What is a Generator Function and the Use of the Generator Function to Create a NumPy Array?

You may use generator functions to declare a function that works like an iterator. To generate a NumPy array from a generator, we may use the numpy.fromiter() method.

Well, this is only a quick fix. Let's delve into the intricacies of Generator Functions by taking you to step by step from the fundamentals to intermediate topics. Finally, we can generate a NumPy array using the generator function, which serves as a doorway to the practical implementation of using generator functions to create NumPy arrays.

Python Generators

A generator is just a function or expression that has been customized to operate as an iterator. It signifies that Generator has iterator properties, such as __iter__ and __next__ methods for a class.

Using Python, Create Generators

In Python, creating a generator is quite straightforward. It's the same as defining a regular function, but you use a yield statement rather than a return statement.

A function becomes a generator function if at least one yield statement is present (additional yield or return statements may also be included). Return and yield both return some value from a function.

The distinction is that a return statement closes a function completely, but a yield statement freezes the function while storing all of its states and then proceeds from there on subsequent calls. Consider the following example:

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Python Generator Expression

Using generator expressions, simple generators may be quickly generated on the spot. It simplifies the construction of generators.

Generator expressions generate anonymous generator functions in the same way that lambda functions do.

Use of the Generator Function to Create a Numpy Array

NumPy is a library designed for numerical and dimensional array calculation, one of its most common applications is to construct dimensional arrays based on given rules. Let's have a look at some of the methods for generating NumPy arrays with the help of a Python generator.

Before proceeding, let us first build a generator object, which we will use later in the article to convert it to a NumPy array.

Code:

Let's get started with the generator object to generate a NumPy array. Let us begin with something easy.

Approach 1

Unlike Python lists, NumPy arrays demand their length to be explicitly defined at compile time. This is required so that memory space may be provided sequentially to each object. The primary characteristic of numpy arrays is sequential allocation, which, when paired with native code implementation, allows operations on them to run significantly faster than operations on conventional lists. Keeping this in mind, let's get started on our first approach.

It is theoretically impossible to convert a generator object to a NumPy array until you either :

1. Can anticipate the number of items it will produce when run:

In our example, we are perfectly aware that the generator object will generate 10 separate elements, resulting in the production of 10 items. We may use the following code to convert a generator object to a NumPy array because we know the number of elements.

Code:

Output:

2. Are ready to keep its constituents in an interim list object:

You are aware that the number of items you are creating with the generator object is not limitless, and your list can easily accommodate that number of items in memory; thus, we may use the following code.

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Output:

3. May create two identical generators, run the first one to determine overall length, create the array, and then run the generator again to determine each element:

Let me illustrate this with an example.

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Output:

The first of the three methods stated before is usually what you're looking for when you're well aware of the fact; the amount of items that the generator object may create, the second is space inefficient, and the third is time inefficient since you have to go through the generator repeatedly.

Approach 2

We will look at a more pythonic technique to generate a NumPy array using the generator object in this approach. We will use the numpy.fromiter() function to iterate over the generator object and thus construct a NumPy array. Let's have a look at its syntax first.

Syntax:

Parameter:

Let us look at an example.:

Code:

Output:

When it comes to creating a Numpy array from a generator object, this technique is preferred over the previous one since the second approach eliminates all of the limitations of the first approach.