HackerRank Python Solution - Numpy Topic - Min and Max

Min:

The tool min returns the minimum value along a given axis.

import numpy

my_array = numpy.array([[2, 5], 
                        [3, 7],
                        [1, 3],
                        [4, 0]])

print numpy.min(my_array, axis = 0)         #Output : [1 0]
print numpy.min(my_array, axis = 1)         #Output : [2 3 1 0]
print numpy.min(my_array, axis = None)      #Output : 0
print numpy.min(my_array)                   #Output : 0

By default, the axis value is None. Therefore, it finds the minimum over all the dimensions of the input array.

Max:

The tool max returns the maximum value along a given axis.

import numpy

my_array = numpy.array([[2, 5], 
                        [3, 7],
                        [1, 3],
                        [4, 0]])

print numpy.max(my_array, axis = 0)         #Output : [4 7]
print numpy.max(my_array, axis = 1)         #Output : [5 7 3 4]
print numpy.max(my_array, axis = None)      #Output : 7
print numpy.max(my_array)                   #Output : 7

By default, the axis value is None. Therefore, it finds the maximum over all the dimensions of the input array.

Task:

You are given a 2-D array with dimensions N x M. Your task is to perform the min function over axis 1 and then find the max of that.

Input Format:

The first line of input contains the space-separated values of N and M. The next N lines contain M space-separated integers.

Output Format:

Compute the min along axis 1 and then print the max of that result.

Sample Input:

4 2
2 5
3 7
1 3
4 0

Sample Output:

 

3

Explanation:

The min along axis 1 = [2, 3, 1, 0] The max of [2, 3, 1, 0] = 3

Solution:

import numpy as np

N, M = map(int,input().split())

arr = np.array([input().split() for _ in range(N)],int)

print(np.max(np.min(arr,axis=1)))

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