Karl Genockey
Karl Genockey
Can reproduce. It seems the `_right` is completely ignored for whatever reason. ```python (df1.join(df2, on="a") .select(pl.col("b").filter(pl.col("b") == pl.col("foo_right"))) .collect() ) # ComputeError: column 'foo' not available in 'DataFrame' with Schema:...
Can reproduce. Seems to be one of those optimizer issues: ```python >>> df.filter(pl.col("x").eq("a")).collect(comm_subplan_elim=False) shape: (2, 2) ┌─────┬─────┐ │ x ┆ y │ │ --- ┆ --- │ │ str ┆...
@knl You can try disabling specific optimizations to check for potential causes e.g. `.collect(comm_subplan_elim=False)` The example query in this issue ran for me under 2 situations: https://github.com/pola-rs/polars/issues/15980#issuecomment-2088234408
If I understand correctly, this appears to be a minimal repro? Data: ```shell wget https://nemweb.com.au/Reports/Current/Daily_Reports/PUBLIC_DAILY_202401270000_20240128040505.zip unzip PUBLIC_DAILY_202401270000_20240128040505.zip ``` `.read_csv` works as expected. ```python import polars as pl pl.read_csv( "PUBLIC_DAILY_202401270000_20240128040505.CSV", skip_rows=1,...
I suppose the data is not actually needed. Simpler repro: ```python import polars as pl import tempfile with tempfile.NamedTemporaryFile() as f: f.write(b""" A,B,C 1,2,3 4,5,6,7,8 9,10,11 """.strip()) f.seek(0) df =...
It looks like @filabrazilska did file a PR to address this https://github.com/pola-rs/polars/pull/15305 But it hasn't been reviewed yet. (PRs can be linked to issues with keywords: https://docs.github.com/en/issues/tracking-your-work-with-issues/linking-a-pull-request-to-an-issue#linking-a-pull-request-to-an-issue-using-a-keyword)
Hi [mdevore300](https://github.com/mdevore300) If you look at the traceback, the error is coming from `pdfminer` trying to load `cryptography` (pdfplumber uses pdfminer). > Import Cryptography on its own works fine. Not...
Yes, that suggests your `cryptography` installation is somehow "broken". Not used anaconda, but it looks like the next step in debugging would be creating a fresh environment and seeing if...
The working version also appears to be broken. The `1` from `a` ends up in `b` ```python df.group_by('c').agg( pl.col('a').filter(pl.col('a').eq(1)), pl.col('b').filter(pl.col('b').ge(0)) ) # shape: (1, 3) # ┌─────┬───────────┬───────────┐ # │ c...
I was going to ask if `.partition_by` could just be implemented in terms of `.group_by` so they behave the same? But experimenting with your latest selectors example, there seems to...