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473185670
473185670

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

#ai #data #python #tools

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.

Example

Input:

Group sales by month, calculate total revenue and average order size
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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()
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Note it auto-handled the datetime conversion — easy to miss on first write, then 10 minutes of debugging.

How it works

Three pieces, no black magic:

  1. 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.

  2. Schema-aware: upload a CSV or describe columns, and it knows df['date'] is datetime, df['user_id'] is string. No placeholder columns.

  3. 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).

More examples

7-day rolling average:

df['rolling_avg'] = df['close'].rolling(window=7).mean()
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Quartile bins:

df['income_quartile'] = pd.qcut(df['income'], q=4, labels=['Q1','Q2','Q3','Q4'])
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Correlation heatmap:

import seaborn as sns
sns.heatmap(df.corr(numeric_only=True), annot=True, cmap='coolwarm')
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What it is not

  • Not a black-box analyst. It generates code; you read and verify it.
  • It does not know your data. 'revenue' in cents or yuan? null = N/A or zero? Domain knowledge stays yours.
  • Not for 10-step exploratory pipelines (yet). 2-3 step combos work well.

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.

Try it

Free tier: 5 queries/day per IP, no signup.

→ PandasAI demo

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.

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