Issues resizing a NumPy array while maintaining data types and memory layout
I'm maintaining legacy code that I'm working through a tutorial and This might be a silly question, but I'm trying to resize a NumPy array but I'm running into issues with maintaining the data type and memory layout, especially when my array has multiple dimensions... I'm using NumPy version 1.23.4 and I have the following code snippet: ```python import numpy as np # Initializing a 3D array arr = np.random.rand(4, 3, 2).astype(np.float32) print('Original array shape:', arr.shape) # Attempting to resize while trying to preserve data type resized_arr = np.resize(arr, (6, 4)) print('Resized array shape:', resized_arr.shape) print('Data type:', resized_arr.dtype) ``` The output of the above code shows that the resized array has a shape of (6, 4) but I'm concerned that the data type has changed from `float32` to `float64`. Additionally, I'm not sure if the elements are being filled appropriately, as it seems the data isn't preserved correctly. When I check the contents of `resized_arr`, I notice that it's filled with some unexpected values, which makes me think that resizing might be altering the underlying memory structure. I also tried using `arr.flatten()` before resizing but ran into issues with the dimensionality that I initially wanted to maintain. Here's that snippet: ```python flattened_arr = arr.flatten() resized_arr = np.resize(flattened_arr, (6, 4)) print('Resized flattened array shape:', resized_arr.shape) ``` This still produces a similar issue with the data type. I read that using `np.resize` can sometimes yield unexpected behavior, but I don't know how to effectively resize my original array while keeping the data type and structure intact. Is there a recommended approach to do this in a way that preserves the original data type and ensures my array elements are filled correctly? Any insights or suggestions for best practices would be greatly appreciated! I'm working on a API that needs to handle this. Any help would be greatly appreciated! I recently upgraded to Python 3.10. Any examples would be super helpful. Is there a simpler solution I'm overlooking?