Stop Passing Raw DataFrames to Your LLM — Here's a Better Way

TL;DR df.to_string() on a 100K-row DataFrame = millions of tokens, guaranteed failure df.head() = 5 rows with zero statistical context, useless for real analysis dfcontext generates a token-budget-aware, column-type-aware summary — no LLM calls required The Problem Nobody Talks About You've got a DataFrame with 100,000 rows. You want to ask an LLM about it. What do you do? Most people try one of two things: # Option A: Dump everything (will blow the context window) prompt = df.to_string() # Option B: Just use head (loses almost all information) prompt = df.head().to_string() Option A will hit your token limit instantly. Option B gives the model five rows of data and basically asks it to guess the rest. There's no obvious middle ground in the standard pandas API — so I built one. Introducing dfcontext dfcontext generates a compact, statistically rich summary of your DataFrame that fits within a token budget you specify. It's pure data processing — zero LLM calls, works with any LLM prov