Min and Max HackerRank Solution In Python

Problem

min

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

import numpymy_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 : 0print 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 numpymy_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 : 7print 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 NXM.
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 contains M space separated integers.

Output Format

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

Sample Input

4 22 53 71 34 0

Sample Output

3

Explanation

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

Solution – Min and Max In Python | HackerRank

import numpy as npn, m = list(map(int, input().split()))lst = np.array([list(map(int, input().split())) for _ in range(n)])b = np.min(lst, axis=1)print(np.max(b))