# Subtracting Row Means in NumPy

Subtracting the mean of each row of a matrix is a common data preprocessing technique used in various machine learning applications. This technique involves subtracting the mean of each row from each element of the corresponding row in a matrix. The resulting matrix will have a zero mean for each row, which can be useful for normalization or standardization purposes. Here are a few ways through which you can do this:

#### 1. Using "NumPy vectorization":

• The code first creates a 3x3 matrix called `arr` with random integer values between `0` and `9`.
• It then calculates the mean of each row using `np.mean()` with `axis=1` to sum over each row.
• After which, it subtracts the row means from each element in the row using NumPy vectorization by adding a new axis to `row_means` with `[:, np.newaxis]`.
• The resulting array is printed to the console.

#### 2. Using "np.apply_along_axis()" function:

• This code generates a 3x3 matrix of random integers between `0` and `10` using the NumPy `random.randint()` function.
• Then, it subtracts the mean of each row of the matrix using the `apply_along_axis()` function with a `lambda` function that calculates the mean of each row and subtracts it from the elements of that row.
• Finally, it prints the resulting matrix with the mean of each row subtracted.
• In terms of efficiency, the first method is likely to be more efficient because it uses NumPy’s built-in broadcasting to subtract the row means from each element in the array, which is a vectorized operation that can be performed quickly.

• The second method involves applying the `lambda` function to each row individually, which can be slower for larger arrays.

• However, the advantage of the second method is that it is more concise and potentially easier to read, especially for people who are familiar with the `apply_along_axis()` function.

• Additionally, the second method can be more flexible than the first method because it allows for more complex functions to be applied to each row, not just the mean subtraction.