import pandas as pd
data = pd.read_csv('data/mtcars.csv', index_col=0)
from sklearn.preprocessing import MinMaxScaler
minmax = MinMaxScaler()
scaled = minmax.fit_transform(data[['qsec']])
sum = len(data[scaled > 0.5])
print(sum)
or
#라이브러리
import pandas as pd
from sklearn.preprocessing import MinMaxScaler
#데이터 로드
data = pd.read_csv('/content/mtcars.csv')
data
#필요한 컬럼만 선택
df = data[['qsec']]
df
#스케일링
scaler = MinMaxScaler()
scaler.fit(df)
data['qsec_scaled'] = scaler.transform(df)
# data
#0.5넘는 데이터
a = data[:][data.qsec_scaled > 0.5]
print(len(a))
#출력
9
pd.read.csv('파일명',index_col=0)
from sklearn.preprocessing import MinMaxScaler(최소최대)
from sklearn.preprocessing import StandardCcaler(표준화)
min_max_scaler = MinMaxScaler()
X_scaled_train = min_max_scaler.fit_transform('정규화할 데이터')