如何基于列内爆(大 pandas 反转爆炸) [英] How to implode(reverse of pandas explode) based on a column

问题描述

我有一个如下所示的数据框df

I have a dataframe df like below

  NETWORK       config_id       APPLICABLE_DAYS  Case    Delivery  
0   Grocery     5399            SUN               10       1        
1   Grocery     5399            MON               20       2       
2   Grocery     5399            TUE               30       3        
3   Grocery     5399            WED               40       4       

我想进行内爆(将多行的Applicable_days合并为单行，如下所示)，并获取每个config_id的平均情况和交付情况

I want to implode( combine Applicable_days from multiple rows into single row like below) and get the average case and delivery per config_id

  NETWORK       config_id       APPLICABLE_DAYS      Avg_Cases    Avg_Delivery 
0   Grocery     5399            SUN,MON,TUE,WED         90           10

使用网络上的groupby，config_id，我可以得到如下所示的avg_cases和avg_delivery.

using the groupby on network,config_id i can get the avg_cases and avg_delivery like below.

df.groupby(['network','config_id']).agg({'case':'mean','delivery':'mean'})

但是在执行此聚合时我如何能够加入APPLICABLE_DAYS?

But How do i be able to join APPLICABLE_DAYS while performing this aggregation?

推荐答案

爆炸，则意味着将其放入解决方案#1中的列表.您也可以加入刺痛方式加入解决方案#2:

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:

在 .agg groupby函数中的'APPLICABLE_DAYS'列中使用 lambda x:x.tolist():

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

---

在 .agg groupby函数中的'APPLICABLE_DAYS'列中使用 lambda x:，.join(x):

---

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

如果您要查找 sum ，则可以将 Cases 的 mean 更改为 sum .和 Delivery 列.

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

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