Efficiently Finding Duplicates in a Large NumPy Array - performance optimization on Large Datasets
I'm converting an old project and I'm wondering if anyone has experience with I'm wondering if anyone has experience with I just started working with I recently switched to I'm stuck on something that should probably be simple..... I'm currently working with a large NumPy array (shape: (1000000,)) and need to identify duplicate entries efficiently. I tried using `np.unique()` and `np.bincount()` but ran into performance optimization as my array grows larger. Here's the code snippet I used: ```python import numpy as np # Sample large array large_array = np.random.randint(0, 10000, size=(1000000,)) # Attempt to find duplicates using np.unique() unique_elements, counts = np.unique(large_array, return_counts=True) duplicates = unique_elements[counts > 1] ``` While this works for smaller datasets, I noticed it takes a important amount of time with larger arrays, and I received a `MemoryError` when scaling up to about 10 million entries. I also explored using a Python dictionary to count occurrences, but the performance wasn’t much better. Are there any better approaches or algorithms I can implement to find duplicates in this large NumPy array without running into performance bottlenecks? Any suggestions on optimizing memory usage or using different libraries would be greatly appreciated! For context: I'm using Python on Ubuntu. Am I missing something obvious? I'm working with Python in a Docker container on Ubuntu 20.04. Thanks for your help in advance! For context: I'm using Python on Windows 10. Any help would be greatly appreciated! The project is a desktop app built with Python. What's the best practice here?