get_type_hints() resolves annotations and can include metadata with include_extras=True.
What It Does and Why It Matters
get_type_hints() resolves annotations and can include metadata with include_extras=True. Evaluating annotations may resolve names or execute annotation expressions, so avoid inspecting untrusted definitions.
Annotations guide static tools; by default they do not validate external input or change the return value at runtime.
Step-by-Step Example
Start with the complete example below. Read the inputs and predict what the final print statement will show before you run it.
from typing import get_type_hints
def scale(quantity: int, factor: float) -> float:
return quantity * factor
print(get_type_hints(scale))How the Code Works
- The imports make these standard-library tools available: typing.
- The definition of scale creates reusable behavior; its body runs only when called or instantiated as appropriate.
- The print calls display the results. Compare these results with the method’s documented return type and the input used.
- The annotations describe intended types for editors and checkers. They are not a replacement for validating input values when the function runs.
Expected output:
{'quantity': <class 'int'>, 'factor': <class 'float'>, 'return': <class 'float'>}For the demonstrated inputs, Python displays "{'quantity': <class 'int'>, 'factor': <class 'float'>, 'return': <class 'float'>}". This result follows from the operation described above; change one input at a time to understand which rule matters.
What to Notice
Pay attention to the accepted input types, the returned value and the boundary behavior. Predict what changes if you replace the example input with an empty, zero, negative or missing value, where that value is meaningful for this operation.
If the task uses a library function, inspect its documented exceptions and return type before reusing it inside a larger function.
Where You Would Use This
This helps maintain larger projects where functions and data shapes cross module boundaries. Check the code with a static analyzer, and still validate data received from files, users or remote APIs. Annotation constructs do not normally enforce types automatically at runtime.
Practice Exercise
Inspect annotations of a small function and compare to its raw __annotations__.
Before running your edited example, write down the result you expect. Then test at least one valid boundary or deliberately invalid value, using appropriate exception handling where necessary.
Reference: official Python documentation for the complete API contract and version notes.
Common Beginner Mistakes
- Assuming annotations are always plain strings or introspection has no side effects.
- Assuming a type checker has checked values received dynamically from an untrusted source.
Key Takeaways
- get_type_hints() resolves annotations and can include metadata with include_extras=True.
- Inspect annotations of a small function and compare to its raw __annotations__.
- Annotations guide static tools; by default they do not validate external input or change the return value at runtime.