您可以使用idxmin查找每行具有最小值的列，使用min查找每行的最小值。

import pandas as pd

data = {'CODBAR':[29951,29952,29953],'DESCRI':['IPHONE 15','SAMSUNG S40','MOTOROLA G1000'],'PRICE_PROVIDER1':[1000.00,1200.00,1100.00],'PRICE_PROVIDER2':[999.00,1299.00,1199.00],'PRICE_PROVIDER3':[1199.00,1199.00,999.00]}

df = pd.DataFrame(data)

# Select only the numerical columns
df_num = df.select_dtypes(include=['float64'])

# Find the column with the minimum value for each row
min_col = df_num.idxmin(axis=1)

# Find the minimum value itself
min_val = df_num.min(axis=1)

# Create a new dataframe with the CODBAR, DESCRI, and minimum value and provider
result = pd.DataFrame({'CODBAR': df['CODBAR'], 'DESCRI': df['DESCRI'], 'MIN_VALUE': min_val, 'PROVIDER': min_col})

print(result)

• • 输出：**

CODBAR          DESCRI  MIN_VALUE         PROVIDER
0   29951       IPHONE 15      999.0  PRICE_PROVIDER2
1   29952     SAMSUNG S40     1199.0  PRICE_PROVIDER3
2   29953  MOTOROLA G1000      999.0  PRICE_PROVIDER3

你可以一次找到几个聚合体，你可以加入计数、总和、平均值、标准差等，这里是最小值和最大值。

import pandas as pd

data = {'CODBAR':[29951,29952,29953],'DESCRI':['IPHONE 15','SAMSUNG S40','MOTOROLA G1000'],'PRICE_PROVIDER1':[1000.00,1200.00,1100.00],'PRICE_PROVIDER2':[999.00,1299.00,1199.00],'PRICE_PROVIDER3':[1199.00,1199.00,999.00]}

df = pd.DataFrame(data)
df

df[['PRICE_PROVIDER1', 'PRICE_PROVIDER2', 'PRICE_PROVIDER3']].agg(['min', 'max'])