numpy.logical_and() in Python
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
import numpy as np
arr1 = [ 1 , 3 , False , 4 ]
arr2 = [ 3 , 0 , True , False ]
out_arr = np.logical_and(arr1, arr2)
print ( "Output Array : " , out_arr)
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Output :
Output Array : [ True False False False]
Code 2 : Value Error if input array’s have different shapes
import numpy as np
arr1 = [ 8 , 2 , False , 4 ]
arr2 = [ 3 , 0 , True , False , 8 ]
out_arr = np.logical_and(arr1, arr2)
print ( "Output Array : " , out_arr)
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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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Last Updated :
29 Nov, 2018
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