How to Rename Columns in Polars

Renaming columns is one of the first things you do after loading messy data. Polars keeps it simple, and nothing mutates in place: every rename returns a new frame. Here is each clean way to do it.

Rename one column or several at once

The main tool is df.rename, which takes a dict that maps old names to new ones. One pair or many, it works the same.

• df = df.rename({"old": "new"})

• df = df.rename({"a": "alpha", "b": "beta"})

• # only the columns you name change; the rest stay as they are

Replace column names with a dict

If you already have a mapping dict, hand it straight to rename. This is the answer to replacing column names from a dictionary.

• mapping = {"col1": "id", "col2": "date", "col3": "amount"}

• df = df.rename(mapping)

Rename every column with a function

To transform all the names at once, build the dict from df.columns with a comprehension. This covers lowercasing, trimming, and swapping spaces for underscores.

• # lowercase every column name:

• df = df.rename({c: c.lower() for c in df.columns})

• # strip spaces and use snake_case:

• df = df.rename({c: c.strip().lower().replace(" ", "_") for c in df.columns})

Rename inline with alias

When you are already selecting or transforming, rename in the same step with .alias instead of a separate rename call. This is the idiomatic Polars style inside select and with_columns.

• df.select(pl.col("old").alias("new"))

• df.with_columns((pl.col("price") * 1.2).alias("price_with_tax"))

Everything here works the same on a LazyFrame. Renaming only relabels, so it is cheap: use it freely early in a lazy pipeline before you collect. For the full toolkit, see the Python Polars cheat sheet. Next up: creating DataFrames and filtering rows.