Comment by latent-person

25 days ago

The text right above the code says why you can't...

edit:

Let me clarify. From the blog-post:

> since a `DataFrameGroupBy` object doesn’t have a `.query()` or boolean-indexing shortcut of its own, so filtering within groups needs `.apply()` again, and the surrounding pipeline has to be rebuilt around it:

Hence you really do need one of the versions of the code I gave. You can't do the naive approach with just `.groupby().filter(lambda: )`, since you need a row-wise decision.

4 comments

latent-person

__mharrison__ 24 days ago

I misspoke, you need to use .groupby/.transform to add a new filtering column:

    (sales
      .assign(country_median=lambda df_: (
          df_.groupby("country")["amount"].transform("median")
      ))
      .query("amount <= country_median * 10")
      .assign(net=pd.col('amount') - pd.col('discount'))
      .groupby("country", as_index=False)
      .agg(total=("net", "sum"))
    )

latent-person 24 days ago

Yes, so basically equivalent to the code I showed in the blog.

__mharrison__ 25 days ago

I'm confused, you can use filter after a groupby in pandas...

It's late here, I'm going to bed, perhaps I'll write the code tomorrow when I'm at my laptop and not on my phone.