Filtering polars dataframe by row with boolean mask [closed]

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I'm trying to filter a Polars dataframe by using a boolean mask for the rows, which is generated from conditions on an specific column using:

df = df[df['col'] == cond]

And it's giving me an error because that filter is meant for column filter:

expected xx values when selecting columns by boolean mask, got yy

Where xx is the total columns and yy is the count of True's in the mask result.

According to the Polars API, that syntax should apply to filter to the rows (the same as how Pandas work), but it's instead trying to apply it to the columns.

Is there any way to change this behaviour?

PS: Please don't advice to use .filter or .sql instead, that's not what I'm asking here.

rhosignal.com/posts/polars-boolean-filter And i've found a couple more examples in the last few days using the same filter syntax, not sure if outdated. — Ghost
– Ghost, Commented Mar 17 at 17:39
Yeah, it only applied to rows in the early versions of Polars. github.com/pola-rs/polars/pull/4342 — jqurious
– jqurious, Commented Mar 17 at 17:46
Ah damn.. so, no way to bool-mask the rows? I know i can bypass it by just "converting" them to row numbers, but any way to do it directly? — Ghost
– Ghost, Commented Mar 17 at 18:51

etrotta · Accepted Answer · 2025-03-18 01:33:51Z

3

Use .filter() instead.

# You can use it with masks just fine:
bool_mask = [True, False, ...]
df.filter(bool_mask)

# Although ideally you should use it with expressions
df.filter(pl.col('xyz') == ...)

# Even if you need to call a custom function
df.filter(pl.col('xyz').map_elements(foo))

answered Mar 18 at 1:33

etrotta

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2 Comments

Ghost Mar 18 at 21:31

Literally, literally, LITERALLY, the PS. in my post says "don't advice to use .filter or .sql instead, that's not what i'm asking here."

robertspierre Oct 30 at 17:47

The right way to do that is with filter. If GCP freezes you should open an issue about that

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