In Python data visualization, how do I profile bottlenecks?

Profiling bottlenecks in Python data visualization helps identify parts of the code that are slow or inefficient, allowing for optimization and improved performance in generating visualizations.
Python, data visualization, profiling, bottlenecks, performance, optimization
import cProfile import pstats import io import matplotlib.pyplot as plt # Example function for data visualization def create_plot(): x = range(100000) y = [i**2 for i in x] plt.plot(x, y) plt.title('Example Data Visualization') plt.xlabel('X-axis') plt.ylabel('Y-axis') plt.show() # Profiling the function def profile_plot(): pr = cProfile.Profile() pr.enable() create_plot() pr.disable() s = io.StringIO() sortby = pstats.SortKey.CUMULATIVE ps = pstats.Stats(pr, stream=s).sort_stats(sortby) ps.print_stats() print(s.getvalue()) profile_plot()

Python data visualization profiling bottlenecks performance optimization

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