In Python data analysis, how do I optimize performance?

In Python data analysis, optimizing performance is crucial for handling large datasets efficiently. This can involve various techniques such as using optimized libraries, memory management, vectorized operations, and parallel processing.
Python, data analysis, performance optimization, libraries, memory management, vectorized operations, parallel processing
# Example of optimizing performance in Python data analysis import pandas as pd import numpy as np # Create a large DataFrame n = 10**6 df = pd.DataFrame({ 'A': np.random.rand(n), 'B': np.random.rand(n) }) # Using vectorized operations for performance df['C'] = df['A'] + df['B'] # Using built-in functions for fast aggregation result = df['C'].mean() print(result)

Python data analysis performance optimization libraries memory management vectorized operations parallel processing

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