Convert NumPy array to 0 or 1 based on threshold

Question

I have an array below:

a=np.array([0.1, 0.2, 0.3, 0.7, 0.8, 0.9])

What I want is to convert this vector to a binary vector based on a threshold. take threshold=0.5 as an example, element that greater than 0.5 convert to 1, otherwise 0.
The output vector should like this:

a_output = [0, 0, 0, 1, 1, 1]

How can I do this?

Answer 1

np.where

np.where(a > 0.5, 1, 0)
# array([0, 0, 0, 1, 1, 1])

---

Boolean basking with astype

(a > .5).astype(int)
# array([0, 0, 0, 1, 1, 1])

---

np.select

np.select([a <= .5, a>.5], [np.zeros_like(a), np.ones_like(a)])
# array([ 0.,  0.,  0.,  1.,  1.,  1.])

---

Special case: np.round

This is the best solution if your array values are floating values between 0 and 1 and your threshold is 0.5.

a.round()
# array([0., 0., 0., 1., 1., 1.])

Answer 2

You could use binarize from the sklearn.preprocessing module .

However this will work only if you want your final values to be binary ie '0' or '1'. The answers provided above are great of non-binary results as well.

from sklearn.preprocessing import binarize

a = np.array([0.1, 0.2, 0.3, 0.7, 0.8, 0.9]).reshape(1,-1)
x = binarize(a) 
a_output = np.ravel(x)
print(a_output) 

#everything together 
a_output = np.ravel(binarize(a.reshape(1,-1), 0.5))