Niels Bantilan
Niels Bantilan
Hi @Blogem, yes this is the behavior I encountered when testing out the pyspark integration... unfortunately I couldn't think of a nice way of handling this without keeping track of...
currently there's no way of specifying a "number" column since right now pandera adheres to pandas data types (and also in general python doesn't have a generic number type), although...
oh, I guess another way of doing this would be to specify `pandas_dtype = None` (the default) and then use a `Check` to validate a number type: ```python import pandera...
> I think we should add a built-in Number type that includes all kinds of integers and floats The higher-level data types are still TBD, but Number will most likely...
adjusted the tags, PR is welcome after the fix for #369 is done
hey @quancore you can register checks into the `pa.Check` namespace with the [extensions API](https://pandera.readthedocs.io/en/stable/extensions.html). I'd recommend doing that, as I don't think it makes sense to temporarily add a built-in...
Amazing @vignkri ! Super excited to get built-in support for this :) So for each of the tests you list above, it would help if you could provide code snippets...
as part of this issue, rename the `transformer` argument to `parser` in the `pa.DataFrameSchema`, and it should have the same function signature and behavior as a `pa.parser` decorator.
thanks for the feedback @Tankske, will consider it when working out the specific implementation for this issue. The `Check` object has an [`error`](https://pandera.readthedocs.io/en/stable/generated/pandera.Check.html#pandera.Check) argument that serves the same purpose as...