Python 如何根据不同的数据帧应用最小-最大定标器

我有一个数据帧，如下所示：

import pandas as pd

df = pd.DataFrame({

'category': ['fruits','fruits','fruits','fruits','fruits','vegetables','vegetables','vegetables','vegetables','vegetables'],
'product' : ['apple','orange','durian','coconut','grape','cabbage','carrot','spinach','grass','potato'],
'sales'   : [10,20,30,40,100,10,30,50,60,100]

})

df.head(15)

当前方法：根据df中的单个类别进行规范化，手动

from sklearn import preprocessing
from sklearn.preprocessing import MinMaxScaler
scaler = MinMaxScaler()

df_fruits = df[df['category'] == "fruits"]
df_fruits['sales'] = scaler.fit_transform(df_fruits[['sales']])
df_fruits.head()
df_fruits = pd.to_csv('minmax/output/category-{}-minmax.csv'.format('XX'))

问题：
-如何相应地循环到df中的所有类别
-然后如何相应地导出包含类别名称的csv文件

---

非常感谢使用

系列。独特的
：

for i in df["category"].unique():
    cat = df[df['category'] == i]
    cat['sales'] = scaler.fit_transform(cat[['sales']])
    cat.to_csv('minmax/output/category-{}-minmax.csv'.format(i))

看起来你需要做一些功能体操才能让它起作用

您的
数据帧

import pandas as pd

df = pd.DataFrame({

'category': ['fruits','fruits','fruits','fruits','fruits','vegetables','vegetables','vegetables','vegetables','vegetables'],
'product' : ['apple','orange','durian','coconut','grape','cabbage','carrot','spinach','grass','potato'],
'sales'   : [10,20,30,40,100,10,30,50,60,100]

})

现在将其应用于分组的数据帧

df['scaled_sales'] = df.groupby('category')['sales'].transform(minmax_wrapper)

瞧

您可以使用

# I believe this should work haven't tried it out
for category, grouped in df.groupby('category'):
    grouped.to_csv(f"minmax/output/category-{category}-minmax.csv")

AttributeError：模块“pandas”没有“to_csv”属性，我做错了哪部分？对不起，一个简单的输入错误。上面已编辑。您是否尝试使用
groupby
例如
df.groupby（'category'）['sales'].agg（scaler.fit\u transform）
# I believe this should work haven't tried it out
for category, grouped in df.groupby('category'):
    grouped.to_csv(f"minmax/output/category-{category}-minmax.csv")