In NumPy, a sliding window is *a way of selecting a subset of consecutive elements from an array, where the window “slides” over the array at a certain stride*. This is often useful for computing moving averages, smoothing noisy data, or performing convolution operations. To compute averages using a sliding window over an array in NumPy, you can use the following functions:

#### 1. Using “Convolution with a Uniform Window”:

One way to compute sliding window averages is to convolve the array with a uniform window, where each element of the window has the same weight. This function creates a uniform window of size `window_size`

using `np.ones()`

, normalizes it to sum to `1`

using division by `window_size`

, and then applies it to the array using `np.convolve()`

with `mode='valid'`

to ensure that the output has the same length as the input.

#### 2. Using “Convolution with a Gaussian Window”:

Another approach is to use a Gaussian window instead of a uniform window. A Gaussian window can be useful if *you want to give more weight to elements near the center of the window*. This function creates a Gaussian window of size `window_size`

with a standard deviation of `sigma`

using `np.exp()`

and `np.linspace()`

, normalizes it to sum to `1`

using division by `window.sum()`

, and then applies it to the array using `np.convolve()`

with `mode='valid'`

.

#### 3. Using “Rolling Window with Mean”:

A third approach is to use NumPy’s rolling function to apply a moving window to the array and then take the mean of each window. This function uses `np.lib.stride_tricks.sliding_window_view()`

to create a view of the input array with windows of size `window_size`

, and then takes the mean of each window along axis `1`

using `np.mean()`

.

**Example**:

Depending on your specific use case, you may want to experiment with different window shapes or smoothing functions to achieve the desired result.