DA-002【总第4期】Python数据分析：利用get_dummies对类目型的特征因子化

当我们逻辑回归建模时，输入的特征都是数据型特征，因此，我们会先对类目特征进行因子化

比如，统计人口时有“Man”和“Woman”两个属性，做数据分析时，我们需要将这种属性转化为数值形式

import pandas as pd
num_sex=['man','woman','man','man','woman','man','woman']
pd.get_dummies(num_sex,prefix='kind')

输出如下：

	kind_man	kind_woman
0	1	0
1	0	1
2	1	0
3	1	0
4	0	1
5	1	0
6	0	1

其中，get_dummies的用法为：

pd.get_dummies(data,prefix="kind", prefix_sep='_', dummy_na=False, columns=None, sparse=False, drop_first=False)
''''
data : array-like, Series, or DataFrame 
输入的数据
prefix : get_dummies转换后，列名的前缀 
columns : 指定需要实现类别转换的列名
dummy_na : 增加一列表示空缺值，如果False就忽略空缺值
drop_first : 获得k中的k-1个类别值，去除第一个
'''

同样，对于文件中的数据也可以如此操作

以泰坦尼克号幸存者预测数据集为例

f=open("train.csv")
data=pd.read_csv(f)

dummies_Cabin = pd.get_dummies(data['Cabin'], prefix= 'Cabin')#使用pandas的”get_dummies”，并拼接在原来的”data_train”之上
#data : array-like, Series, or DataFrame输入的数据,prefix:新建的列名
dummies_Embarked = pd.get_dummies(data['Embarked'], prefix= 'Embarked')
dummies_Sex = pd.get_dummies(data['Sex'], prefix= 'Sex')
dummies_Pclass = pd.get_dummies(data['Pclass'], prefix= 'Pclass')
data = pd.concat([data,dummies_Cabin, dummies_Embarked, dummies_Sex, dummies_Pclass], axis=1)#表格链接，axis=1表示列
data.drop(['Pclass', 'Name', 'Sex', 'Ticket', 'Cabin', 'Embarked'], axis=1, inplace=True)#丢弃原先的那些列

用data.head()查看数据

处理前：

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	No	S
1	2	1	1	Cumings, Mrs. John Bradley (Florence Briggs Th...	female	38.000000	1	0	PC 17599	71.2833	Yes	C
2	3	1	3	Heikkinen, Miss. Laina	female	26.000000	0	0	STON/O2. 3101282	7.9250	No	S
3	4	1	1	Futrelle, Mrs. Jacques Heath (Lily May Peel)	female	35.000000	1	0	113803	53.1000	Yes	S
4	5	0	3	Allen, Mr. William Henry	male	35.000000	0	0	373450	8.0500	No	S
5	6	0	3	Moran, Mr. James	male	23.828953	0	0	330877	8.4583	No	Q
6	7	0	1	McCarthy, Mr. Timothy J	male	54.000000	0	0	17463	51.8625	Yes	S
7	8	0	3	Palsson, Master. Gosta Leonard	male	2.000000	3	1	349909	21.0750	No	S
8	9	1	3	Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg)	female	27.000000	0	2	347742	11.1333	No	S
9	10	1	2	Nasser, Mrs. Nicholas (Adele Achem)	female	14.000000	1	0	237736	30.0708	No	C
10	11	1	3	Sandstrom, Miss. Marguerite Rut	female	4.000000	1	1	PP 9549	16.7000	Yes	S
11	12	1	1	Bonnell, Miss. Elizabeth	female	58.000000	0	0	113783	26.5500	Yes	S
12	13	0	3	Saundercock, Mr. William Henry	male	20.000000	0	0	A/5. 2151	8.0500	No	S
13	14	0	3	Andersson, Mr. Anders Johan	male	39.000000	1	5	347082	31.2750	No	S
14	15	0	3	Vestrom, Miss. Hulda Amanda Adolfina	female	14.000000	0	0	350406	7.8542	No	S
15	16	1	2	Hewlett, Mrs. (Mary D Kingcome)	female	55.000000	0	0	248706	16.0000	No	S
16	17	0	3	Rice, Master. Eugene	male	2.000000	4	1	382652	29.1250	No	Q
17	18	1	2	Williams, Mr. Charles Eugene	male	32.066493	0	0	244373	13.0000	No	S
18	19	0	3	Vander Planke, Mrs. Julius (Emelia Maria Vande...	female	31.000000	1	0	345763	18.0000	No	S
19	20	1	3	Masselmani, Mrs. Fatima	female	29.518205	0	0	2649	7.2250	No	C

处理后

PassengerId	Survived	Age	SibSp	Parch	Fare	Cabin_No	Cabin_Yes	Embarked_C	Embarked_Q	Embarked_S	Sex_female	Sex_male	Pclass_1	Pclass_2	Pclass_3
0	1	0	22.000000	1	0	7.2500	1	0	0	0	1	0	1	0	0	1
1	2	1	38.000000	1	0	71.2833	0	1	1	0	0	1	0	1	0	0
2	3	1	26.000000	0	0	7.9250	1	0	0	0	1	1	0	0	0	1
3	4	1	35.000000	1	0	53.1000	0	1	0	0	1	1	0	1	0	0
4	5	0	35.000000	0	0	8.0500	1	0	0	0	1	0	1	0	0	1
5	6	0	23.828953	0	0	8.4583	1	0	0	1	0	0	1	0	0	1
6	7	0	54.000000	0	0	51.8625	0	1	0	0	1	0	1	1	0	0
7	8	0	2.000000	3	1	21.0750	1	0	0	0	1	0	1	0	0	1
8	9	1	27.000000	0	2	11.1333	1	0	0	0	1	1	0	0	0	1
9	10	1	14.000000	1	0	30.0708	1	0	1	0	0	1	0	0	1	0
10	11	1	4.000000	1	1	16.7000	0	1	0	0	1	1	0	0	0	1
11	12	1	58.000000	0	0	26.5500	0	1	0	0	1	1	0	1	0	0
12	13	0	20.000000	0	0	8.0500	1	0	0	0	1	0	1	0	0	1
13	14	0	39.000000	1	5	31.2750	1	0	0	0	1	0	1	0	0	1
14	15	0	14.000000	0	0	7.8542	1	0	0	0	1	1	0	0	0	1
15	16	1	55.000000	0	0	16.0000	1	0	0	0	1	1	0	0	1	0
16	17	0	2.000000	4	1	29.1250	1	0	0	1	0	0	1	0	0	1
17	18	1	32.066493	0	0	13.0000	1	0	0	0	1	0	1	0	1	0
18	19	0	31.000000	1	0	18.0000	1	0	0	0	1	1	0	0	0	1
19	20	1	29.518205	0	0	7.2250	1	0	1	0	0	1	0	0