675: Pandas for Data Analysis and Visualization
Super Data Science: ML & AI Podcast with Jon Krohn
Exploring Data Visualization Libraries and Software Engineering in Data Science
This chapter delves into the benefits of object-oriented programming for simplifying adding shapes, the importance of modular and reusable code for visualizations, and the transition from pandas to matplotlib and Seaborn for more advanced plotting tasks. The discussion covers the differences between pandas, Matplotlib, and Seaborn in data visualization, emphasizing the importance of statistical approaches beyond summary statistics for valuable insights. Additionally, it touches on open-source tools in data science, contributing to libraries like pandas and Seaborn, and the satisfaction software engineers derive from giving back to the open-source community.
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