【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。
#加载所需的库importnumpyasnpimportpandasaspd
#加载数据train.csvdf = pd.read_csv('train.csv')df.head(3)
PassengerIdSurvivedPclassNameSexAgeSibSpParchTicketFareCabinEmbarked
0103Braund, Mr. Owen Harrismale22.010A/5 211717.2500NaNS
1211Cumings, Mrs. John Bradley (Florence Briggs Th...female38.010PC 1759971.2833C85C
2313Heikkinen, Miss. Lainafemale26.000STON/O2. 31012827.9250NaNS
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样子。
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
(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，所以建议大家学习的时候多多益善
#方法一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
#方法二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)
AgeCabinEmbarked
022.0NaNS
138.0C85C
226.0NaNS
(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
以下是举例：
df[df['Age']==None]=0df.head(3)
PassengerIdSurvivedPclassNameSexAgeSibSpParchTicketFareCabinEmbarked
0103Braund, Mr. Owen Harrismale22.010A/5 211717.2500NaNS
1211Cumings, Mrs. John Bradley (Florence Briggs Th...female38.010PC 1759971.2833C85C
2313Heikkinen, Miss. Lainafemale26.000STON/O2. 31012827.9250NaNS
df[df['Age'].isnull()] =0# 还好df.head(3)
PassengerIdSurvivedPclassNameSexAgeSibSpParchTicketFareCabinEmbarked
0103Braund, Mr. Owen Harrismale22.010A/5 211717.2500NaNS
1211Cumings, Mrs. John Bradley (Florence Briggs Th...female38.010PC 1759971.2833C85C
2313Heikkinen, Miss. Lainafemale26.000STON/O2. 31012827.9250NaNS
df[df['Age'] == np.nan] =0df.head()
PassengerIdSurvivedPclassNameSexAgeSibSpParchTicketFareCabinEmbarked
0103Braund, Mr. Owen Harrismale22.010A/5 211717.2500NaNS
1211Cumings, Mrs. John Bradley (Florence Briggs Th...female38.010PC 1759971.2833C85C
2313Heikkinen, Miss. Lainafemale26.000STON/O2. 31012827.9250NaNS
3411Futrelle, Mrs. Jacques Heath (Lily May Peel)female35.01011380353.1000C123S
4503Allen, Mr. William Henrymale35.0003734508.0500NaNS
【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
df.dropna().head(3)
PassengerIdSurvivedPclassNameSexAgeSibSpParchTicketFareCabinEmbarked
1211Cumings, Mrs. John Bradley (Florence Briggs Th...female38.010PC 1759971.2833C85C
3411Futrelle, Mrs. Jacques Heath (Lily May Peel)female35.01011380353.1000C123S
5000000.00000.000000
df.fillna(0).head(3)
PassengerIdSurvivedPclassNameSexAgeSibSpParchTicketFareCabinEmbarked
0103Braund, Mr. Owen Harrismale22.010A/5 211717.25000S
1211Cumings, Mrs. John Bradley (Florence Briggs Th...female38.010PC 1759971.2833C85C
2313Heikkinen, Miss. Lainafemale26.000STON/O2. 31012827.92500S
【思考】dropna和fillna有哪些参数，分别如何使用呢?
【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
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
df[df.duplicated()]
PassengerIdSurvivedPclassNameSexAgeSibSpParchTicketFareCabinEmbarked
17000000.00000.000
19000000.00000.000
26000000.00000.000
28000000.00000.000
29000000.00000.000
.......................................
859000000.00000.000
863000000.00000.000
868000000.00000.000
878000000.00000.000
888000000.00000.000
176 rows × 12 columns
(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
以下是对整个行有重复值的清理的方法举例：
df = df.drop_duplicates()df.head()
PassengerIdSurvivedPclassNameSexAgeSibSpParchTicketFareCabinEmbarked
0103Braund, Mr. Owen Harrismale22.010A/5 211717.2500NaNS
1211Cumings, Mrs. John Bradley (Florence Briggs Th...female38.010PC 1759971.2833C85C
2313Heikkinen, Miss. Lainafemale26.000STON/O2. 31012827.9250NaNS
3411Futrelle, Mrs. Jacques Heath (Lily May Peel)female35.01011380353.1000C123S
4503Allen, Mr. William Henrymale35.0003734508.0500NaNS
df.to_csv('test_clear.csv')
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。
(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
#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()
PassengerIdSurvivedPclassNameSexAgeSibSpParchTicketFareCabinEmbarkedAgeBand
0103Braund, Mr. Owen Harrismale22.010A/5 211717.2500NaNS2
1211Cumings, Mrs. John Bradley (Florence Briggs Th...female38.010PC 1759971.2833C85C3
2313Heikkinen, Miss. Lainafemale26.000STON/O2. 31012827.9250NaNS2
3411Futrelle, Mrs. Jacques Heath (Lily May Peel)female35.01011380353.1000C123S3
4503Allen, Mr. William Henrymale35.0003734508.0500NaNS3
df.to_csv('test_ave.csv')
#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)
PassengerIdSurvivedPclassNameSexAgeSibSpParchTicketFareCabinEmbarkedAgeBand
0103Braund, Mr. Owen Harrismale22.010A/5 211717.2500NaNS3
1211Cumings, Mrs. John Bradley (Florence Briggs Th...female38.010PC 1759971.2833C85C4
2313Heikkinen, Miss. Lainafemale26.000STON/O2. 31012827.9250NaNS3
df.to_csv('test_cut.csv')
#将连续变量Age按10%30%5070%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()
PassengerIdSurvivedPclassNameSexAgeSibSpParchTicketFareCabinEmbarkedAgeBand
0103Braund, Mr. Owen Harrismale22.010A/5 211717.2500NaNS2
1211Cumings, Mrs. John Bradley (Florence Briggs Th...female38.010PC 1759971.2833C85C5
2313Heikkinen, Miss. Lainafemale26.000STON/O2. 31012827.9250NaNS3
3411Futrelle, Mrs. Jacques Heath (Lily May Peel)female35.01011380353.1000C123S4
4503Allen, Mr. William Henrymale35.0003734508.0500NaNS4
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
(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
方法多多益善
#查看类别文本变量名及种类#方法一: value_countsdf['Sex'].value_counts()
male 453
female 261
0 1
Name: Sex, dtype: int64
df['Cabin'].value_counts()
G6 4
C23 C25 C27 4
B96 B98 4
F33 3
C22 C26 3
..
