unique_combination_of_columns

checks.unique_combination_of_columns(data, columns=None)

Ensure that the selected column have a unique combination per row.

This function is particularly helpful to establish the granularity of a dataframe, i.e. this is a row oriented check.

Parameters

data: PolarsLazyOrDataFrame

description

columns: Optional[PolarsColumnType] = None

Columns to consider for row unicity. By default, all columns are checked.

Returns

Type	Description
PolarsLazyOrDataFrame	The original polars DataFrame or LazyFrame when the check passes

Examples

>>> import polars as pl
>>> import pelage as plg
>>> df = pl.DataFrame({"a": ["a", "a"], "b": [1, 2]})
>>> df.pipe(plg.unique_combination_of_columns, ["a", "b"])
shape: (2, 2)
┌─────┬─────┐
│ a   ┆ b   │
│ --- ┆ --- │
│ str ┆ i64 │
╞═════╪═════╡
│ a   ┆ 1   │
│ a   ┆ 2   │
└─────┴─────┘
>>> bad = pl.DataFrame({"a": ["X", "X"]})
>>> bad.pipe(plg.unique_combination_of_columns, "a")
Traceback (most recent call last):
...
pelage.checks.PolarsAssertError: Details
shape: (1, 2)
┌─────┬─────┐
│ a   ┆ len │
│ --- ┆ --- │
│ str ┆ u32 │
╞═════╪═════╡
│ X   ┆ 2   │
└─────┴─────┘
Error with the DataFrame passed to the check function:
-->Some combinations of columns are not unique. See above, selected: col("a")