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5 Python Best Practices for Data Science

Level up your Python skills for data science with these by following these best practices.
KDnuggets 5:53 pm on May 29, 2024

This guide explores Python best practices for data science tasks like defining Pydantic models and validating data with 'Data Validation in Python Made Simple'. It emphasizes profiling code to identify performance bottlenecks, using vectorized operations for efficient data processing, and comparing NumPy's execution time versus a manual implementation. The author is Bala Priya C, an enthusiast at the intersection of machine learning, programming, content creation, with expertise in various domains like natural language processing.

Define Pydantic Models: Implement and profile data validation using Pydantic for clean, robust code.
Vectorized Operations: Utilize NumPy's vectorized operations to enhance preprocessing speed and efficiency.
Performance Profiling: Use cProfile to identify and optimize performance bottlenecks in Python code.
Data Validation Best Practices: Learn how 'Data Validation Made Simple' guides creating accurate, maintainable data validation schemas.
Expert Insight: Gain knowledge from Bala Priya C, a seasoned professional with contributions to various domains of tech and data science.

https://www.kdnuggets.com/5-python-best-practices-for-data-science

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5 Python Best Practices for Data Science

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