pandas & polars

araclean registers an .araclean accessor on pandas and polars Series, so normalizing a text column is one idiomatic call. Both adapters validate their arguments through the same trust boundary as normalize, build the pipeline once, and map it over the column — they hold no normalization logic of their own.

Each lives behind its extra:

pip install 'araclean[pandas]'   # or 'araclean[polars]', or 'araclean[all]'

pandas

Importing araclean.pandas registers the accessor (importing it without pandas installed raises a clear error naming the extra):

>>> import pandas as pd
>>> import araclean.pandas
>>> s = pd.Series(["العـــربية", "اَلسّلامُ عليكم"])
>>> s.araclean.normalize(profile="search").tolist()
['العربيه', 'السلام عليكم']

In a dataframe workflow:

>>> df = pd.DataFrame({"text": ["جميييييل 😍 https://t.co/xyz"]})
>>> df["text"].araclean.normalize(profile="social", emoji="strip").tolist()
['جمييل [رابط]']

Missing values (NaN/None) pass through unchanged (na_action="ignore"); empty strings normalize to empty strings.

polars

Importing araclean.polars registers the namespace, mirroring the pandas accessor:

>>> import polars as pl
>>> import araclean.polars
>>> s = pl.Series(["العـــربية", "اَلسّلامُ عليكم"])
>>> s.araclean.normalize(profile="search").to_list()
['العربيه', 'السلام عليكم']

Use it inside an expression pipeline via map_batches on the column, or normalize the Series and reattach it:

>>> df = pl.DataFrame({"text": ["العـــربية"]})
>>> df.with_columns(df["text"].araclean.normalize(profile="search").alias("text")).to_dicts()
[{'text': 'العربيه'}]

Null values pass through unchanged; the result is a String Series, value-for-value identical to what the pandas accessor produces.

Overrides and errors

Both accessors take the same profile plus per-knob **overrides as normalize — map_digits=True, emoji="strip", teh_marbuta="keep", … An unknown profile, knob, or value raises the same clear error as the facade, before any row is touched. See Tuning profiles.

Performance notes

The pipeline is assembled and validated once per call, not once per row — the per-row work is the optimized core (see Architecture & performance). For very large datasets prefer the CLI on JSONL, which streams without materializing the corpus, or Pipeline.batch over an iterator in Python.