Programming for Data Analysts Cheat Sheet - 4 Pages Pandas | Numpy | Matplotlib | Seaborn

Why is it helpful?

Python for Data Analysis: Data Analyst Programming Guide is a concise, four-page guide that provides a comprehensive overview of Python's major data analysis libraries: Pandas, Numpy, Matplotlib and Seaborn. It's designed to be a practical resource for beginners and seasoned professionals seeking to refine their skills.

What you will get:

• Page one introduces Pandas, a powerful data manipulation library, where you'll learn about Series and DataFrame structures, data cleaning, filtering, and exploration methods, including descriptive statistics and pivot tables.
• Page two blends data manipulation with visualization, covering pandas' plotting capabilities and NumPy, known for high-performance arrays and a broad range of mathematical functions, including linear algebra operations.
• Page three dives into Matplotlib, enabling you to create various types of plots, and how to customize them for aesthetic appeal and clarity. This page also introduces Seaborn, a library that eases the creation of complex statistical visualizations.
• Finally, page four delves deeper into Seaborn, exploring its categorical and relationship plots and extensive options for plot customization and styling.