Handling masked arrays in NumPy when performing interpolation with NaN values
I'm reviewing some code and I keep running into This might be a silly question, but I'm trying to perform interpolation on a 1D NumPy array that contains NaN values using `numpy.ma` for masked arrays. My goal is to fill these NaNs using linear interpolation, but I'm working with issues where the output isn't as expected. I'm using NumPy version 1.24.1. Here’s the code I’ve written so far: ```python import numpy as np import numpy.ma as ma # Sample data with NaN values data = np.array([1, 2, np.nan, 4, 5, np.nan, 7]) # Create a masked array where NaNs are masked masked_data = ma.masked_array(data, np.isnan(data)) # Trying to interpolate the masked array interpolated_data = ma.interpolate(masked_data) print(interpolated_data) ``` However, when I run this code, I get the following behavior: ``` AttributeError: module 'numpy.ma' has no attribute 'interpolate' ``` It seems that `numpy.ma` does not have an `interpolate` method. I've tried checking the NumPy documentation, but I need to find a suitable method to accomplish this. I also looked at alternatives like `scipy.interpolate`, but I would prefer to stick with NumPy if possible. How can I achieve linear interpolation for masked arrays containing NaNs without running into this scenario? I'm developing on Ubuntu 22.04 with Python. Any ideas how to fix this? This is my first time working with Python 3.10. What would be the recommended way to handle this? Hoping someone can shed some light on this.