动手学数据分析 Task2 学习笔记

【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
data=pd.read_csv('train.csv')

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
data.isnull().any()

PassengerId    False
Survived       False
Pclass         False
Name           False
Sex            False
Age             True
SibSp          False
Parch          False
Ticket         False
Fare           False
Cabin           True
Embarked        True
dtype: bool

df.isnull()函数返回的是dataframe表中是否有缺失值的总体概述，df中每一个值都为False或者True

isnull.sum（）返回每一列缺失值统计个数

isnull().any() 是对列表对象迭代对象（True or False）进行一个再统计，如果某一列有一个True则这一列的结果返回True

#写入代码

data.isnull().sum(axis=1)
#统计行缺失值

0      1
1      0
2      1
3      0
4      1
      ..
886    1
887    0
888    2
889    0
890    1
Length: 891, dtype: int64

data.shape[0]-data.count()  #列空值数
data.shape[1]-data.count(axis=1) #行空值数

0      1
1      0
2      1
3      0
4      1
      ..
886    1
887    0
888    2
889    0
890    1
Length: 891, dtype: int64

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

data

	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

#处理缺失值的一般思路：默认值填充 和 删除空值行
#提醒：可使用的函数有--->dropna函数与fillna函数
meandata=data['Age'].mean()
data['Age'].fillna(meandata,inplace=True)
# age列的缺失值填入平均数

#写入代码

data.dropna(subset=['Embarked'],axis=0)

	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

889 rows × 12 columns

#写入代码

data.dropna(axis=0)

	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
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
...	...	...	...	...	...	...	...	...	...	...	...	...
871	872	1	1	Beckwith, Mrs. Richard Leonard (Sallie Monypeny)	female	47.0	1	1	11751	52.5542	D35	S
872	873	0	1	Carlsson, Mr. Frans Olof	male	33.0	0	0	695	5.0000	B51 B53 B55	S
879	880	1	1	Potter, Mrs. Thomas Jr (Lily Alexenia Wilson)	female	56.0	0	1	11767	83.1583	C50	C
887	888	1	1	Graham, Miss. Margaret Edith	female	19.0	0	0	112053	30.0000	B42	S
889	890	1	1	Behr, Mr. Karl Howell	male	26.0	0	0	111369	30.0000	C148	C

183 rows × 12 columns

#写入代码
data.fillna(value={'Age':data['Age'].mean(),'Emabarkex':'Unknow','Cabin':'Unkown'})
#根据不同列填写不同的缺失值

	PassengerId	Survived	Pclass	Name	Sex	Age	SibSp	Parch	Ticket	Fare	Cabin	Embarked
0	1	0	3	Braund, Mr. Owen Harris	male	22.000000	1	0	A/5 21171	7.2500	Unkown	S
1	2	1	1	Cumings, Mrs. John Bradley (Florence Briggs Th...	female	38.000000	1	0	PC 17599	71.2833	C85	C
2	3	1	3	Heikkinen, Miss. Laina	female	26.000000	0	0	STON/O2. 3101282	7.9250	Unkown	S
3	4	1	1	Futrelle, Mrs. Jacques Heath (Lily May Peel)	female	35.000000	1	0	113803	53.1000	C123	S
4	5	0	3	Allen, Mr. William Henry	male	35.000000	0	0	373450	8.0500	Unkown	S
...	...	...	...	...	...	...	...	...	...	...	...	...
886	887	0	2	Montvila, Rev. Juozas	male	27.000000	0	0	211536	13.0000	Unkown	S
887	888	1	1	Graham, Miss. Margaret Edith	female	19.000000	0	0	112053	30.0000	B42	S
888	889	0	3	Johnston, Miss. Catherine Helen "Carrie"	female	29.699118	1	2	W./C. 6607	23.4500	Unkown	S
889	890	1	1	Behr, Mr. Karl Howell	male	26.000000	0	0	111369	30.0000	C148	C
890	891	0	3	Dooley, Mr. Patrick	male	32.000000	0	0	370376	7.7500	Unkown	Q

891 rows × 12 columns

【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)
Parameters
axis {0 or ‘index’, 1 or ‘columns’}, default 0
Determine if rows or columns which contain missing values are removed.
0, or ‘index’ : Drop rows which contain missing values.
1, or ‘columns’ : Drop columns which contain missing value.

how {‘any’, ‘all’}, default ‘any’
Determine if row or column is removed from DataFrame, when we have at least one NA or all NA.
‘any’ : If any NA values are present, drop that row or column.
‘all’ : If all values are NA, drop that row or column.

thresh int, optional
Require that many non-NA values.

subset column label or sequence of labels, optional
Labels along other axis to consider, e.g. if you are dropping rows these would be a list of columns to include.

inplace bool, default False
If True, do operation inplace and return None.

DataFrame.fillna(value=None, method=None, axis=None, inplace=False, limit=None, downcast=None)
Fill NA/NaN values using the specified method.

