如何在python中使用分类因子变量

我正在使用此数据集，希望将

年龄

、

收入

等变量作为

因子变量

放在

R

中，如何在python中实现这一点

您可以使用参数

类别

：

    age     income  student     credit_rating   Class_buys_computer
0   youth   high    no  fair    no
1   youth   high    no  excellent   no
2   middle_aged     high    no  fair    yes
3   senior  medium  no  fair    yes
4   senior  low     yes     fair    yes
5   senior  low     yes     excellent   no
6   middle_aged     low     yes     excellent   yes
7   youth   medium  no  fair    no
8   youth   low     yes     fair    yes
9   senior  medium  yes     fair    yes
10  youth   medium  yes     excellent   yes
11  middle_aged     medium  no  excellent   yes
12  middle_aged     high    yes     fair    yes
13  senior  medium  no  excellent   no

如果需要转换所有列：

cols = ['age','income','student']

for col in cols:
    df[col] = df[col].astype('category')

print (df.dtypes)
age                    category
income                 category
student                category
credit_rating            object
Class_buys_computer      object
dtype: object

您需要循环，因为如果使用：

for col in df.columns:
    df[col] = df[col].astype('category')

print (df.dtypes)
age                    category
income                 category
student                category
credit_rating          category
Class_buys_computer    category
dtype: object

NotImplementedError:>1 ndim分类目前不受支持

按注释编辑：

如果需要有序分类，请使用另一种解决方案：

---

我需要一个python（Pandas）R中的解决方案，它内置了对factors的支持。尽管pandas有分类数据类型，但许多库要求您使用Dummie。你可能需要使用pandas的get_dummies或scikit learn的OneHotEncoder。有可能像这个年轻人一样给予吗？是的，当然，给我一点时间。

df = df.astype('category')

df['age']=pd.Categorical(df['age'],categories=["youth","middle_aged","senior"],ordered=True)

print (df.age)
0           youth
1           youth
2     middle_aged
3          senior
4          senior
5          senior
6     middle_aged
7           youth
8           youth
9          senior
10          youth
11    middle_aged
12    middle_aged
13         senior
Name: age, dtype: category
Categories (3, object): [youth < middle_aged < senior]

df = df.sort_values('age')
print (df)
            age  income student credit_rating Class_buys_computer
0         youth    high      no          fair                  no
1         youth    high      no     excellent                  no
7         youth  medium      no          fair                  no
8         youth     low     yes          fair                 yes
10        youth  medium     yes     excellent                 yes
2   middle_aged    high      no          fair                 yes
6   middle_aged     low     yes     excellent                 yes
11  middle_aged  medium      no     excellent                 yes
12  middle_aged    high     yes          fair                 yes
3        senior  medium      no          fair                 yes
4        senior     low     yes          fair                 yes
5        senior     low     yes     excellent                  no
9        senior  medium     yes          fair                 yes
13       senior  medium      no     excellent                  no