In this article, we will see how to build a numpy array from the generator.

Using generator functions we can iterate over an infinite sequence of values without storing them in a memory. They are particularly useful when dealing with large datasets.

Let’s see how to build a numpy array from a generator with an example.

Using the fromiter function from the Numpy, we can build an array from an iterable object. Following is an example:

import numpy as np

def data_generator():
    for i in range(10):
        yield i

generator = data_generator()
array = np.fromiter(generator, dtype=int, count=10)

print(array)

In the above example, the data_generator() function yields integers from 0 to 9 and then we created an instance of generator and pass it to np.fromiter(). This function takes three arguments i.e. iterable object, data type and no of elements to be consumed from the iterator.

After calling np.fromiter(), we get a NumPy aray which contains the values provided by the generator and then print the array which leads to following output.

<code>[0 1 2 3 4 5 6 7 8 9]

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