How to Apply Custom Functions in Polars (.map_elements())

You’ve mastered the fast Polars Expression API. But what if you need to run a complex Python function that Polars doesn’t have?, so it’s Polars apply function.

In Pandas, you use .apply(), which is notoriously slow. In Polars, the equivalent is .map_elements() (or .apply() in some contexts), but it comes with a big warning:

⚠️ AVOID IF POSSIBLE!

Using .map_elements() (or .apply()) kills Polars’ performance. It takes your parallel, Rust-optimized query and forces it back into a slow, row-by-row Python loop.

Always try to use native expressions first (like pl.when/then or .str). If you must use it, here’s how.

The .map_elements() Method

This is the safest way to apply a custom function.

import polars as pl

df = pl.DataFrame({"value": [1, 5, 10]})

# A complex function Polars can't do natively
def my_complex_logic(x):
    if x < 3:
        return "Low"
    elif 3 <= x < 7:
        return "Mid"
    else:
        return "High"

# Use .map_elements() to run the function on each value
result = df.with_columns(
    pl.col("value").map_elements(my_complex_logic).alias("Category")
)
print(result)

Output:

shape: (3, 2)
┌───────┬──────────┐
│ value ┆ Category │
│ ---   ┆ ---      │
│ i64   ┆ str      │
╞═══════╪══════════╡
│ 1     ┆ Low      │
│ 5     ┆ Mid      │
│ 10    ┆ High     │
└───────┴──────────┘

This is a powerful escape hatch for when you need complex logic, but always remember it’s your slowest option.