Assign group averages to each row in python/pandas

I have a dataframe and I am looking to calculate the mean based on store and all stores. I created code to calculate the mean but I am looking for a way that is more efficient.

我有一个数据框,我希望根据商店和所有商店计算平均值。我创建了计算平均值的代码,但我正在寻找一种更有效的方法。

Cashier#     Store#     Sales    Refunds
001          001        100      1
002          001        150      2
003          001        200      2
004          002        400      1
005          002        600      4

DF-Desired

Cashier#     Store#     Sales    Refunds     Sales_StoreAvg    Sales_All_Stores_Avg
001          001        100      1            150               290
002          001        150      2            150               290
003          001        200      2            150               290
004          002        400      1            500               290
005          002        600      4            500               290

My Attempt I created two additional dataframes then did a left join

我的尝试我创建了两个额外的数据帧,然后进行了左连接

df.groupby(['Store#']).sum().reset_index().groupby('Sales').mean()

2 个解决方案

#1

I think need GroupBy.transform for new column filled by aggregate values with mean:

我认为需要GroupBy.transform为由聚合值填充的新列使用mean:

df['Sales_StoreAvg'] = df.groupby('Store#')['Sales'].transform('mean')
df['Sales_All_Stores_Avg'] = df['Sales'].mean()
print (df)
   Cashier#  Store#  Sales  Refunds  Sales_StoreAvg  Sales_All_Stores_Avg
0         1       1    100        1             150                 290.0
1         2       1    150        2             150                 290.0
2         3       1    200        2             150                 290.0
3         4       2    400        1             500                 290.0
4         5       2    600        4             500                 290.0

#2

Use this, with transform and assign:

使用此,使用转换和分配:

df.assign(Sales_StoreAvg = df.groupby('Store#')['Sales'].transform('mean'),
          Sales_All_Stores_Avg = df['Sales'].mean()).astype(int)

Output:

   Cashier#  Store#  Sales  Refunds  Sales_All_Stores_Avg  Sales_StoreAvg
0         1       1    100        1                   290             150
1         2       1    150        2                   290             150
2         3       1    200        2                   290             150
3         4       2    400        1                   290             500
4         5       2    600        4                   290             500

#1