Handling mixed data types in NumPy arrays when using np.where
Quick question that's been bugging me - I'm stuck on something that should probably be simple..... I'm working with an scenario while trying to use `np.where()` to conditionally select elements from a NumPy array containing mixed data types (strings and integers). My goal is to replace all occurrences of a specific integer with a string label in the array, but I'm getting unexpected results. Here's the code I've written: ```python import numpy as np # Create a mixed type NumPy array arr = np.array([1, 2, 'apple', 4, 'banana', 2], dtype=object) # I want to replace all occurrences of '2' with 'two' result = np.where(arr == 2, 'two', arr) print(result) ``` When I run this code, I get the following output: ``` ['two' 'two' 'apple' '4' 'banana' 'two'] ``` Instead of replacing just the integers, it seems like the operation is treating the entire array as strings after the replacement. I expected the output to still include the original integers and other strings, but now all the elements appear mixed. Is there a way to achieve the replacement correctly while preserving the original data types of the other elements? I’ve read about using different data types in NumPy arrays, but I thought using `dtype=object` would allow me to handle mixed types correctly. Any suggestions? Thanks! My development environment is macOS. Thanks in advance! I've been using Python for about a year now. What are your experiences with this?