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]`