numpy.all() in Python
The numpy.all() function tests whether all array elements along the mentioned axis evaluate to True.
Syntax: numpy.all(array, axis = None, out = None, keepdims = class numpy._globals._NoValue at 0x40ba726c)
array :[array_like]Input array or object whose elements, we need to test. axis : [int or tuple of ints, optional]Axis along which array elements are evaluated. The default (axis = None) is to perform a logical AND over all the dimensions of the input array. Axis may be negative, in which case it counts from the last to the first axis. out : [ndarray, optional]Output array with same dimensions as Input array, placed with result keepdmis : [boolean, optional]If this is set to True, the axes which are reduced are left in the result as dimensions with size one. With this option, the result will broadcast correctly against the input array. If the default value is passed, then keepdims will not be passed through to the all method of sub-classes of ndarray, however any non-default value will be. If the sub-classes sum method does not implement keepdims any exceptions will be raised.
A new Boolean array as per 'out' parameter
Code 1 :
Bool Value with axis = NONE : False Bool Value with axis = 0 : [ True False] Bool : True Bool : True Bool Value : False
Code 2 :
Bool Value : [False False] Bool Value : [False False] VisibleDeprecationWarning: using a boolean instead of an integer will result in an error in the future return umr_all(a, axis, dtype, out, keepdims)
These codes won’t run on online-ID. Please run them on your systems to explore the working.
This article is contributed by Mohit Gupta_OMG 😀. If you like GeeksforGeeks and would like to contribute, you can also write an article using contribute.geeksforgeeks.org or mail your article to email@example.com. See your article appearing on the GeeksforGeeks main page and help other Geeks.
Please write comments if you find anything incorrect, or you want to share more information about the topic discussed above.
Attention geek! Strengthen your foundations with the Python Programming Foundation Course and learn the basics.
To begin with, your interview preparations Enhance your Data Structures concepts with the Python DS Course. And to begin with your Machine Learning Journey, join the Machine Learning – Basic Level Course