D37 1
C92 1
E58 1
E77 1
B4 1
Name: Cabin, Length: 135, dtype: int64
df['Embarked'].value_counts()
S 554
C 130
Q 28
0 1
Name: Embarked, dtype: int64
#方法二: uniquedf['Sex'].unique()
array(['male', 'female', 0], dtype=object)
df['Sex'].nunique()
3
#将类别文本转换为12345#方法一: replacedf['Sex_num'] = df['Sex'].replace(['male','female'],[1,2])df.head()
PassengerIdSurvivedPclassNameSexAgeSibSpParchTicketFareCabinEmbarkedAgeBandSex_num
0103Braund, Mr. Owen Harrismale22.010A/5 211717.2500NaNS21
1211Cumings, Mrs. John Bradley (Florence Briggs Th...female38.010PC 1759971.2833C85C52
2313Heikkinen, Miss. Lainafemale26.000STON/O2. 31012827.9250NaNS32
3411Futrelle, Mrs. Jacques Heath (Lily May Peel)female35.01011380353.1000C123S42
4503Allen, Mr. William Henrymale35.0003734508.0500NaNS41
#方法二: mapdf['Sex_num'] = df['Sex'].map({'male':1,'female':2})df.head()
PassengerIdSurvivedPclassNameSexAgeSibSpParchTicketFareCabinEmbarkedAgeBandSex_num
0103Braund, Mr. Owen Harrismale22.010A/5 211717.2500NaNS21.0
1211Cumings, Mrs. John Bradley (Florence Briggs Th...female38.010PC 1759971.2833C85C52.0
2313Heikkinen, Miss. Lainafemale26.000STON/O2. 31012827.9250NaNS32.0
3411Futrelle, Mrs. Jacques Heath (Lily May Peel)female35.01011380353.1000C123S42.0
4503Allen, Mr. William Henrymale35.0003734508.0500NaNS41.0
#方法三: 使用sklearn.preprocessing的LabelEncoderfrom sklearn.preprocessingimport LabelEncoderforfeatin['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()
PassengerIdSurvivedPclassNameSexAgeSibSpParchTicketFareCabinEmbarkedAgeBandSex_numCabin_labelEncodeTicket_labelEncode
0103Braund, Mr. Owen Harrismale22.010A/5 211717.2500NaNS21.0135409
1211Cumings, Mrs. John Bradley (Florence Briggs Th...female38.010PC 1759971.2833C85C52.074472
2313Heikkinen, Miss. Lainafemale26.000STON/O2. 31012827.9250NaNS32.0135533
3411Futrelle, Mrs. Jacques Heath (Lily May Peel)female35.01011380353.1000C123S42.05041
4503Allen, Mr. William Henrymale35.0003734508.0500NaNS41.0135374
#将类别文本转换为one-hot编码#方法一: OneHotEncoderfor featin["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()
PassengerIdSurvivedPclassNameSexAgeSibSpParchTicketFare...Age_66.0Age_70.0Age_70.5Age_71.0Age_74.0Age_80.0Embarked_0Embarked_CEmbarked_QEmbarked_S
0103Braund, Mr. Owen Harrismale22.010A/5 211717.2500...0000000001
1211Cumings, Mrs. John Bradley (Florence Briggs Th...female38.010PC 1759971.2833...0000000100
2313Heikkinen, Miss. Lainafemale26.000STON/O2. 31012827.9250...0000000001
3411Futrelle, Mrs. Jacques Heath (Lily May Peel)female35.01011380353.1000...0000000001
4503Allen, Mr. William Henrymale35.0003734508.0500...0000000001
5 rows × 109 columns
2.3.3 任务三（附加）：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()
PassengerIdSurvivedPclassNameSexAgeSibSpParchTicketFare...Age_66.0Age_70.0Age_70.5Age_71.0Age_74.0Age_80.0Embarked_CEmbarked_QEmbarked_STitle
0103Braund, Mr. Owen Harrismale22.010A/5 211717.2500...000000001Mr
1211Cumings, Mrs. John Bradley (Florence Briggs Th...female38.010PC 1759971.2833...000000100Mrs
2313Heikkinen, Miss. Lainafemale26.000STON/O2. 31012827.9250...000000001Miss
3411Futrelle, Mrs. Jacques Heath (Lily May Peel)female35.01011380353.1000...000000001Mrs
4503Allen, Mr. William Henrymale35.0003734508.0500...000000001Mr
5 rows × 108 columns
