not_accepted_values

checks.not_accepted_values(data, items)

Raises error if columns contains values specified in List of forbbiden items

Parameters

data: PolarsLazyOrDataFrame

items: Dict[str, List]

A dictionnary where keys are a string compatible with a pl.Expr, to be used with pl.col(). The value for each key is a List of all forbidden values in the dataframe.

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": [1, 2, 3], "b": ["a", "b", "c"]}
... )
>>> df.pipe(plg.not_accepted_values, {"a": [4, 5]})
shape: (3, 2)
┌─────┬─────┐
│ a   ┆ b   │
│ --- ┆ --- │
│ i64 ┆ str │
╞═════╪═════╡
│ 1   ┆ a   │
│ 2   ┆ b   │
│ 3   ┆ c   │
└─────┴─────┘
>>> df.pipe(plg.not_accepted_values, {"b": ["a", "b"]})
Traceback (most recent call last):
...
pelage.checks.PolarsAssertError: Details
shape: (2, 1)
┌─────┐
│ b   │
│ --- │
│ str │
╞═════╡
│ a   │
│ b   │
└─────┘
Error with the DataFrame passed to the check function:
-->This DataFrame contains values marked as forbidden