NumPy: Using ‘DtypeLike’ type hint to annotate variables (4 examples)

Introduction

In the realm of scientific computing within Python, NumPy stands out as a foundational package that provides efficient operations for manipulating large arrays and matrices of numeric data. With the advent of type hints in Python, developers can now write clearer and more maintainable code, especially when dealing with complex numerical computations. One such enhancement that NumPy offers to this end is the DtypeLike type hint. This tutorial explores how to use DtypeLike to annotate variables, enhancing code readability, maintainability, and reducing potential for errors.

Pre-requisites: Familiarity with Python and basics of NumPy.

Understanding DtypeLike Type Hint

The DtypeLike type hint in NumPy is a way to annotate variables that are expected to be compatible with NumPy dtype specifications. This includes standard Python types that NumPy can convert to dtypes (e.g., int, float, str), scalar types, array scalar types, and dtypes themselves.

Basic Example: Annotating Scalars

import numpy as np
from numpy.typing import DtypeLike

age: DtypeLike = 32
height: DtypeLike = 5.6
name: DtypeLike = "Alex"

# Using variables in a NumPy array
array = np.array([age, height, name])
print(array)

This shows how scalars of different types can be annotated as DtypeLike, allowing them to be efficiently used together in NumPy arrays. The printed output would be: [’32’ ‘5.6’ ‘Alex’]

This demonstrates the versatility of DtypeLike in handling a variety of data types cohesively.

Annotating Array Elements and Custom Structures

import numpy as np
from numpy.typing import DtypeLike

class Student:
    age: DtypeLike
    height: DtypeLike
    scores: np.ndarray[DtypeLike]

    def __init__(self, age: int, height: float, scores: list):
        self.age = age
        self.height = height
        self.scores = np.array(scores, dtype=float)

# Instance with mixed types
stu = Student(20, 5.8, [95, 85, 92])
print(f"Age: {stu.age}, Height: {stu.height}, Scores: {stu.scores}")

This example extends the use of DtypeLike to a class that combines scalar types and arrays. It showcases the capability to not only annotate primitive types but also numpy arrays with elements of a type that is valid under DtypeLike. The output for this code block is: Age: 20, Height: 5.8, Scores: [95. 85. 92.]

Annotating Function Return Types

import numpy as np
from numpy.typing import DtypeLike

def calculate_average(scores: np.ndarray[DtypeLike]) -> DtypeLike:
    return np.mean(scores)

# Using the function
scores = np.array([88, 92, 85, 98, 75])
average = calculate_average(scores)
print(f"The average score is: {average}")

This code example demonstrates how DtypeLike can be used to annotate return types in functions, ensuring that the return type is valid for NumPy computations. The output would be: The average score is: 87.6

This strengthens the function’s documentation and assists developers and tools in understanding the expected type.

Advanced Usage: Alongside Generics

import numpy as np
from numpy.typing import DtypeLike

T = TypeVar('T', bound='np.ndarray')

def scale_array(array: T, factor: DtypeLike) -> T:
    assert isinstance(array, np.ndarray), "Expected NumPy array"
    return array * factor

# Scaling an integer array with a float factor
int_array = np.array([1, 2, 3])
scaled_array = scale_array(int_array, 2.5)
print(scaled_array)

This example combines the use of DtypeLike with Python generics to create function annotations that are both flexible and informative. This approach allows specifying that the function takes a NumPy array of any dtype and returns an array of the same type. The example output clearly shows the flexibility and power of using DtypeLike in more complex scenarios: [ 2.5 5. 7.5]

Conclusion

Throughout this tutorial, we explored how to use DtypeLike to enhance clarity and maintenance of numerical code in Python. Through a spectrum of examples, from basic to advanced, it’s clear that DtypeLike offers a significant benefit in terms of code readability and type safety. As Python and NumPy continue to evolve, taking advantage of such features is crucial for developing efficient and error-free numerical applications.