能这部分的数据就是存在问题的，也可能是逃票了或者其他原因。

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

• .drop_duplicates(‘Class’)

#重复值有哪些处理方式：

• 删除重复值
• 单独对于重复值进行修改

#写入代码


df.drop_duplicates()

	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
...	...	...	...	...	...	...	...	...	...	...	...	...
886	887	0	2	Montvila, Rev. Juozas	male	27.0	0	0	211536	13.0000	NaN	S
887	888	1	1	Graham, Miss. Margaret Edith	female	19.0	0	0	112053	30.0000	B42	S
888	889	0	3	Johnston, Miss. Catherine Helen "Carrie"	female	NaN	1	2	W./C. 6607	23.4500	NaN	S
889	890	1	1	Behr, Mr. Karl Howell	male	26.0	0	0	111369	30.0000	C148	C
890	891	0	3	Dooley, Mr. Patrick	male	32.0	0	0	370376	7.7500	NaN	Q

891 rows × 12 columns

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。

df.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

2.3.1 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

df['Age_cut']=pd.cut(df.Age,5, right=False, labels=[1,2,3,4,5])#
df['Age_cut']

0        2
1        3
2        2
3        3
4        3
      ... 
886      2
887      2
888    NaN
889      2
890      2
Name: Age_cut, Length: 891, dtype: category
Categories (5, int64): [1 < 2 < 3 < 4 < 5]

df.to_csv('test_ave.csv')

#分箱操作是什么：
df['Age_cut']=pd.cut(df.Age,[0,5,15,30,50,80], right=False, labels=[1,2,3,4,5])
df['Age_cut']

0        3
1        4
2        3
3        4
4        4
      ... 
886      3
887      3
888    NaN
889      3
890      4
Name: Age_cut, Length: 891, dtype: category
Categories (5, int64): [1 < 2 < 3 < 4 < 5]

df.to_csv('test_ave.csv')

#写入代码
df['Age_cut']=pd.qcut(df.Age,[0,.1,.3,0.5,0.7,0.9], labels=[1,2,3,4,5])
df['Age_cut']

0        2
1        5
2        3
3        4
4        4
      ... 
886      3
887      2
888    NaN
889      3
890      4
Name: Age_cut, Length: 891, dtype: category
Categories (5, int64): [1 < 2 < 3 < 4 < 5]

#写入代码
df.to_csv('test_ave.csv')

#写入代码

【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

#写入代码
df.Sex.describe()

count      891
unique       2
top       male
freq       577
Name: Sex, dtype: object

df.Sex.value_counts()

male      577
female    314
Name: Sex, dtype: int64

df.Cabin.value_counts()

G6             4
C23 C25 C27    4
B96 B98        4
F33            3
F2             3
              ..
A5             1
B71            1
C111           1
B79            1
D48            1
Name: Cabin, Length: 147, dtype: int64

df.Embarked.value_counts()

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

df['Sex_num'] = df['Sex'].replace(['male','female'],[1,2])
df.head()

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

#写入代码
df['Sex_num'] = df['Sex'].replace(r'male',1).replace(r'female',2)
df.head()

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

#写入代码
df['Sex_num'] = df['Sex'].map({'male':1,'female':2})
df.head()

	PassengerId	Survived	Pclass	Name	Sex	Age	SibSp	Parch	Ticket	Fare	Cabin	Embarked	Age_cut	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 ['Sex','Cabin', 'Ticket']:
    lbl = LabelEncoder()  
    label_dict = dict(zip(df[feat].unique(), range(df[feat].nunique())))
    df[feat + "_labelEncode_map"] = df[feat].map(label_dict)
    df[feat + "_labelEncode_lbl"] = lbl.fit_transform(df[feat].astype(str))

df.head()

