How do I conditionally aggregate values in projection part of pandas query?

问题

I currently have a csv file with this content:

 ID PRODUCT_ID        NAME  STOCK  SELL_COUNT DELIVERED_BY
1         P1  PRODUCT_P1     12          15          UPS
2         P2  PRODUCT_P2      4           3          DHL
3         P3  PRODUCT_P3    120          22          DHL
4         P1  PRODUCT_P1    423          18          UPS
5         P2  PRODUCT_P2      0           5          GLS
6         P3  PRODUCT_P3     53          10          DHL
7         P4  PRODUCT_P4     22           0          UPS
8         P1  PRODUCT_P1     94          56          GLS
9         P1  PRODUCT_P1      9          24          GLS

When I execute this SQL query:

    SELECT
      PRODUCT_ID,
      MIN(CASE WHEN DELIVERED_BY = 'UPS' THEN STOCK END) as STOCK,
      SUM(CASE WHEN ID > 6 THEN SELL_COUNT END) as TOTAL_SELL_COUNT,
      SUM(CASE WHEN SELL_COUNT * 100 > 1000 THEN SELL_COUNT END) as COND_SELL_COUNT
    FROM products
    GROUP BY PRODUCT_ID;

I get the desired result:

PRODUCT_ID  STOCK   TOTAL_SELL_COUNT    COND_SELL_COUNT
P1          12      80                  113
P2          null    null                null
P3          null    null                22
P4          22      0                   null

Now I'm trying to somehow get the same result on that dataset using pandas, and that's what I'm struggling with.

I imported the csv file to da DataFrame called df_products. Then I tried this:

def custom_aggregate(grouped):

    data = {
        'STOCK': np.where(grouped['DELIVERED_BY'] == 'UPS', grouped['STOCK'].min(), np.nan)  # [grouped['STOCK'].min() if grouped['DELIVERED_BY'] == 'UPS' else None]
    }

    d_series = pd.Series(data)
    return d_series


result = df_products.groupby('PRODUCT_ID').apply(custom_aggregate)
print(result)

As you can see I'm nowhere near the expected result as I'm already having problems getting the conditional STOCK aggregration to work depending on the DELIVERED_BY values.

This outputs:

                           STOCK
PRODUCT_ID                      
P1          [9.0, 9.0, nan, nan]
P2                    [nan, nan]
P3                    [nan, nan]
P4                        [22.0]

which is not even in the correct format, but I'd be happy if I could get the expected 12.0 instead of 9.0 for P1.

Thanks

---

I just wanted to add that I got near the result by creating additional columns:

df_products['COND_STOCK'] = df_products[df_products['DELIVERED_BY'] == 'UPS']['STOCK']
df_products['SELL_COUNT_ID_GT6'] = df_products[df_products['ID'] > 6]['SELL_COUNT']
df_products['SELL_COUNT_GT1000'] = df_products[(df_products['SELL_COUNT'] * 100) > 1000]['SELL_COUNT'] 

The function would then look like this:

def custom_aggregate(grouped):

    data = {
        'STOCK': grouped['COND_STOCK'].min(),
        'TOTAL_SELL_COUNT': grouped['SELL_COUNT_ID_GT6'].sum(),
        'COND_SELL_COUNT': grouped['SELL_COUNT_GT1000'].sum(),
    }

    d_series = pd.Series(data)
    return d_series


result = df_products.groupby('PRODUCT_ID').apply(custom_aggregate)

This is the 'almost' desired result:

            STOCK  TOTAL_SELL_COUNT  COND_SELL_COUNT
PRODUCT_ID                                          
P1           12.0              80.0            113.0
P2            NaN               0.0              0.0
P3            NaN               0.0             22.0
P4           22.0               0.0              0.0

回答1:

Usually we can write the pandas as below

df.groupby('PRODUCT_ID').apply(lambda x : pd.Series({'STOCK':x.loc[x.DELIVERED_BY =='UPS','STOCK'].min(),
                                                 'TOTAL_SELL_COUNT': x.loc[x.ID>6,'SELL_COUNT'].sum(min_count=1),
                                                 'COND_SELL_COUNT':x.loc[x.SELL_COUNT>10,'SELL_COUNT'].sum(min_count=1)}))

Out[105]:

            STOCK  TOTAL_SELL_COUNT  COND_SELL_COUNT
PRODUCT_ID                                          
P1           12.0              80.0            113.0
P2            NaN               NaN              NaN
P3            NaN               NaN             22.0
P4           22.0               0.0              NaN

来源：https://stackoverflow.com/questions/57317598/how-do-i-conditionally-aggregate-values-in-projection-part-of-pandas-query