（四）pandas开源课程学习笔记——数据清洗及特征处理

这里写目录标题

• • 2 第二章：数据清洗及特征处理
  • • 2.1 缺失值观察与处理
    • • 2.1.1 任务一：缺失值观察
      • 2.1.2 任务二：对缺失值进行处理
    • 2.2 重复值观察与处理
    • • 2.2.1 任务一：请查看数据中的重复值
      • 2.2.2 任务二：对重复值进行处理
      • 2.2.3 任务三：将前面清洗的数据保存为csv格式
    • 2.3 特征观察与处理
    • • 2.3.1 任务一：对年龄进行分箱（离散化）处理
      • 2.3.2 任务二：对文本变量进行转换
      • 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)

开始之前，导入numpy、pandas包和数据

import pandas as pd
import numpy as np

#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 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

df.info()

<class 'pandas.core.frame.DataFrame'>
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

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

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

(1)处理缺失值一般有几种思路

答：常见的缺失值补全方法：均值插补、同类均值插补、建模预测、高维映射、多重插补、极大似然估计、压缩感知和矩阵补全。具体查看下放链接
机器学习中处理缺失值的9种方法https://zhuanlan.zhihu.com/p/270551105

(2) 请尝试对Age列的数据的缺失值进行处理

df[df['Age'] == None] = 0
df.head(8)

	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
5	6	0	3	Moran, Mr. James	male	NaN	0	0	330877	8.4583	NaN	Q
6	7	0	1	McCarthy, Mr. Timothy J	male	54.0	0	0	17463	51.8625	E46	S
7	8	0	3	Palsson, Master. Gosta Leonard	male	2.0	3	1	349909	21.0750	NaN	S

df[df['Age'].isnull()] = 0 
df.head(8)

	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
5	0	0	0	0	0	0.0	0	0	0	0.0000	0	0
6	7	0	1	McCarthy, Mr. Timothy J	male	54.0	0	0	17463	51.8625	E46	S
7	8	0	3	Palsson, Master. Gosta Leonard	male	2.0	3	1	349909	21.0750	NaN	S

df[df['Age'] == np.nan] = 0
df.head(8)

	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
5	0	0	0	0	0	0.0	0	0	0	0.0000	0	0
6	7	0	1	McCarthy, Mr. Timothy J	male	54.0	0	0	17463	51.8625	E46	S
7	8	0	3	Palsson, Master. Gosta Leonard	male	2.0	3	1	349909	21.0750	NaN	S

【思考】检索空缺值用np.nan,None以及.isnull()哪个更好，这是为什么？如果其中某个方式无法找到缺失值，原因又是为什么？

【回答】数值列读取数据后，空缺值的数据类型为float64所以用None一般索引不到，比较的时候最好用np.nan

(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

df.dropna().head(8)

	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
5	0	0	0	0	0	0.0	0	0	0	0.0000	0	0
6	7	0	1	McCarthy, Mr. Timothy J	male	54.0	0	0	17463	51.8625	E46	S
10	11	1	3	Sandstrom, Miss. Marguerite Rut	female	4.0	1	1	PP 9549	16.7000	G6	S
11	12	1	1	Bonnell, Miss. Elizabeth	female	58.0	0	0	113783	26.5500	C103	S
17	0	0	0	0	0	0.0	0	0	0	0.0000	0	0
19	0	0	0	0	0	0.0	0	0	0	0.0000	0	0

df.fillna(0).head(8)

	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
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	0	S
5	0	0	0	0	0	0.0	0	0	0	0.0000	0	0
6	7	0	1	McCarthy, Mr. Timothy J	male	54.0	0	0	17463	51.8625	E46	S
7	8	0	3	Palsson, Master. Gosta Leonard	male	2.0	3	1	349909	21.0750	0	S

【思考】dropna和fillna有哪些参数，分别如何使用呢?

