Member-only story
No cloud sandbox provider, and no per-minute billing. #
Read for free link
Most of the chatbots we use daily (such as ChatGPT, Claude, Gemini, and Qwen) allow us to upload files, like images, PDFs, and spreadsheets, which they can ingest and analyze to answer our queries.
Many of these requests can be handled solely by the Large Language Model (LLM)’s native capabilities, such as describing an image, summarizing a PDF, or extracting specific text from a document.
However, if you provide a chatbot with an Excel sheet or a .csv
file and ask it to produce a comprehensive statistical analysis report with charts, the task requires more than just basic reasoning.
Such complex requests demand active data manipulation, statistical calculations, and visual chart generation.
While an LLM cannot perform these mathematical and visual tasks natively, it is perfectly capable of writing a Python script that utilizes essential data science libraries like pandas, NumPy, and Matplotlib, to do the heavy lifting.