numpy.logical_and(arr1, arr2, out=None, where = True, casting = ‘same_kind’, order = ‘K’, dtype = None, ufunc ‘logical_and’) : This is a logical function and it helps user to find out the truth value of arr1 AND arr2 element-wise. Both the arrays must be of same shape.
Parameters :
arr1 : [array_like]Input array.
arr2 : [array_like]Input array.out : [ndarray, optional]Output array with same dimensions as Input array, placed with result.
**kwargs : allows you to pass keyword variable length of argument to a function. It is used when we want to handle named argument in a function.
where : [array_like, optional]True value means to calculate the universal functions(ufunc) at that position, False value means to leave the value in the output alone.
Return :
An array with Boolean results of arr1 and arr2 element-wise(of the same shape).
Code 1 : Working
# Python program explaining # logical_and() function import numpy as np # input arr1 = [1, 3, False, 4] arr2 = [3, 0, True, False] # output out_arr = np.logical_and(arr1, arr2) print ("Output Array : ", out_arr) |
Output :
Output Array : [ True False False False]
Code 2 : Value Error if input array’s have different shapes
# Python program explaining # logical_and() function import numpy as np # input arr1 = [8, 2, False, 4] arr2 = [3, 0, True, False, 8] # output out_arr = np.logical_and(arr1, arr2) print ("Output Array : ", out_arr) |
Output :
ValueError:operands could not be broadcast together with shapes (4,) (5,)
References :
https://docs.scipy.org/doc/numpy-1.13.0/reference/generated/numpy.logical_and.html#numpy.logical_and
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