Understanding Mypy’s Varied Responses with Untyped Variables and TypedDicts

Exploring Mypy’s Different Behaviors with Variables and Typed Dictionaries

In this discussion, we will delve into the nuances of Mypy, a widely-used static type checker for Python, and how it showcases distinct behaviors when handling untyped variables versus typed dictionaries. This exploration aims to provide insights into the intricacies of type checking in Python and empower you to navigate them effectively.

What You Will Learn

By the end of this guide, you will gain a comprehensive understanding of why Mypy treats bare variables differently from TypedDicts, and how you can leverage this knowledge to enhance the type safety of your code.

Introduction to the Problem and Solution

In the realm of Python development, especially in larger or more intricate projects, ensuring proper typing in your code is essential for maintainability and error prevention. A noteworthy scenario arises where Mypy treats untyped variables distinctively from those annotated as TypedDicts. This distinction can sometimes lead to confusion regarding when types are inferred or enforced by Mypy.

To address this issue effectively, we will first elucidate what bare variables and TypedDicts signify within Python typing conventions. Subsequently, we will investigate the reasons behind Mypy’s differential treatment of these entities. Through illustrative examples and detailed explanations, our goal is to equip you with the knowledge required to confidently utilize types in your Python projects.


from typing import TypedDict

class ExampleTypedDict(TypedDict):
    key: str

# Using a TypedDict
typed_dict_var: ExampleTypedDict = {"key": "value"}

# Using an untyped variable
untyped_var = {"key": "value"}

# Copyright PHD


Let’s break down why Mypy responds uniquely to these situations:

  1. TypedDictionaries (TypedDict) – By defining a variable as a TypedDict, you explicitly specify the expected structure (keys and their corresponding types) of the dictionary. This specificity allows Mypy to conduct rigorous type checks on both keys and values within your dictionary based on its definition.

  2. Untyped Variables – In the case of untyped variables like untyped_var, unless additional annotations are provided elsewhere (e.g., through function signatures), Mypy takes a more lenient approach during analysis. It primarily relies on initial assignments at declaration for inferring internal structure but does not enforce strict compliance across operations involving that variable as it would with a TypedDict.

This differentiation highlights how nuances in static typing influence coding practices within Python projects striving for high reliability and maintainability standards.

    1. Can I force stricter checks on untyped variables? Yes, using global flags such as –strict when running MyPy can enhance scrutiny over unannotated sections of your codebase.

    2. Does using TypedDitcs impact performance? No significant runtime performance impact arises from employing TypeDics; they mainly aid in improved type-checking during development.

    3. Are there alternatives if I find TypeDits too verbose? Exploring other typing constructs like data classes (dataclasses.dataclass) offers similar benefits but may be better suited for different scenarios.

    4. Is it possible for MyPy to infer types without any hints? While MyPy performs some level of inference based on usage patterns, providing explicit annotations consistently yields better results.

    5. How do I handle optional keys in ‘TypeDic’? Optional keys can be specified using either Union[None,…] or Optional[…,].


Understanding the distinctions between handling untyped variables and TypedDicts is crucial for effectively leveraging Python’s robust typing system. Armed with the insights shared here, you can confidently approach your next project with a deeper comprehension of both potential pitfalls and rewards offered by adhering to principles encapsulated within Python’s dynamic language features.

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