implementing broadcasting in NumPy when stacking arrays of different dimensions
I've encountered a strange issue with I'm working on a project and hit a roadblock... I'm working with a question when trying to stack multiple 1D NumPy arrays of different lengths using `np.vstack`. The operation seems to be failing, and I'm not sure how to handle arrays that don't have the same size. Here's what I've tried: ```python import numpy as np a = np.array([1, 2, 3]) b = np.array([4, 5]) c = np.array([6, 7, 8, 9]) # Attempting to vertically stack arrays with differing lengths try: result = np.vstack((a, b, c)) except ValueError as e: print(f"behavior: {e}") ``` When I run this code, I get the behavior: ``` ValueError: all the input array dimensions for the concatenation axis must match exactly ``` I thought `np.vstack` would handle this by padding the shorter arrays with zeros or something similar, but it looks like I was mistaken. I need to create a 2D array where the shorter arrays are filled with NaNs or zeros to match the size of the longest array. Is there a way to achieve this without manually padding each array? Any best practices for working with arrays of different lengths in NumPy would be greatly appreciated! I'm using NumPy version 1.23.5. The stack includes Python and several other technologies. Any feedback is welcome!