👀 Views: 21 💬 Answers: 1 📅 Created: 2025-06-10

numpy dtype concatenate Python

I'm stuck on something that should probably be simple. I'm relatively new to this, so bear with me... I am working with an scenario with `np.concatenate` when trying to merge two NumPy arrays containing mixed dtypes. I'm working with NumPy version 1.24.0. The first array is of `dtype=int` and the second array is of `dtype=float`. Here’s the code I’ve written: ```python import numpy as np a1 = np.array([1, 2, 3], dtype=int) a2 = np.array([4.0, 5.0, 6.0], dtype=float) result = np.concatenate((a1, a2)) print(result) ``` I expected the output to be a single array of floats, like `array([1.0, 2.0, 3.0, 4.0, 5.0, 6.0])`, but instead, I am receiving the output as `array([1., 2., 3., 4., 5., 6.])` without any behavior, which is actually what I wanted. However, I then tried to perform further operations that expect integers on this resulting array, and I received the behavior: ``` TypeError: need to convert float NaN to integer ``` When I try to use `np.nan_to_num` on the result, it doesn't seem to behave as expected because the function seems to ignore the conversion when the base dtype is still mixed. What is the best way to handle this situation to ensure that I can concatenate arrays of mixed dtypes without running into errors during further operations? Ideally, I would like to retain the integer values as integers and only convert the final result when necessary. Any suggestions or best practices would be appreciated! Any help would be greatly appreciated! Is there a simpler solution I'm overlooking?