Unexpected NaN Values When Calculating Moving Average with NumPy
I'm wondering if anyone has experience with I'm trying to compute a moving average from a NumPy array, but I'm encountering unexpected NaN values in the output. I'm using NumPy version 1.24.0 and have implemented the moving average computation using a simple convolution method with a uniform kernel. Here's the code snippet I've written: ```python import numpy as np # Sample data data = np.array([1, 2, 3, 4, 5]) # Kernel for moving average, size 3 kernel = np.ones(3) / 3 # Convolution to calculate moving average moving_avg = np.convolve(data, kernel, mode='valid') print(moving_avg) ``` Initially, I expected the output to contain the moving averages of the last three elements, but instead, I am getting unexpected NaN values, especially when I try with larger arrays. For example, when I run the same code with an array of 100 elements (ranging from 1 to 100), I start to see NaN values appearing at the end of the output. I suspect it might be related to how the convolution is handling the edges of the array. I've tried using different values for the `mode` parameter in `np.convolve`, but it didn’t seem to resolve the issue. Here's what I tried: - Using `mode='same'` to maintain the original array length, but that didn't give me a clean moving average as it included the edge effects. - Adding padding to the original data before the convolution, which somewhat helped but didn't seem elegant. Is there a better approach to calculate the moving average using NumPy that avoids these NaN values while keeping the implementation clean? I'd love to hear your thoughts on this.