Parameters
value scalar, dict, Series, or DataFrame
Value to use to fill holes (e.g. 0), alternately a dict/Series/DataFrame of values specifying which value to use for each index (for a Series) or column (for a DataFrame). Values not in the dict/Series/DataFrame will not be filled. This value cannot be a list.

method {‘backfill’, ‘bfill’, ‘pad’, ‘ffill’, None}, default None
Method to use for filling holes in reindexed Series pad / ffill: propagate last valid observation forward to next valid backfill / bfill: use next valid observation to fill gap.

axis {0 or ‘index’, 1 or ‘columns’}
Axis along which to fill missing values.

inplace bool, default False
If True, fill in-place. Note: this will modify any other views on this object (e.g., a no-copy slice for a column in a DataFrame).

limit int, default None
If method is specified, this is the maximum number of consecutive NaN values to forward/backward fill. In other words, if there is a gap with more than this number of consecutive NaNs, it will only be partially filled. If method is not specified, this is the maximum number of entries along the entire axis where NaNs will be filled. Must be greater than 0 if not None.

downcast dict, default is None
A dict of item->dtype of what to downcast if possible, or the string ‘infer’ which will try to downcast to an appropriate equal type (e.g. float64 to int64 if possible).

【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？

思考回答

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

【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

DataFrame.duplicated(subset=None, keep=‘first’)

subset：默认为None，需要标记重复的标签或标签序列
keep：默认为‘first’，如何标记重复标签

first：将除第一次出现以外的重复数据标记为True
last：将除最后一次出现以外的重复数据标记为True
False：将所有重复的项都标记为True（不管是不是第一次出现）

2.2.1 任务一：请查看数据中的重复值

#写入代码
data.duplicated(keep='first')

0      False
1      False
2      False
3      False
4      False
       ...  
886    False
887    False
888    False
889    False
890    False
Length: 891, dtype: bool

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

DataFrame.drop_duplicates(subset=None, keep=‘first’, inplace=False)
subset：与duplicated()中相同
keep：与duplicated()中相同
inplace：与pandas其他函数的inplace相同，选择是修改现有数据还是返回新的数据

#重复值有哪些处理方式：
data.drop_duplicates(keep='first')

	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

data

	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
data.to_csv('cleandata.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。

pandas.cut:

pandas.cut(x, bins, right=True, labels=None, retbins=False, precision=3, include_lowest=False)

参数：

x，类array对象，且必须为一维，待切割的原形式
bins, 整数、序列尺度、或间隔索引。如果bins是一个整数，它定义了x宽度范围内的等宽面元数量，但是在这种情况下，x的范围在每个边上被延长1%，以保证包括x的最小值或最大值。如果bin是序列，它定义了允许非均匀in宽度的bin边缘。在这种情况下没有x的范围的扩展。
right,布尔值。是否是左开右闭区间
labels,用作结果箱的标签。必须与结果箱相同长度。如果FALSE，只返回整数指标面元。
retbins,布尔值。是否返回面元
precision，整数。返回面元的小数点几位
include_lowest，布尔值。第一个区间的左端点是否包含

import numpy as np import pandas as pd

df = pd.DataFrame(data = np.random.randint(0,150,size = (100,3)), columns=['Python','Tensorflow','Keras'])

# 1、等宽分箱 pd.cut(df.Python,bins = 3)

# 指定宽度分箱 pd.cut(df.Keras,#分箱数据 bins = [0,60,90,120,150],#分箱断点 right = False,# 左闭右开 labels=['不及格','中等','良好','优秀'])# 分箱后分类

# 2、等频分箱 pd.qcut(df.Python,q = 4,# 4等分 labels=['差','中','良','优']) # 分箱后分类

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：

分箱操作就是将连续数据转换为分类对应物的过程。⽐如将连续的身⾼数据划分为：矮中⾼。

分箱操作分为等距分箱和等频分箱。

分箱操作也叫⾯元划分或者离散化。

#写入代码
data1=pd.cut(data.Age,bins=5,labels=[1,2,3,4,5])
data['Age5Cut']=pd.cut(data.Age,bins=5,labels=[1,2,3,4,5])
#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

#写入代码

data['Age5Cut2']=pd.cut(data.Age,bins=[0,5,15,30,50,80],right=False,labels=[1,2,3,4,5])
data

	PassengerId	Survived	Pclass	Name	Sex	Age	SibSp	Parch	Ticket	Fare	Cabin	Embarked	Age5Cut	Age5Cut2
0	1	0	3	Braund, Mr. Owen Harris	male	22.000000	1	0	A/5 21171	7.2500	NaN	S	2	3
1	2	1	1	Cumings, Mrs. John Bradley (Florence Briggs Th...	female	38.000000	1	0	PC 17599	71.2833	C85	C	3	4
2	3	1	3	Heikkinen, Miss. Laina	female	26.000000	0	0	STON/O2. 3101282	7.9250	NaN	S	2	3
3	4	1	1	Futrelle, Mrs. Jacques Heath (Lily May Peel)	female	35.000000	1	0	113803	53.1000	C123	S	3	4
4	5	0	3	Allen, Mr. William Henry	male	35.000000	0	0	373450	8.0500	NaN	S	3	4
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
886	887	0	2	Montvila, Rev. Juozas	male	27.000000	0	0	211536	13.0000	NaN	S	2	3
887	888	1	1	Graham, Miss. Margaret Edith	female	19.000000	0	0	112053	30.0000	B42	S	2	3
888	889	0	3	Johnston, Miss. Catherine Helen "Carrie"	female	29.699118	1	2	W./C. 6607	23.4500	NaN	S	2	3
889	890	1	1	Behr, Mr. Karl Howell	male	26.000000	0	0	111369	30.0000	C148	C	2	3
890	891	0	3	Dooley, Mr. Patrick	male	32.000000	0	0	370376	7.7500	NaN	Q	2	4

