## Question

Asked By – ChrisFro

I have a large numpy array that I need to manipulate so that each element is changed to either a 1 or 0 if a condition is met (will be used as a pixel mask later). There are about 8 million elements in the array and my current method takes too long for the reduction pipeline:

```
for (y,x), value in numpy.ndenumerate(mask_data):
if mask_data[y,x]<3: #Good Pixel
mask_data[y,x]=1
elif mask_data[y,x]>3: #Bad Pixel
mask_data[y,x]=0
```

Is there a numpy function that would speed this up?

**Now we will see solution for issue: Replacing Numpy elements if condition is met **

## Answer

```
>>> import numpy as np
>>> a = np.random.randint(0, 5, size=(5, 4))
>>> a
array([[4, 2, 1, 1],
[3, 0, 1, 2],
[2, 0, 1, 1],
[4, 0, 2, 3],
[0, 0, 0, 2]])
>>> b = a < 3
>>> b
array([[False, True, True, True],
[False, True, True, True],
[ True, True, True, True],
[False, True, True, False],
[ True, True, True, True]], dtype=bool)
>>>
>>> c = b.astype(int)
>>> c
array([[0, 1, 1, 1],
[0, 1, 1, 1],
[1, 1, 1, 1],
[0, 1, 1, 0],
[1, 1, 1, 1]])
```

You can shorten this with:

```
>>> c = (a < 3).astype(int)
```

This question is answered By – Steve Barnes

**This answer is collected from stackoverflow and reviewed by FixPython community admins, is licensed under cc by-sa 2.5 , cc by-sa 3.0 and cc by-sa 4.0 **