👀 Views: 25 💬 Answers: 1 📅 Created: 2025-06-11

numpy arrays data-types Python

This might be a silly question, but I'm working through a tutorial and I keep running into I'm testing a new approach and Quick question that's been bugging me - This might be a silly question, but I'm currently working with a 2D NumPy array and I need to reshape it into a 1D array while preserving the original data types of each element....... However, I keep running into issues when the original array contains mixed data types. My initial approach was to use the `reshape` method, but it seems to not preserve the types correctly when flattening. Here's what I've tried: ```python import numpy as np # Original 2D array with mixed types array_2d = np.array([[1, 2.5, 'apple'], [3, 4.0, 'banana']]) # Attempt to reshape array array_reshaped = array_2d.reshape(-1) print(array_reshaped) print(array_reshaped.dtype) # This shows 'object' type ``` This gives me an array where all elements are of type 'object', which isn't what I want. I need to ensure that when I flatten the array, the integers remain integers, floats remain floats, and strings remain strings. Is there a way to achieve this in NumPy without losing the type fidelity? Or should I be using a different approach altogether? I also considered using lists, but I would prefer to stay within NumPy for performance reasons. Any insights on best practices or alternative methods would be greatly appreciated! My development environment is Windows. I'd really appreciate any guidance on this. I'm on Windows 11 using the latest version of Python. What would be the recommended way to handle this? The stack includes Python and several other technologies. What would be the recommended way to handle this? Any examples would be super helpful. I'm working with Python in a Docker container on Debian. Any pointers in the right direction?