</>
pythonbeginner

Polars String Operations

Use the Polars .str namespace for fast, vectorised string cleaning and extraction.

python
import polars as pl

df = pl.DataFrame({'email':['Alice@Example.COM','  bob@domain.org ','CHARLIE@TEST.IO'],'code':['US-001','GB-002','DE-003']})

result = df.with_columns([
    pl.col('email').str.strip_chars().str.to_lowercase().alias('email_clean'),
    pl.col('code').str.split('-').list.first().alias('country'),
    pl.col('code').str.extract(r'-(\d+)$', 1).cast(pl.Int32).alias('num'),
])
print(result)

Use Cases

  • data cleaning
  • feature extraction
  • ETL text ops

Tags

#polars#strings#cleaning#etl

Related Snippets

Similar patterns you can reuse in the same workflow.

pythonintermediate

Polars DataFrame Operations

High-performance DataFrame operations using Polars: filtering, groupby, joins, and lazy evaluation.

Best for: data transformation

#polars#dataframe
pythonbeginner

Pandas String Operations

Clean, extract, and transform string columns using pandas .str accessor methods.

Best for: data cleaning

#pandas#strings
pythonintermediate

Polars Join Strategies

Perform inner, left, cross, and anti joins in Polars with optimal join strategies.

Best for: data enrichment

#polars#join
pythonintermediate

Polars Expressions API Patterns

Use Polars expression API for complex column-level transformations without apply or loops.

Best for: column transformations

#polars#expressions