pandas 数据快速 处理成标签数据

from sklearn import preprocessing
le = preprocessing.LabelEncoder()
le.fit(df.fruit)
df['categorical_label'] = le.transform(df.fruit)

#解码回到原始数据.
le.inverse_transform(df['categorical_label'])

You can use sklearn.preprocessing

具体参考网址
https://stackoverflow.com/questions/42320834/sklearn-changing-string-class-label-to-int

pandas 快速处理onehot

import pandas as pd

df = pd.DataFrame({
          'A':['a','b','a'],
          'B':['b','a','c']
        })
df
Out[]: 
   A  B
0  a  b
1  b  a
2  a  c

# Get one hot encoding of columns B
one_hot = pd.get_dummies(df['B'])
# Drop column B as it is now encoded
df = df.drop('B',axis = 1)
# Join the encoded df
df = df.join(one_hot)
df  
Out[]: 
       A  a  b  c
    0  a  0  1  0
    1  b  1  0  0
    2  a  0  0  1

参考网址
https://stackoverflow.com/questions/37292872/how-can-i-one-hot-encode-in-python

数据转化成list

df.values.tolist()