How to implode(reverse of pandas explode) based on a column

If you want the "opposite" of explode, then that means bringing it into a list in Solution #1. You can also join as a sting in Solution #2:

Use lambda x: x.tolist() for the 'APPLICABLE_DAYS' column within your .agg groupby function:

df = (df.groupby(['NETWORK','config_id'])
      .agg({'APPLICABLE_DAYS': lambda x: x.tolist(),'Case':'mean','Delivery':'mean'})
      .rename({'Case' : 'Avg_Cases','Delivery' : 'Avg_Delivery'},axis=1)
      .reset_index())
df
Out[1]: 
   NETWORK  config_id       APPLICABLE_DAYS  Avg_Cases  Avg_Delivery
0  Grocery       5399  [SUN, MON, TUE, WED]         25           2.5

---

Use lambda x: ",".join(x) for the 'APPLICABLE_DAYS' column within your .agg groupby function:

 df = (df.groupby(['NETWORK','config_id'])
      .agg({'APPLICABLE_DAYS': lambda x: ",".join(x),'Case':'mean','Delivery':'mean'})
      .rename({'Case' : 'Avg_Cases','Delivery' : 'Avg_Delivery'},axis=1)
      .reset_index())
df
Out[1]: 
   NETWORK  config_id       APPLICABLE_DAYS  Avg_Cases  Avg_Delivery
0  Grocery       5399       SUN,MON,TUE,WED         25           2.5

If you are looking for the sum, then you can just change mean to sum for the Cases and Delivery columns.