How to implement guide with dtype promotion in numpy when creating structured arrays with mixed types
I'm having a hard time understanding I'm having trouble creating a structured NumPy array with mixed data types... Specifically, I'm trying to create an array that includes both integers and strings, but I'm running into unexpected behavior when it comes to the data types being promoted. Here's the code I'm using: ```python import numpy as np # Define a structured data type with integer and string fields dtype = np.dtype([('id', 'i4'), ('name', 'U10')]) # Create a structured array data = np.array([(1, 'Alice'), (2, 'Bob')], dtype=dtype) print(data) print(data.dtype) ``` When I run this code, it outputs: ``` [(1, 'Alice') (2, 'Bob')] (numpy.record,) ``` However, I expected the output to clearly represent the structure with both 'id' and 'name' fields, not as a record type. I also noticed that when I try to access the fields like `data['id']`, it returns a different view than I anticipated. I attempted to modify the dtype to explicitly define the structure as `np.dtype([('id', np.int32), ('name', 'U10')])`, but I still face the same scenario. I’ve checked the NumPy documentation and searched through related posts, but I can’t seem to find a solution. Is there something I’m missing in how structured arrays work, or is there a specific way I should define the dtype to avoid this promotion scenario? I'm using NumPy version 1.24.0. Any insights would be greatly appreciated! I'm working in a Debian environment. Any help would be greatly appreciated!