Handling large datasets with NumPy arrays for real-time API responses
I've been working on this all day and I'm stuck on something that should probably be simple... I tried several approaches but none seem to work. Currently developing a research-focused API that processes extensive datasets in real time. The project leverages NumPy for numerical computations, but I’m concerned about performance with large arrays. For instance, when handling an array of shape `(100000, 10)`, I noticed significant delays when executing element-wise operations. I’ve tried using `np.vectorize()` to enhance performance, but the speed increase was negligible. Here’s a snippet of what I currently have: ```python import numpy as np # Sample data data = np.random.rand(100000, 10) # Applying a function using np.vectorize def custom_function(x): return x ** 2 + 2 * x + 1 vectorized_function = np.vectorize(custom_function) result = vectorized_function(data) ``` While this works, it doesn’t seem efficient for large datasets. I also read about using `np.frompyfunc()` but am unsure how it compares in terms of execution time. For optimization, I’ve considered utilizing NumPy’s built-in operations instead of applying a custom function. For example, I could refactor the code like this: ```python # Optimized computation using NumPy's built-in capabilities result_optimized = data ** 2 + 2 * data + 1 ``` This seems to yield better performance, but I’m still uncertain about how to best manage memory consumption as the dataset scales even larger. Any insights on additional optimizations or best practices when using NumPy in a performance-critical API context would be greatly appreciated! This is part of a larger web app I'm building. Am I missing something obvious? This is part of a larger service I'm building. The stack includes Python and several other technologies. Hoping someone can shed some light on this.