CodeWhizz

Start by typing a goal in plain language, such as “clean this CSV,” “call an API,” or “build a quick web scraper.” The workspace turns that request into a working Python draft you can adjust immediately in the editor. When you need to change inputs, refactor functions, or add error handling, iterate in place and re-run until the output matches what you expect.

During troubleshooting, paste the failing code or the error message and ask for a fix. The assistant points to likely causes, suggests edits, and helps you verify the result by executing the script again. This is useful for common tasks like fixing imports, resolving type issues, handling missing keys, or correcting logic bugs that only show up at runtime.

For data work, you can run analysis steps as you go—load a dataset, transform columns, calculate summaries, and check results right away. If your script produces a chart, generate the plot during execution so you can confirm trends and formatting without leaving the environment. That tight loop helps when you’re tuning a visualization, comparing parameters, or validating assumptions.

Learning workflows fit naturally into the same process. Ask why a solution works, request a simpler example, or have it explain a concept using your current file. You can practice by modifying the script, re-running it, and getting feedback on style, performance, or best practices. more