我有两个特点rank and ratings不同日期从电子商务网站抓取的不同类别下的不同产品 ID。
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
我想正常化rank and ratings using MinMaxScaler()来自sklearn。
I tried
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。
我想对每个特定日期的每个类别执行此操作groupby.
Use GroupBy.apply http://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.core.groupby.GroupBy.apply.html:
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'))
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