	PassengerId	Survived	Pclass	Name	Sex	Age	SibSp	Parch	Ticket	Fare	Cabin	Embarked	Age_cut	Sex_num	Sex_labelEncode_map	Sex_labelEncode_lbl	Cabin_labelEncode_map	Cabin_labelEncode_lbl	Ticket_labelEncode_map	Ticket_labelEncode_lbl
0	1	0	3	Braund, Mr. Owen Harris	male	22.0	1	0	A/5 21171	7.2500	NaN	S	2	1	0	1	0.0	147	0	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	1	0	1.0	81	1	596
2	3	1	3	Heikkinen, Miss. Laina	female	26.0	0	0	STON/O2. 3101282	7.9250	NaN	S	3	2	1	0	0.0	147	2	669
3	4	1	1	Futrelle, Mrs. Jacques Heath (Lily May Peel)	female	35.0	1	0	113803	53.1000	C123	S	4	2	1	0	2.0	55	3	49
4	5	0	3	Allen, Mr. William Henry	male	35.0	0	0	373450	8.0500	NaN	S	4	1	0	1	0.0	147	4	472

###  factorize方法
#### 该方法主要用于自然数编码，并且缺失值会被记做-1，其中sort参数表示是否排序后赋值
codes, uniques = pd.factorize(df['Sex'], sort=True)
df['Sex_factorize']=codes
codes, uniques = pd.factorize(df['Cabin'], sort=True)
df['Cabin_factorize']=codes
codes, uniques = pd.factorize(df['Embarked'], sort=True)
df['Embarked_factorize']=codes
df.head()

	PassengerId	Survived	Pclass	Name	Sex	Age	SibSp	Parch	Ticket	Fare	...	Sex_num	Sex_labelEncode_map	Sex_labelEncode_lbl	Cabin_labelEncode_map	Cabin_labelEncode_lbl	Ticket_labelEncode_map	Ticket_labelEncode_lbl	Sex_factorize	Cabin_factorize	Embarked_factorize
0	1	0	3	Braund, Mr. Owen Harris	male	22.0	1	0	A/5 21171	7.2500	...	1	0	1	0.0	147	0	523	1	-1	2
1	2	1	1	Cumings, Mrs. John Bradley (Florence Briggs Th...	female	38.0	1	0	PC 17599	71.2833	...	2	1	0	1.0	81	1	596	0	81	0
2	3	1	3	Heikkinen, Miss. Laina	female	26.0	0	0	STON/O2. 3101282	7.9250	...	2	1	0	0.0	147	2	669	0	-1	2
3	4	1	1	Futrelle, Mrs. Jacques Heath (Lily May Peel)	female	35.0	1	0	113803	53.1000	...	2	1	0	2.0	55	3	49	0	55	2
4	5	0	3	Allen, Mr. William Henry	male	35.0	0	0	373450	8.0500	...	1	0	1	0.0	147	4	472	1	-1	2

5 rows × 23 columns

#one_hot
df.join(pd.get_dummies(df[['Sex', 'Cabin','Embarked']])).head()
#可选prefix参数添加前缀，prefix_sep添加分隔符

	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 × 175 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['Titles']=df['Name'].str.extract('(["Mr","Miss","Mrs"]+)\.')
df.head()

	PassengerId	Survived	Pclass	Name	Sex	Age	SibSp	Parch	Ticket	Fare	...	Sex_labelEncode_map	Sex_labelEncode_lbl	Cabin_labelEncode_map	Cabin_labelEncode_lbl	Ticket_labelEncode_map	Ticket_labelEncode_lbl	Sex_factorize	Cabin_factorize	Embarked_factorize	Titles
0	1	0	3	Braund, Mr. Owen Harris	male	22.0	1	0	A/5 21171	7.2500	...	0	1	0.0	147	0	523	1	-1	2	Mr
1	2	1	1	Cumings, Mrs. John Bradley (Florence Briggs Th...	female	38.0	1	0	PC 17599	71.2833	...	1	0	1.0	81	1	596	0	81	0	Mrs
2	3	1	3	Heikkinen, Miss. Laina	female	26.0	0	0	STON/O2. 3101282	7.9250	...	1	0	0.0	147	2	669	0	-1	2	Miss
3	4	1	1	Futrelle, Mrs. Jacques Heath (Lily May Peel)	female	35.0	1	0	113803	53.1000	...	1	0	2.0	55	3	49	0	55	2	Mrs
4	5	0	3	Allen, Mr. William Henry	male	35.0	0	0	373450	8.0500	...	0	1	0.0	147	4	472	1	-1	2	Mr

5 rows × 24 columns

#保存最终你完成的已经清理好的数据
# 保存上面的为最终结论
df.to_csv('test_fin.csv')