Let’s see how to getting the row numbers of a numpy array that have at least one item is larger than a specified value X. So, for doing this task we will use numpy.where() and numpy.any() functions together.
Syntax: numpy.where(condition[, x, y])
Return: [ndarray or tuple of ndarrays] If both x and y are specified, the output array contains elements of x where condition is True, and elements from y elsewhere.
Syntax: numpy.any(a, axis = None, out = None, keepdims = class numpy._globals._NoValue at 0x40ba726c)
Return: [ndarray, optional]Output array with same dimensions as Input array, Placed with result
Example :
Arr = [[1,2,3,4,5],
[10,-3,30,4,5],
[3,2,5,-4,5],
[9,7,3,6,5]]
and X = 6 then output is [ 0, 2 ].
Here,
[[1,2,3,4,5],
no element is greater than 6 so output is [0].
[10,-3,30,4,5],
10 is greater than 6 so output is [0].
[3,2,5,-4,5],
no element is greater than 6 so output is [0, 2].
[4,7,3,6,5]]
7 is greater than 6 so output is [0, 2].
Below is the implementation:
Python3
# importing library import numpy # create numpy array arr = numpy.array([[1, 2, 3, 4, 5], [10, -3, 30, 4, 5], [3, 2, 5, -4, 5], [9, 7, 3, 6, 5] ]) # declare specified value X = 6 # view array print("Given Array:\n", arr) # finding out the row numbers output = numpy.where(numpy.any(arr > X, axis = 1)) # view output print("Result:\n", output) |
Output:
Given Array: [[ 1 2 3 4 5] [10 -3 30 4 5] [ 3 2 5 -4 5] [ 9 7 3 6 5]] Result: (array([1, 3], dtype=int64),)
