实现功能：
Python数据分析实战-数值型特征和类别型特征归一化编码操作
实现代码：
import pandasas pd
import warnings
warnings.filterwarnings("ignore")
df = pd.read_csv("E:\数据杂坛\datasets\kidney_disease.csv")
df=pd.DataFrame(df)
pd.set_option('display.max_rows', None)
pd.set_option('display.width', None)
df.drop("id",axis=1,inplace=True)
print(df.head())
df["classification"] = df["classification"].apply(lambda x: xif x =="notckd" else "ckd")
# 数值型变量名
num_cols = [colfor colin df.columnsif df[col].dtype !="object"]
# 分类型变量名
cat_cols = [colfor colin df.columnsif df[col].dtype =="object"]
print(df.isnull().sum().sort_values(ascending =False))
# ======================缺失值处理============================
def random_value_imputate(col):
"""
函数：随机填充方法（缺失值较多的字段）"""
# 1、确定填充的数量；在取出缺失值随机选择缺失值数量的样本
random_sample = df[col].dropna().sample(df[col].isna().sum())
# 2、索引号就是原缺失值记录的索引号
random_sample.index = df[df[col].isnull()].index
# 3、通过loc函数定位填充
df.loc[df[col].isnull(), col] = random_sample
def mode_impute(col):
"""
函数：众数填充缺失值"""
# 1、确定众数
mode = df[col].mode()[0]
# 2、fillna函数填充众数
df[col] = df[col].fillna(mode)
for colin num_cols:
random_value_imputate(col)
for colin cat_cols:
if colin ['rbc','pc']:
# 随机填充
random_value_imputate('rbc')
random_value_imputate('pc')
else:
mode_impute(col)
print(df.isnull().sum().sort_values(ascending =False))
print(df.head())
# ======================特征编码============================
from sklearn.preprocessingimport MinMaxScaler
mms = MinMaxScaler()
df[num_cols] = mms.fit_transform(df[num_cols])
from sklearn.preprocessingimport LabelEncoder
led = LabelEncoder()
for colin cat_cols:
df[col] = led.fit_transform(df[col])
print(df.head())
实现效果：
