has_dtypes

checks.has_dtypes(data, items)

Check that the columns have the expected types

Parameters

data: PolarsLazyOrDataFrame

Polars DataFrame or LazyFrame containing data to check.

items: Dict[str, PolarsDataType]

A dictionnary of column name with their expected polars data type:

{
    "col_a": pl.String,
    "col_b": pl.Int64,
    "col_c": pl.Float64,
    ...
}

Returns

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

Examples

>>> import polars as pl
>>> from pelage import checks
>>> df = pl.DataFrame({
...     "name": ["Alice", "Bob", "Charlie"],
...     "age": [20, 30, 40],
...     "city": ["New York", "London", "Paris"],
... })
>>> checks.has_dtypes(df, {
...     "name": pl.String,
...     "age": pl.Int64,
...     "city": pl.String,
... })
shape: (3, 3)
┌─────────┬─────┬──────────┐
│ name    ┆ age ┆ city     │
│ ---     ┆ --- ┆ ---      │
│ str     ┆ i64 ┆ str      │
╞═════════╪═════╪══════════╡
│ Alice   ┆ 20  ┆ New York │
│ Bob     ┆ 30  ┆ London   │
│ Charlie ┆ 40  ┆ Paris    │
└─────────┴─────┴──────────┘
>>> checks.has_dtypes(df, {
...     "age": pl.String,
...     "city": pl.Int64,
... })
Traceback (most recent call last):
    ...
pelage.checks.PolarsAssertError: Details
Error with the DataFrame passed to the check function:
--> Some columns don't have the expected type:
column='age', expected_type=String, real_dtype=Int64
column='city', expected_type=Int64, real_dtype=String