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 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 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 Implementation
import numpy
N, M = map(int, input().split())
A = numpy.array([input().split() for _ in range(N)], int)
print(numpy.max(numpy.min(A, axis=1), axis=0))