Analyze Data Like A Python Pro 0 like February 14, 2025Share this postAuthor(s): Katlego Thobye Originally published on Towards AI. Create a Cheat Sheet and Stop Googling the DocsThis member-only story is on us. Upgrade to access all of Medium.Photo by Mathew Schwartz on UnsplashI remember drowning in online searches for the first few months of my data science journey. Every task, no matter how small, required an endless cycle of searching Stack Overflow, poring over Pandas documentation, and frantically flipping between Matplotlib examples. My code was a Frankensteinian monster of copy-pasted snippets, barely held together with duct tape and hope. I spent more time debugging syntax errors and wrestling with data types than analyzing anything. From struggling to determine the difference between df.loc() and df. iloc(), and the vast assortment of filling methods (method=ffill) these commands swam before my eyes, a jumbled mess of possibilities I could never quite grasp.One particularly frustrating day, I spent hours trying to create a simple scatter plot with different colors based on a categorical variable. I jumped between Matplotlibs documentation, Seaborn tutorials, and countless blog posts, each offering a slightly different (and often conflicting) approach. That's when it hit me: I needed a consolidated resourcemy own data science cheat sheet that captured the essential Pandas, NumPy, and Matplotlib commands I used most frequently, along with clear examples and explanations.I started Read the full blog for free on Medium.Join thousands of data leaders on the AI newsletter. Join over 80,000 subscribers and keep up to date with the latest developments in AI. From research to projects and ideas. If you are building an AI startup, an AI-related product, or a service, we invite you to consider becoming asponsor. Published via Towards AITowards AI - Medium Share this post