Turn plain English into pandas code — with AST validation (free tool)

A developer has built a free tool that converts plain English descriptions into syntax-validated pandas code, using AST validation to catch errors and block dangerous operations. The tool includes 22 curated few-shot examples, schema awareness for CSV uploads, and a free tier of 5 queries per day per IP.

If you use pandas daily, you have probably burned minutes hunting for the right syntax. .agg takes a dict or a list? .rolling then .mean — what is the window arg called? I built a tool: describe what you want in English, get syntax-validated pandas code back. Input: Group sales by month, calculate total revenue and average order size Output: df 'month' = df 'date' .dt.to period 'M' result = df.groupby 'month' .agg total revenue= 'revenue', 'sum' , avg order size= 'order size', 'mean' .reset index Note it auto-handled the datetime conversion — easy to miss on first write, then 10 minutes of debugging. Three pieces, no black magic: Few-shot examples 22 curated patterns : groupby+agg, merge/join, datetime, string ops, missing values, pivot, viz, binning, filtering, chaining. Not a generic LLM wrapper — tuned for pandas. Schema-aware : upload a CSV or describe columns, and it knows df 'date' is datetime, df 'user id' is string. No placeholder columns. AST validation : runs ast.parse before returning. If the model hallucinates a nonexistent method, the validator flags it. You never get syntax-broken code — and it scans for dangerous ops eval , exec , subprocess , os.remove . 7-day rolling average: df 'rolling avg' = df 'close' .rolling window=7 .mean Quartile bins: df 'income quartile' = pd.qcut df 'income' , q=4, labels= 'Q1','Q2','Q3','Q4' Correlation heatmap: python import seaborn as sns sns.heatmap df.corr numeric only=True , annot=True, cmap='coolwarm' Honest value prop: saves the 20-30% of time spent on syntax lookup, so you spend it on the 70-80% that matters — understanding your data and reading results. Free tier: 5 queries/day per IP, no signup. Type a data operation in English, get validated pandas code. If you hit a pattern it handles well or badly , tell me in the comments — the edge cases on messy real-world data are what I care about most.