Reading all data in a csv to any other type than pl.String likely fails with a lot of null values. We can use expressions to declare how we want to deal with those null values.

If you read a csv with infer_schema_length=0, polars does not know the schema and will read all columns as pl.String as that is a super type of all polars types.

When read as String we can use expressions to cast all columns.

(pl.read_csv("test.csv", infer_schema_length=0)
   .with_columns(pl.all().cast(pl.Int32, strict=False))

Update: infer_schema=False was added in 1.2.0 as a more user-friendly name for this feature.

pl.read_csv("test.csv", infer_schema=False) # read all as pl.String

IDK if there is any polars way to achieve this. My solution make use of getattr builtin function to fetch the attribute from module object.

>>> import polars as pl
>>> 
>>> 
>>> def convert_string_to_polars_dtype(
...     mapping: dict[str, str]
... ) -> dict[str, pl.PolarsDataType]:
...     return {key: getattr(pl, value.split(".")[1]) for key, value in mapping.items()}
... 
>>> 
>>> columns_dtypes_polars = {
...     "pcd": "pl.Utf8",
...     "streg": "pl.Int64",
...     "oac11": "pl.Utf8",
...     "lat": "pl.Float64",
...     "long": "pl.Float64",
...     "imd": "pl.Int64",
... }
>>> 
>>> print(convert_string_to_polars_dtype(columns_dtypes_polars))
{'pcd': Utf8, 'streg': Int64, 'oac11': Utf8, 'lat': Float64, 'long': Float64, 'imd': Int64}