Modify pandas iteration from iterrows to apply/vectorized format

Question

While using iterrows to implement the logic takes lot of time.Can some suggest a way on how I could optimize the code with vectorized/apply()

Below is the input table..From a partition of (ITEMSALE,ITEMID),I need to populate rows with rank=1 .If any column value is null in rank=1,I need to populate the next available value in that column.This has to be done for all columns in dataset.

Below is the output format expected

I have tried below logic using iterrows where am accessing values rowise.Performance is too low using this method.

Please provide text based data and code. Do not use images. – mozway Jul 26 '22 at 03:30 — mozway, Jul 26 '22 at 03:30

score 0 · Answer 1 · answered Jul 26 '22 at 03:39

0

This should get you what you need

df.loc[df.loc[df['Item_ID'].isna()].groupby('Item_Sale')['Date'].idxmin()]

answered Jul 26 '22 at 03:39

ArchAngelPwn

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Using groupby and first() helped to achieve the result.Thanks for helping out – VIDYA RENUKA Jul 26 '22 at 16:51

Modify pandas iteration from iterrows to apply/vectorized format

1 Answers1