python数据预处理之将类别数据转换为数值的方法

            
import pandas as pd
from io import StringIO

csv_data = '''A,B,C,D
1,2,3,4
5,6,,8
0,11,12,'''

df = pd.read_csv(StringIO(csv_data))
print(df)
#统计为空的数目
print(df.isnull().sum())
print(df.values)

#丢弃空的
print(df.dropna())
print('after', df)
from sklearn.preprocessing import Imputer
# axis=0 列  axis = 1 行
imr = Imputer(missing_values='NaN', strategy='mean', axis=0)
imr.fit(df) # fit 构建得到数据
imputed_data = imr.transform(df.values) #transform 将数据进行填充
print(imputed_data)

df = pd.DataFrame([['green', 'M', 10.1, 'class1'],
          ['red', 'L', 13.5, 'class2'],
          ['blue', 'XL', 15.3, 'class1']])
df.columns =['color', 'size', 'price', 'classlabel']
print(df)

size_mapping = {'XL':3, 'L':2, 'M':1}
df['size'] = df['size'].map(size_mapping)
print(df)

## 遍历Series
for idx, label in enumerate(df['classlabel']):
  print(idx, label)

#1, 利用LabelEncoder类快速编码,但此时对color并不适合,
#看起来,好像是有大小的
from sklearn.preprocessing import LabelEncoder
class_le = LabelEncoder()
color_le = LabelEncoder()
df['classlabel'] = class_le.fit_transform(df['classlabel'].values)
#df['color'] = color_le.fit_transform(df['color'].values)
print(df)

#2, 映射字典将类标转换为整数
import numpy as np
class_mapping = {label: idx for idx, label in enumerate(np.unique(df['classlabel']))}
df['classlabel'] = df['classlabel'].map(class_mapping)
print('2,', df)


#3,处理1不适用的
#利用创建一个新的虚拟特征
from sklearn.preprocessing import OneHotEncoder
pf = pd.get_dummies(df[['color']])
df = pd.concat([df, pf], axis=1)
df.drop(['color'], axis=1, inplace=True)
print(df)