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Calculate the difference between the maximum and the minimum values of a given NumPy array along the second axis
• Last Updated : 02 Sep, 2020

Let’s see how to calculate the difference between the maximum and the minimum values of a given NumPy array along the second axis. Here, the Second axis means row-wise.

So firstly for finding the row-wise maximum and minimum elements in a NumPy array we are using numpy.amax() and numpy.amin() functions of NumPy library respectively. then After that we simply perform subtraction on it.

numpy.amax(): This function returns maximum of an array or maximum along axis(if mentioned).

Syntax: numpy.amax(arr, axis = None, out = None, keepdims = )

numpy.amin(): This function returns minimum of an array or minimum along axis(if mentioned).

Syntax: numpy.amin(arr, axis = None, out = None, keepdims = )

Now, Let’s see an example:

Example 1:

## Python3

 `# import library``import` `numpy as np`` ` `# create a numpy 2d-array``x ``=` `np.array([[``100``, ``20``, ``305``],``             ``[ ``200``, ``40``, ``300``]])`` ` `print``(``"given array:\n"``, x)`` ` `# get maximum element row``# wise from numpy array``max1 ``=` `np.amax(x ,``1``)`` ` `# get minimum element row``# wise from numpy array``min1 ``=` `np.amin(x, ``1``)`` ` `# print the row-wise max ``# and min difference``print``(``"difference:\n"``, max1 ``-` `min1)`

Output:

```given array:
[[100  20 305]
[200  40 300]]
difference:
[285 260]```

Example 2:

## Python3

 `# import library``import` `numpy as np`` ` `# list``x ``=` `[``12``, ``13``, ``14``, ``15``, ``16``]``y ``=` `[``17``, ``18``, ``19``, ``20``, ``21``]`` ` `# create a numpy 2d-array``array ``=` `np.array([x, y]).reshape((``2``, ``5``))`` ` `print``(``"original array:\n"``, array)`` ` `# find max and min elements``# row-wise``max1, min1 ``=` `np.amax(array, ``1``), np.amin(array,``1``)`` ` `# print the row-wise max ``# and min difference``print``(``"Difference:\n"``, max1 ``-` `min1)`

Output:

```original array:
[[12 13 14 15 16]
[17 18 19 20 21]]
Difference:
[4 4]```

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