Jan 16, 2014 · Since True and False are represented in Python as 1 and 0, respectively, we have only to specify this array should be boolean using the optional dtype parameter and we are done: numpy.ones((2, 2), dtype=bool) returns: array([[ True, True], [ True, True]], dtype=bool) UPDATE: 30 October 2013

----> 1 (foo < 40) or (foo > 60) ValueError: The truth value of an array with more than one element is ambiguous. Use a.any() or a.all() Is there a canonical way of doing element-wise Boolean operations on NumPy arrays with good looking code?

Jun 15, 2017 · a == b # array([False, False, False, True, True], dtype=bool) but the last item is True (understandably because both are False), whereas I would like the result array to be True only in the 4th element, something like: array([False, False, False, True, False], dtype=bool)

Jul 7, 2013 · 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 ...

These take only one byte per element and are proper arrays. When your array is created by a "logical array operation" like, say, b = (a > 0) it (b) will be automatically of bool type. You can obtain boolean arrays by the standard numpy ways a.astype(bool), array(..., dtype=bool) etc.

The reason x and y returns y and y and x returns x is because boolean operators in python return the last value checked that determines the true-ness of the expression. Non-empty list 's evaluate to True , and since and requires both operands to evaluate True , the last operand checked is the second operand.

TL; DR: use np.where as it is the fastest option. Your options are np.where, itertools.compress, and list comprehension.

Jul 29, 2016 · Is there an efficient Numpy mechanism to retrieve the integer indexes of locations in an array based on a condition is true as opposed to the Boolean mask array? For example: x=np.array([range(100,1,-1)]) #generate a mask to find all values that are a power of 2 mask=x&(x-1)==0 #This will tell me those values print x[mask]

I have a boolean mask array a of length n: a = np.array([True, True, True, False, False]) I have a 2d array with n columns: b = np.array([[1,2,3,4,5], [1,2,3,4,5]]) I want a new array which conta...

Sep 19, 2017 · @norio Regarding bool: boolean values are treated as 1 and 0 in arithmetic operations. See "Boolean Values" in the Python Standard Library documentation. Note that NumPy's bool and Python bool are not the same, but they are …