（1）df.dropna()函数用于删除dataframe数据中的缺失数据，即 删除NaN数据

DataFrame.dropna(axis=0, how=‘any’, thresh=None, subset=None, inplace=False)

parameters	说明
axis	0为行 1为列，default 0，数据删除维度
how	{‘any’, ‘all’}, default ‘any’，any：删除带有nan的行；all：删除全为nan的行
thresh	int，保留至少 int 个非nan行
subset	list，在特定列缺失值处理
inplace	bool，是否修改源文件

（2）df.fillna()函数用于填充确实数据

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

parameters	说明
value	用于填充孔的值（例如 0）
method	{‘backfill’, ‘bfill’, ‘pad’, ‘ffill’, 无}，默认无
axis	0为行 1为列，沿其填充缺失值的轴
inplace	bool，是否修改源文件
limit	整数，默认无，如果指定了方法，则这是向前/向后填充的连续 NaN 值的最大数量
downcast	dict，默认为 None

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

df[df.duplicated()]

	PassengerId	Survived	Pclass	Name	Sex	Age	SibSp	Parch	Ticket	Fare	Cabin	Embarked
17	0	0	0	0	0	0.0	0	0	0	0.0	0	0
19	0	0	0	0	0	0.0	0	0	0	0.0	0	0
26	0	0	0	0	0	0.0	0	0	0	0.0	0	0
28	0	0	0	0	0	0.0	0	0	0	0.0	0	0
29	0	0	0	0	0	0.0	0	0	0	0.0	0	0
31	0	0	0	0	0	0.0	0	0	0	0.0	0	0
32	0	0	0	0	0	0.0	0	0	0	0.0	0	0
36	0	0	0	0	0	0.0	0	0	0	0.0	0	0
42	0	0	0	0	0	0.0	0	0	0	0.0	0	0
45	0	0	0	0	0	0.0	0	0	0	0.0	0	0
46	0	0	0	0	0	0.0	0	0	0	0.0	0	0
47	0	0	0	0	0	0.0	0	0	0	0.0	0	0
48	0	0	0	0	0	0.0	0	0	0	0.0	0	0
55	0	0	0	0	0	0.0	0	0	0	0.0	0	0
64	0	0	0	0	0	0.0	0	0	0	0.0	0	0
65	0	0	0	0	0	0.0	0	0	0	0.0	0	0
76	0	0	0	0	0	0.0	0	0	0	0.0	0	0
77	0	0	0	0	0	0.0	0	0	0	0.0	0	0
82	0	0	0	0	0	0.0	0	0	0	0.0	0	0
87	0	0	0	0	0	0.0	0	0	0	0.0	0	0
95	0	0	0	0	0	0.0	0	0	0	0.0	0	0
101	0	0	0	0	0	0.0	0	0	0	0.0	0	0
107	0	0	0	0	0	0.0	0	0	0	0.0	0	0
109	0	0	0	0	0	0.0	0	0	0	0.0	0	0
121	0	0	0	0	0	0.0	0	0	0	0.0	0	0
126	0	0	0	0	0	0.0	0	0	0	0.0	0	0
128	0	0	0	0	0	0.0	0	0	0	0.0	0	0
140	0	0	0	0	0	0.0	0	0	0	0.0	0	0
154	0	0	0	0	0	0.0	0	0	0	0.0	0	0
158	0	0	0	0	0	0.0	0	0	0	0.0	0	0
...	...	...	...	...	...	...	...	...	...	...	...	...
718	0	0	0	0	0	0.0	0	0	0	0.0	0	0
727	0	0	0	0	0	0.0	0	0	0	0.0	0	0
732	0	0	0	0	0	0.0	0	0	0	0.0	0	0
738	0	0	0	0	0	0.0	0	0	0	0.0	0	0
739	0	0	0	0	0	0.0	0	0	0	0.0	0	0
740	0	0	0	0	0	0.0	0	0	0	0.0	0	0
760	0	0	0	0	0	0.0	0	0	0	0.0	0	0
766	0	0	0	0	0	0.0	0	0	0	0.0	0	0
768	0	0	0	0	0	0.0	0	0	0	0.0	0	0
773	0	0	0	0	0	0.0	0	0	0	0.0	0	0
776	0	0	0	0	0	0.0	0	0	0	0.0	0	0
778	0	0	0	0	0	0.0	0	0	0	0.0	0	0
783	0	0	0	0	0	0.0	0	0	0	0.0	0	0
790	0	0	0	0	0	0.0	0	0	0	0.0	0	0
792	0	0	0	0	0	0.0	0	0	0	0.0	0	0
793	0	0	0	0	0	0.0	0	0	0	0.0	0	0
815	0	0	0	0	0	0.0	0	0	0	0.0	0	0
825	0	0	0	0	0	0.0	0	0	0	0.0	0	0
826	0	0	0	0	0	0.0	0	0	0	0.0	0	0
828	0	0	0	0	0	0.0	0	0	0	0.0	0	0
832	0	0	0	0	0	0.0	0	0	0	0.0	0	0
837	0	0	0	0	0	0.0	0	0	0	0.0	0	0
839	0	0	0	0	0	0.0	0	0	0	0.0	0	0
846	0	0	0	0	0	0.0	0	0	0	0.0	0	0
849	0	0	0	0	0	0.0	0	0	0	0.0	0	0
859	0	0	0	0	0	0.0	0	0	0	0.0	0	0
863	0	0	0	0	0	0.0	0	0	0	0.0	0	0
868	0	0	0	0	0	0.0	0	0	0	0.0	0	0
878	0	0	0	0	0	0.0	0	0	0	0.0	0	0
888	0	0	0	0	0	0.0	0	0	0	0.0	0	0

