NumPy: Perform type checking with ‘mypy’ (4 examples)

Updated: February 20, 2024 By: Guest Contributor Post a comment


NumPy is a cornerstone of numerical computing in Python, providing support for large, multi-dimensional arrays and matrices, along with a collection of mathematical functions to operate on these arrays. While working with NumPy, ensuring that functions and operations receive and return arrays of the correct type and shape can prevent many runtime errors. ‘mypy’, an optional static type checker for Python, can be a powerful ally in enforcing type correctness in NumPy code. In this tutorial, we’ll explore how to leverage ‘mypy’ for performing type checking in NumPy, covering basic to advanced examples.

What is ‘mypy’?

‘mypy’ is a static type checker for Python, meaning it analyzes your code to detect type errors without actually running it. It uses Python’s type hints (PEP 484) to accomplish this. By adopting ‘mypy’ in your workflow, you can catch type-related errors early in the development process, thereby improving the reliability and maintainability of your code.


  • Basic understanding of Python and NumPy.
  • Python 3.6 or higher installed.
  • ‘mypy’ installed in your development environment. You can install it using pip install mypy.
  • A basic setup of a Python project where you wish to incorporate ‘mypy’.

Example 1: Basic Type Checking

Let’s start with a simple example to get familiar with using ‘mypy’ for type checking with NumPy. Assume we have a function that calculates the sum of a NumPy array:

import numpy as np
from typing import Any

def array_sum(arr: np.ndarray) -> np.number:
    return np.sum(arr)

To run ‘mypy’ on this code snippet, save it to a file (e.g.,, and then run mypy in your terminal. If there are no type errors, ‘mypy’ will return without any messages.

Example 2: Enforcing Array Shape

Moving on to a slightly more advanced example, let’s implement a function that takes a two-dimensional array and returns its transpose:

import numpy as np
from typing import Tuple

def transpose(arr: np.ndarray) -> np.ndarray:
    assert arr.ndim == 2, "Array must be 2-dimensional."
    return arr.T

This function enforces that the input array is two-dimensional by asserting its shape. With ‘mypy’, you can further ensure type safety by specifying the expected shape of the array in the function signature, although this feature requires a deeper integration with NumPy typing modules, which we’ll explore in subsequent examples.

Example 3: Using NumPy Typing Module

As NumPy and typing in Python have evolved, the NumPy library now includes a typing module that offers more granular control over array types. Let’s look at an example of how to use it:

import numpy as np
from numpy.typing import NDArray, DTypeLike

def array_stats(arr: NDArray[DTypeLike]) -> Tuple[float, float]:
    mean = np.mean(arr)
    std = np.std(arr)
    return mean, std

In this example, we use NDArray along with DTypeLike to specify that arr should be a numpy array of any data type. Running ‘mypy’ on this will help ensure the input adheres to the specified type constraints.

Example 4: Complex Type Assertions

For our final example, we delve into more complex scenarios, integrating type checks that encompass functions accepting multiple NumPy array types and shapes:

import numpy as np
from numpy.typing import NDArray, Shape
from typing import Any

def multi_array_operations(arr1: NDArray[Any], arr2: NDArray[Any], axis: int) -> NDArray[Any]:
    assert arr1.shape == arr2.shape, "Arrays must have the same shape."
    concatenated = np.concatenate((arr1, arr2), axis=axis)
    return concatenated

This function asserts that the two input arrays have the same shape before performing a concatenation operation along a specified axis. By employing NDArray with the Any type, we provide flexibility in the array’s data type while ensuring they are of compatible shapes.


Throughout this tutorial, we explored how to use ‘mypy’ for type checking in NumPy code, starting from basic examples and moving towards more complex scenarios. Integrating ‘mypy’ can significantly enhance code quality by catching type errors early in the development process. As demonstrated, ‘mypy’ and NumPy’s typing modules are powerful tools for ensuring type and shape correctness in your scientific and numerical Python projects.