Filter Rows in Polars: Numbers, Strings, Dates, Nulls

Every filter in Polars is an expression, not a string. That feels different at first but pays off the moment you chain conditions. Here is how to filter by number, text, null, and date.

Filter by a number

Compare the column expression directly. For a range, is_between reads cleaner than two comparisons.

• df.filter(pl.col("amount") > 100)

• df.filter(pl.col("amount").is_between(10, 50))

• df.filter(pl.col("id").is_in([1, 2, 3]))

Combine conditions

Use & and | between conditions, and wrap each one in parentheses. That parenthesis rule trips up almost everyone once.

• df.filter((pl.col("amount") > 100) & (pl.col("country") == "US"))

• df.filter((pl.col("a") < 0) | (pl.col("b").is_null()))

Filter by string (contains, like)

Polars has no LIKE keyword. Use the str namespace: contains for a substring or regex, starts_with and ends_with for anchors.

• df.filter(pl.col("name").str.contains("adil")) # substring or regex

• df.filter(pl.col("email").str.ends_with("@gmail.com"))

• df.filter(pl.col("sku").str.starts_with("AB"))

Filter nulls and use pl.lit

is_null and is_not_null handle missing data. pl.lit wraps a constant so Polars treats it as a value, not a column name, which matters inside a filter.

• df.filter(pl.col("email").is_not_null())

• df.filter(pl.col("status") == pl.lit("active"))

Filter dates, and rows that are not valid dates

Parse strings with str.to_date. To keep only the rows where a value is not a valid date, parse with strict=False so bad values become null, then filter on is_null.

• df.filter(pl.col("d").str.to_date("%Y-%m-%d") > pl.date(2026, 1, 1))

• # rows where the value is NOT a valid date:

• df.with_columns(pl.col("d").str.to_date("%Y-%m-%d", strict=False).alias("parsed")).filter(pl.col("parsed").is_null())

For creating frames to filter, see creating DataFrames; to inspect the result, see viewing DataFrames. Full reference: the Python Polars cheat sheet.