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如何在pandas中的多个列上进行分组和聚合

[英]How to group by and aggregate on multiple columns in pandas

I have following dataframe in pandas 我在pandas中有以下数据帧

 ID     Balance     ATM_drawings    Value
 1      100         50              345 
 1      150         33              233
 2      100         100             333 
 2      100         100             234

I want data in that desired format 我想要所需格式的数据

 ID     Balance_mean    Balance_sum     ATM_Drawings_mean    ATM_drawings_sum 
 1      75              250             41.5                 83 
 2      200             100             200                  100

I am using following command to do it in pandas 我正在使用以下命令在pandas中执行此操作

 df1= df[['Balance','ATM_drawings']].groupby('ID', as_index = False).agg(['mean', 'sum']).reset_index()

But, it does not give what I intended to get. 但是,它没有给出我想要的东西。

You can use a dictionary to specify aggregation functions for each series: 您可以使用字典为每个系列指定聚合函数:

d = {'Balance': ['mean', 'sum'], 'ATM_drawings': ['mean', 'sum']}
res = df.groupby('ID').agg(d)

# flatten MultiIndex columns
res.columns = ['_'.join(col) for col in res.columns.values]

print(res)

    Balance_mean  Balance_sum  ATM_drawings_mean  ATM_drawings_sum
ID                                                                
1            125          250               41.5                83
2            100          200              100.0               200

Or you can define d via dict.fromkeys : 或者您可以通过dict.fromkeys定义d

d = dict.fromkeys(('Balance', 'ATM_drawings'), ['mean', 'sum'])

Not sure how to achieve this using agg , but you could reuse the `groupby´ object to avoid having to do the operation multiple times, and then use transformations: 不知道如何使用agg实现这一点,但你可以重用`groupby'对象来避免多次执行操作,然后使用转换:

import pandas as pd

df = pd.DataFrame({
    "ID": [1, 1, 2, 2],
    "Balance": [100, 150, 100, 100],
    "ATM_drawings": [50, 33, 100, 100],
    "Value": [345, 233, 333, 234]
})

gb = df.groupby("ID")
df["Balance_mean"] = gb["Balance"].transform("mean")
df["Balance_sum"] = gb["Balance"].transform("sum")
df["ATM_drawings_mean"] = gb["ATM_drawings"].transform("mean")
df["ATM_drawings_sum"] = gb["ATM_drawings"].transform("sum")
print df

Which yields: 产量:

   ID  Balance  Balance_mean  Balance_sum  ATM_drawings  ATM_drawings_mean  ATM_drawings_sum  Value
0   1      100           125          250            50               41.5                83    345
1   1      150           125          250            33               41.5                83    233
2   2      100           100          200           100              100.0               200    333
3   2      100           100          200           100              100.0               200    234

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