I use Scilab, and want to convert an array of booleans into an array of integers:
>>> x = np.array([4, 3, 2, 1])
>>> y = 2 >= x
>>> y
array([False, False, True, True], dtype=bool)
In Scilab I can use:
>>> bool2s(y)
0. 0. 1. 1.
or even just multiply it by 1:
>>> 1*y
0. 0. 1. 1.
Is there a simple command for this in Python, or would I have to use a loop?
Numpy arrays have an astype method. Just do y.astype(int).
Note that it might not even be necessary to do this, depending on what you're using the array for. Bool will be autopromoted to int in many cases, so you can add it to int arrays without having to explicitly convert it:
>>> x
array([ True, False, True], dtype=bool)
>>> x + [1, 2, 3]
array([2, 2, 4])
The 1*y method works in Numpy too:
>>> import numpy as np
>>> x = np.array([4, 3, 2, 1])
>>> y = 2 >= x
>>> y
array([False, False, True, True], dtype=bool)
>>> 1*y # Method 1
array([0, 0, 1, 1])
>>> y.astype(int) # Method 2
array([0, 0, 1, 1])
If you are asking for a way to convert Python lists from Boolean to int, you can use map to do it:
>>> testList = [False, False, True, True]
>>> map(lambda x: 1 if x else 0, testList)
[0, 0, 1, 1]
>>> map(int, testList)
[0, 0, 1, 1]
Or using list comprehensions:
>>> testList
[False, False, True, True]
>>> [int(elem) for elem in testList]
[0, 0, 1, 1]
y = 1 if x else 0 is the same as y = 1 if x>0 else 0 and the same as if x: y = 1 ""NEXT LINE"" else: y = 0....how did you learn those tricks, i didn't see it in the if statement documentation?
y=1 if x else 0 is not the same as y=1 if x>0 else 0, since the latter doesn't take the negative numbers into consideration. This is just what Python defines as True or False, these are all in the documentation.
Using numpy, you can do:
y = x.astype(int)
If you were using a non-numpy array, you could use a list comprehension:
y = [int(val) for val in x]
Most of the time you don't need conversion:
>>>array([True,True,False,False]) + array([1,2,3,4])
array([2, 3, 3, 4])
The right way to do it is:
yourArray.astype(int)
or
yourArray.astype(float)
A funny way to do this is
>>> np.array([True, False, False]) + 0
np.array([1, 0, 0])
I know you asked for non-looping solutions, but the only solutions I can come up with probably loop internally anyway:
map(int,y)
or:
[i*1 for i in y]
or:
import numpy
y=numpy.array(y)
y*1