176 rows × 12 columns

2.2.2 任务二：对重复值进行处理

df = df.drop_duplicates()
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

2.2.3 任务三：将前面清洗的数据保存为csv格式

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？

答：是指将值划分到离散区间。好比不同大小的苹果归类到几个事先布置的箱子中；不同年龄的人划分到几个年龄段中

(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

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

(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

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

	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

(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

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(3)

	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

【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

#方法一: value_counts
df['Sex'].value_counts()

male      453
female    261
0           1
Name: Sex, dtype: int64

#方法二: unique
df['Sex'].unique()

array(['male', 'female', 0], dtype=object)

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

#方法一: 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	1.0
1	2	1	1	Cumings, Mrs. John Bradley (Florence Briggs Th...	female	38.0	1	0	PC 17599	71.2833	C85	C	5	2.0
2	3	1	3	Heikkinen, Miss. Laina	female	26.0	0	0	STON/O2. 3101282	7.9250	NaN	S	3	2.0
3	4	1	1	Futrelle, Mrs. Jacques Heath (Lily May Peel)	female	35.0	1	0	113803	53.1000	C123	S	4	2.0
4	5	0	3	Allen, Mr. William Henry	male	35.0	0	0	373450	8.0500	NaN	S	4	1.0

#方法三: 使用sklearn.preprocessing的LabelEncoder
from sklearn.preprocessing import LabelEncoder
for feat in ['Cabin', 'Ticket']:
    lbl = LabelEncoder()  
    label_dict = dict(zip(df[feat].unique(), range(df[feat].nunique())))
    df[feat + "_labelEncode"] = df[feat].map(label_dict)
    df[feat + "_labelEncode"] = lbl.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	1.0	135	409
1	2	1	1	Cumings, Mrs. John Bradley (Florence Briggs Th...	female	38.0	1	0	PC 17599	71.2833	C85	C	5	2.0	74	472
2	3	1	3	Heikkinen, Miss. Laina	female	26.0	0	0	STON/O2. 3101282	7.9250	NaN	S	3	2.0	135	533
3	4	1	1	Futrelle, Mrs. Jacques Heath (Lily May Peel)	female	35.0	1	0	113803	53.1000	C123	S	4	2.0	50	41
4	5	0	3	Allen, Mr. William Henry	male	35.0	0	0	373450	8.0500	NaN	S	4	1.0	135	374

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

#将类别文本转换为one-hot编码

#方法一: OneHotEncoder
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()

	PassengerId	Survived	Pclass	Name	Sex	Age	SibSp	Parch	Ticket	Fare	...	Age_66.0	Age_70.0	Age_70.5	Age_71.0	Age_74.0	Age_80.0	Embarked_0	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 × 109 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)

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

	PassengerId	Survived	Pclass	Name	Sex	Age	SibSp	Parch	Ticket	Fare	...	Age_70.0	Age_70.5	Age_71.0	Age_74.0	Age_80.0	Embarked_0	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 × 110 columns

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

标签：...,df,Age,笔记,Mrs,开源,Mr,Miss,pandas
来源： https://blog.csdn.net/qq_46378251/article/details/122771787