（四）pandas开源课程学习笔记——数据清洗及特征处理

2023-10-07 12:12:34

这里写目录标题

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')
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