891 rows × 14 columns

#写入代码
data['AgeQcut']=pd.qcut(data.Age,q=[0,0.1,0.3,0.5,0.7,0.9,1],labels=[1,2,3,4,5,6])

data

	PassengerId	Survived	Pclass	Name	Sex	Age	SibSp	Parch	Ticket	Fare	Cabin	Embarked	Age5Cut	Age5Cut2	AgeQcut
0	1	0	3	Braund, Mr. Owen Harris	male	22.000000	1	0	A/5 21171	7.2500	NaN	S	2	3	2
1	2	1	1	Cumings, Mrs. John Bradley (Florence Briggs Th...	female	38.000000	1	0	PC 17599	71.2833	C85	C	3	4	5
2	3	1	3	Heikkinen, Miss. Laina	female	26.000000	0	0	STON/O2. 3101282	7.9250	NaN	S	2	3	3
3	4	1	1	Futrelle, Mrs. Jacques Heath (Lily May Peel)	female	35.000000	1	0	113803	53.1000	C123	S	3	4	5
4	5	0	3	Allen, Mr. William Henry	male	35.000000	0	0	373450	8.0500	NaN	S	3	4	5
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
886	887	0	2	Montvila, Rev. Juozas	male	27.000000	0	0	211536	13.0000	NaN	S	2	3	3
887	888	1	1	Graham, Miss. Margaret Edith	female	19.000000	0	0	112053	30.0000	B42	S	2	3	2
888	889	0	3	Johnston, Miss. Catherine Helen "Carrie"	female	29.699118	1	2	W./C. 6607	23.4500	NaN	S	2	3	3
889	890	1	1	Behr, Mr. Karl Howell	male	26.000000	0	0	111369	30.0000	C148	C	2	3	3
890	891	0	3	Dooley, Mr. Patrick	male	32.000000	0	0	370376	7.7500	NaN	Q	2	4	4

891 rows × 15 columns

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

1
pandas.cut(x, bins, right=True, labels=None, retbins=False, precision=3, include_lowest=False, duplicates='raise')
pandas.cut(x, bins, right=True, labels=None, retbins=False, precision=3, include_lowest=False, duplicates='raise')
参数含义
x：被切分的类数组（array-like）数据，必须是1维的（不能用DataFrame）；
bins：bins是被切割后的区间（或者叫“桶”、“箱”、“面元”），有3中形式：一个int型的标量、标量序列（数组）或者pandas.IntervalIndex 。
一个int型的标量

当bins为一个int型的标量时，代表将x平分成bins份。x的范围在每侧扩展0.1%，以包括x的最大值和最小值。

标量序列

标量序列定义了被分割后每一个bin的区间边缘，此时x没有扩展。

pandas.IntervalIndex

定义要使用的精确区间。

right：bool型参数，默认为True，表示是否包含区间右部。比如如果bins=[1,2,3]，right=True，则区间为(1,2]，(2,3]；right=False，则区间为(1,2),(2,3)。
labels：给分割后的bins打标签，比如把年龄x分割成年龄段bins后，可以给年龄段打上诸如青年、中年的标签。labels的长度必须和划分后的区间长度相等，比如bins=[1,2,3]，划分后有2个区间(1,2]，(2,3]，则labels的长度必须为2。如果指定

labels=False，则返回x中的数据在第几个bin中（从0开始）。
retbins：bool型的参数，表示是否将分割后的bins返回，当bins为一个int型的标量时比较有用，这样可以得到划分后的区间，默认为False。
precision：保留区间小数点的位数，默认为3.
include_lowest：bool型的参数，表示区间的左边是开还是闭的，默认为false，也就是不包含区间左部（闭）。
duplicates：是否允许重复区间。有两种选择：raise：不允许，drop：允许。

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

.参数：pandas.qcut(x,q,labels=None,retbins=False,precision=3,duplicates='raise')

x 要进行分组的数据，数据类型为一维数组，或Series对象

q 组数，即要将数据分成几组，后边举例说明

labels 可以理解为组标签，这里注意标签个数要和组数相等

retbins 默认为False,当为False时，返回值是Categorical类型（具有value_counts()方法），为True是返回值是元组

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

#写入代码

#写入代码

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

#保存最终你完成的已经清理好的数据