While exploring NumPy functionalities, I encountered the concept of subtracting row means from each element in a matrix. While I grasp the idea, translating it into code is challenging. Could someone assist in providing concise and clear logic for this operation?

Sure @safiaa.02, here is an example code that uses the technique of `NumPy vectorization`

to achieve the task you explained:

- NumPy vectorization is a technique to perform mathematical operations on an array in a faster and more efficient way, by operating on the entire array rather than individual elements.
- In this example code, the mean of each row of a 3x3 matrix is calculated using
`np.mean()`

. Then, NumPy vectorization is used to subtract the row means from each element in the row by adding a new axis to`row_means`

with`[:, np.newaxis]`

.

I hope this code is useful and helps you understand the logic of this technique in code!

This code helps clarify logic concepts by generating a 3x3 matrix with random integers (0 to 10) using NumPy’s `random.randint()`

. It proceeds to subtract the row means via `apply_along_axis()`

with a lambda function for calculating and subtracting the mean from each row. Keep in mind that the output may vary on multiple runs due to the random matrix generation.

Note that this method is not very efficient in some cases but it is more concise and may be preferable for readability, particularly for those familiar with the `apply_along_axis()`

function.