Replacing Pandas with LLM Code Gen

Nobody memorizes matplotlib syntax. Stop trying. Here is how to use LLMs to wrangle tick data instantly.

The Workflow

Instead of passing massive CSVs directly to an LLM (which is slow, expensive, and risks exposing proprietary datasets), the efficient workflow is passing the schema and asking for the code.

// The input prompt I have a DataFrame `df` with columns: ['timestamp', 'sym', 'bid', 'ask', 'volume'] The index is datetime naive. Write a python script to: 1. Set index to timezone 'America/New_York' 2. Calculate a 5-minute rolling VWAP for 'AAPL' 3. Plot the price and VWAP using Plotly.

Local vs Cloud

For data wrangling, you don't need GPT-4. Local models like Llama-3 (via Ollama) are more than capable of writing standard pandas transformations, meaning you can integrate this directly into your Jupyter notebooks without API keys.

The Danger Zone

LLMs fail spectacularly at custom resampling logic. If you ask it to "resample to 1-minute bars but only during RTH (Regular Trading Hours)," it will almost always mess up the holiday calendar or handle early closes incorrectly. Rely on established libraries like `pandas_market_calendars` and explicitly tell the LLM to use them.