CODEWITHSUNDEEP
pythonpython-polars
seb2704
seb2704

Reputation: 540

polars categorical feature and lazy api doesn't work like expected

I'm trying to join two Dataframes with the help of categorical features and the lazy API. I tried to do it the way it was decribed in the user guide(https://pola-rs.github.io/polars-book/user-guide/performance/strings.html)

count = admin_df.groupby(['admin','EVENT_DATE']).pivot(pivot_column='FIVE_TYPE',values_column='count').first().lazy()
fatalities = admin_df.groupby(['admin','EVENT_DATE']).pivot(pivot_column='FIVE_TYPE',values_column='FATALITIES').first().lazy()
fatalities = fatalities.with_column(pl.col("admin").cast(pl.Categorical))
count = count.with_column(pl.col("admin").cast(pl.Categorical))
admin_df = fatalities.join(count,on=['admin','EVENT_DATE']).collect()

but i get the following Error:

    Traceback (most recent call last):
  File "country_level.py", line 33, in <module>
    country_level('/c/Users/Sebastian/feast/fluent_sunfish/data/ACLED_geocoded.parquet')
  File "country_level.py", line 10, in country_level
    country_df=aggregate_by_date(df)
  File "country_level.py", line 29, in aggregate_by_date
    admin_df = fatalities.join(count,on=['admin','EVENT_DATE']).collect()
  File "/home/sebastian/.local/lib/python3.8/site-packages/polars/internals/lazy_frame.py", line 293, in collect
    return pli.wrap_df(ldf.collect())
RuntimeError: Any(ValueError("joins on categorical dtypes can only happen if they are created under the same global string cache"))

with the usage of with pl.StringCache(): everything works fine, altough the user guide says it isn't needed if you use the lazy API, do i missing something or is this a bug?

Upvotes: 6

Views: 2210

Answers (1)

ritchie46
ritchie46

Reputation: 14730

You can set a global string cache with pl.StringCache(): or with pl.enable_string_cache()

import polars as pl

pl.enable_string_cache()

lf1 = pl.DataFrame({
    "a": ["foo", "bar", "ham"], 
    "b": [1, 2, 3]
}).lazy()

lf2 = pl.DataFrame({
    "a": ["foo", "spam", "eggs"], 
    "c": [3, 2, 2]
}).lazy()

lf1 = lf1.with_columns(pl.col("a").cast(pl.Categorical))
lf2 = lf2.with_columns(pl.col("a").cast(pl.Categorical))

lf1.join(lf2, on="a", how="inner").collect()

Outputs:

shape: (1, 3)
┌─────┬─────┬─────┐
│ a   ┆ b   ┆ c   │
│ --- ┆ --- ┆ --- │
│ cat ┆ i64 ┆ i64 │
╞═════╪═════╪═════╡
│ foo ┆ 1   ┆ 3   │
└─────┴─────┴─────┘

Upvotes: 7

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