I'm working with a set of classes and functions for analyzing pandas series. The goal is to allow new analysis modules to plug in, using dictionaries that specify required pre-computed values and those that are provided. This way, I'm able to avoid redundant calculations and can arrange the analysis objects as a directed acyclic graph (DAG). I also want to check beforehand if any required values are missing before execution.
Here's a snippet of my code:
```python
class SometimesProvides(ColAnalysis):
provides_defaults = {'conditional_on_dtype':'xcvz'}
requires_summary = []
@staticmethod
def series_summary(ser, _sample_ser):
import pandas as pd
is_numeric = pd.api.types.is_numeric_dtype(ser)
if is_numeric:
return dict(conditional_on_dtype=True)
return {}
class DumbTableHints(ColAnalysis):
provides_defaults = {
'is_numeric':False, 'is_integer':False, 'histogram':[]}
requires_summary = ['conditional_on_dtype']
@staticmethod
def computed_summary(summary_dict):
return {'is_numeric':True,
'is_integer': summary_dict['conditional_on_dtype'],
'histogram': []}
sdf3, errs = produce_series_df(
test_df, order_analysis(DumbTableHints, SometimesProvides))
```
While I understand how to use hinting with `SometimesProvides.provides_defaults`, I'm puzzled about how to enforce type checks at runtime for the `summary_dict` being passed to `DumbTableHints.computed_summary`. I want to ensure that this operates smoothly in an interactive Jupyter notebook, rather than relying on static type checking through tools like mypy, which might lead to less legible error messages. Any advice on how to tackle this?
3 Answers
Consider using libraries like [beartype](https://beartype.readthedocs.io/en/latest/) instead of mypy. Beartype allows for both runtime and static type checks, giving you the flexibility you need for your interactive work.
You mention wanting to do type checking but seem to be moving away from using mypy and static checks, which is actually what type checking is. In Python, there’s really no built-in type system for runtime checking unless you implement your own checks. Since Pandas columns can have different types which you can’t always predict, consider using generics for more flexibility. Alternatively, exploring dependent types might be a solution, but that can be complex in Python.
If you're looking to check types at runtime, the `isinstance()` function is your friend. For example:
```python
x = 0
if isinstance(x, int):
print("x is an int")
```
You can use this within your analysis classes to ensure that the types of your inputs match as expected.
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