python - sklearn MinMaxScaler() 与 groupby pandas

Question

我有两个特征排名和评级，针对不同类别下的不同产品ID，这些特征是在不同日期从电子商务网站上抓取的。

此处提供示例数据框:

import pandas as pd
import numpy as np
import warnings; warnings.simplefilter('ignore')
from sklearn.preprocessing import MinMaxScaler
from sklearn.preprocessing import StandardScaler
from sklearn.preprocessing import RobustScaler

df=pd.read_csv('https://raw.githubusercontent.com/amanaroratc/hello-world/master/testdf.csv')
df.head()

      category                bid         date  rank    ratings
0   Aftershave  ASCDBNYZ4JMSH42B    2021-10-01  61.0    462.0
1   Aftershave  ASCDBNYZ4JMSH42B    2021-10-02  69.0    462.0
2   Aftershave  ASCDBNYZ4JMSH42B    2021-10-05  89.0    463.0
3   Aftershave  ASCE3DZK2TD7G4DN    2021-10-01  309.0   3.0
4   Aftershave  ASCE3DZK2TD7G4DN    2021-10-02  319.0   3.0

我想使用 sklearn 中的 MinMaxScaler() 标准化 rank 和 ratings。

我试过了

cols=['rank','ratings']
features=df[cols]
scaler1=MinMaxScaler()
df_norm[['rank_norm_mm', 'ratings_norm_mm']] = scaler1.fit_transform(features)

这对整个数据集进行标准化。我想使用 groupby 对每个特定日期的每个类别执行此操作。

Answer 1

使用GroupBy.apply :

file = 'https://raw.githubusercontent.com/amanaroratc/hello-world/master/testdf.csv'
df=pd.read_csv(file)

from sklearn.preprocessing import MinMaxScaler

cols=['rank','ratings']

def f(x):
    scaler1=MinMaxScaler()
    x[['rank_norm_mm', 'ratings_norm_mm']] = scaler1.fit_transform(x[cols])
    return x

df = df.groupby(['category', 'date']).apply(f)

另一个解决方案:

file = 'https://raw.githubusercontent.com/amanaroratc/hello-world/master/testdf.csv'
df=pd.read_csv(file)

from sklearn.preprocessing import MinMaxScaler

scaler1=MinMaxScaler()
cols=['rank','ratings']

df= df.join(df.groupby(['category', 'date'])[cols]
               .apply(lambda x: pd.DataFrame(scaler1.fit_transform(x), index=x.index))
               .add_prefix('_norm_mm'))