# Min and Max – Python HackerRank Solution

Min and Max Problem

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.

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 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

Code:

```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)))```

Disclaimer: This problem is originally created and published by HackerRank, we only provide solutions to this problem. Hence, doesn’t guarantee the truthfulness of the problem. This is only for information purposes.