Optimizing NumPy array operations for better performance in a staging environment
I can't seem to get I'm sure I'm missing something obvious here, but After trying multiple solutions online, I still can't figure this out. This might be a silly question, but Been tasked with optimizing a data processing pipeline in our staging environment where we use NumPy for various array manipulations... The goal is to reduce the processing time for large datasets significantly. Currently, I'm loading a CSV file into a NumPy array, performing some filtering, and then calculating statistics. Here's a simplified version of what I'm doing: ```python import numpy as np import pandas as pd # Load data into NumPy array data = pd.read_csv('data.csv').to_numpy() # Filter out rows based on some condition filtered_data = data[data[:, 0] > 10] # Calculate mean of the second column mean_value = np.mean(filtered_data[:, 1]) print(mean_value) ``` This approach works, but as the dataset grows, the performance degrades. I’ve noticed that the filtering step using boolean indexing is particularly slow. I’ve considered using NumPy's `np.where()` for filtering, but I’m unsure if it would yield better performance. I also tried using Dask to handle larger-than-memory arrays, but the overhead of setting it up seemed counterproductive for our current needs. I’m also curious about whether I could leverage NumPy’s built-in functions more effectively. Should I be using `np.vectorize()` for operations across my arrays? Additionally, I’ve read that utilizing structured arrays or even memory-mapped arrays can help, but I'm not clear on how those would fit into my workflow. Any recommendations on best practices for handling these scenarios while keeping performance in mind would be greatly appreciated. Also, if there are specific versions of NumPy or configurations that can speed things up, please share your insights. I'm working on a API that needs to handle this. Has anyone else encountered this? For context: I'm using Python on Ubuntu. Thanks in advance! I'm working on a application that needs to handle this. Is there a simpler solution I'm overlooking? The stack includes Python and several other technologies. Any pointers in the right direction?