kra.columns.Cols

A namespace for convenient DataFrame column operations, accessible via df.cols for any Polars DataFrame.

Methods

apply(fun, in_place=False)

Description: Apply a function to all column names.

Parameters:

  • fun (callable): Function to apply to each column name.

  • in_place (bool, default False): If True, modify the DataFrame in place. If False, return a copy.

Returns:

  • pl.DataFrame: DataFrame with transformed column names.

Example:

import polars as pl
df = pl.DataFrame({"A": [1], "B": [2]})
df.cols.apply(lambda x: x.lower())
# shape: (1, 2)
# ┌─────┬─────┐
# │ a   ┆ b   │
# └─────┴─────┘

to_lowercase(in_place=False)

Description: Convert all column names to lowercase.

Parameters:

  • in_place (bool, default False): If True, modify in place. If False, return a copy.

Returns:

  • pl.DataFrame: DataFrame with lowercase column names.

Example:

import polars as pl
df = pl.DataFrame({"A": [1], "B": [2]})
df.cols.to_lowercase()
# shape: (1, 2)
# ┌─────┬─────┐
# │ a   ┆ b   │
# └─────┴─────┘

to_uppercase(in_place=False)

Description: Convert all column names to uppercase.

Parameters:

  • in_place (bool, default False): If True, modify in place. If False, return a copy.

Returns:

  • pl.DataFrame: DataFrame with uppercase column names.

Example:

import polars as pl
df = pl.DataFrame({"a": [1], "b": [2]})
df.cols.to_uppercase()
# shape: (1, 2)
# ┌─────┬─────┐
# │ A   ┆ B   │
# └─────┴─────┘

to_titlecase(in_place=False)

Description: Convert all column names to title case.

Parameters:

  • in_place (bool, default False): If True, modify in place. If False, return a copy.

Returns:

  • pl.DataFrame: DataFrame with title-cased column names.

Example:

import polars as pl
df = pl.DataFrame({"first name": [1], "last name": [2]})
df.cols.to_titlecase()
# shape: (1, 2)
# ┌────────────┬───────────┐
# │ First Name ┆ Last Name │
# └────────────┴───────────┘

to_camelcalse(in_place=False)

Description: Convert all column names to camel case (e.g., “FirstName”).

Parameters:

  • in_place (bool, default False): If True, modify in place. If False, return a copy.

Returns:

  • pl.DataFrame: DataFrame with camelCase column names.

Example:

import polars as pl
df = pl.DataFrame({"first name": [1], "last name": [2]})
df.cols.to_camelcalse()
# shape: (1, 2)
# ┌──────────┬─────────┐
# │ FirstName┆ LastName│
# └──────────┴─────────┘

to_snakecase(in_place=False)

Description: Convert all column names to snake_case.

Parameters:

  • in_place (bool, default False): If True, modify in place. If False, return a copy.

Returns:

  • pl.DataFrame: DataFrame with snake_case column names.

Example:

import polars as pl
df = pl.DataFrame({"First Name": [1], "Last Name": [2]})
df.cols.to_snakecase()
# shape: (1, 2)
# ┌────────────┬───────────┐
# │ first_name ┆ last_name │
# └────────────┴───────────┘

replace(pattern, repl, in_place=False)

Description: Replace a regex pattern in all column names.

Parameters:

  • pattern (str or re.Pattern): Pattern to search for.

  • repl (str): Replacement string.

  • in_place (bool, default False): If True, modify in place. If False, return a copy.

Returns:

  • pl.DataFrame: DataFrame with replaced column names.

Example:

import polars as pl
df = pl.DataFrame({"foo-bar": [1], "baz-bar": [2]})
df.cols.replace("-bar", "_suffix")
# shape: (1, 2)
# ┌──────────┬────────────┐
# │ foo_suffix ┆ baz_suffix │
# └──────────┴────────────┘

has_all(columns, return_missing=True)

Description: Check if all specified columns are present in the DataFrame.

Parameters:

  • columns (list of str): List of column names to check.

  • return_missing (bool, default True): If True, also return a list of missing columns.

Returns:

  • bool or (bool, list of str): True if all columns are present, otherwise False. If return_missing is True, also returns a list of missing columns.

Example:

import polars as pl
df = pl.DataFrame({"a": [1], "b": [2]})
df.cols.has_all(["a", "b"])
# (True, [])
df.cols.has_all(["a", "c"])
# (False, ['c'])

has_any(columns)

Description: Check if any of the specified columns are present in the DataFrame.

Parameters:

  • columns (list of str): List of column names to check.

Returns:

  • bool: True if any column is present, otherwise False.

Example:

import polars as pl
df = pl.DataFrame({"a": [1], "b": [2]})
df.cols.has_any(["b", "c"])
# True
df.cols.has_any(["x", "y"])
# False

has_exactly(columns)

Description: Check if the DataFrame has exactly the specified columns (no more, no less).

Parameters:

  • columns (list of str): List of column names to check.

Returns:

  • bool: True if the DataFrame has exactly these columns, otherwise False.

Example:

import polars as pl
df = pl.DataFrame({"a": [1], "b": [2]})
df.cols.has_exactly(["a", "b"])
# True
df.cols.has_exactly(["a"])
# False

rename(mapping)

Description: Rename columns using a mapping, skipping keys not present in the DataFrame. A non-strict version of DataFrame.rename().

Parameters:

  • mapping (dict of str to str): Mapping from old column names to new names.

Returns:

  • pl.DataFrame: DataFrame with renamed columns.

Example:

import polars as pl
df = pl.DataFrame({"a": [1], "b": [2]})
df.cols.rename({"a": "x", "c": "y"})
# shape: (1, 2)
# ┌─────┬─────┐
# │ x   ┆ b   │
# └─────┴─────┘

For more details, see the source code or the Usage page.