Sourcetable gives you a complete data science environment inside your spreadsheet. Ask the AI to run analyses in plain English and it executes Python code with pandas, scikit-learn, StatsModels, and more — no installs, no notebooks, no configuration.Documentation Index
Fetch the complete documentation index at: https://sourcetable.com/docs/llms.txt
Use this file to discover all available pages before exploring further.
How it works
Prepare your data
Import data via file upload, connector, or paste. Sourcetable handles CSV, XLSX, Parquet, JSON, and 15+ other formats.
Ask the AI
Describe what you want in the chat: “Run a correlation analysis on columns A through F” or “Build a classification model to predict churn.”
Available libraries
| Library | Capabilities |
|---|---|
| pandas | Data manipulation, cleaning, aggregation, merging, time series |
| NumPy | Numerical computing, linear algebra, random sampling |
| SciPy | Statistical tests, optimization, signal processing, interpolation |
| StatsModels | Regression, ANOVA, time series (ARIMA/SARIMAX), hypothesis tests |
| scikit-learn | Classification, regression, clustering, dimensionality reduction, feature selection |
| TabPFN | Zero-shot predictions and classifications without training |
| plotly | Interactive charts and visualizations |
| matplotlib | Static plots, histograms, heatmaps |
| seaborn | Statistical visualizations, distribution plots, pair plots |
| bokeh | Interactive browser-based visualizations |
| transformers | NLP, text classification, sentiment analysis |
| BeautifulSoup | Web scraping and HTML parsing |
| SQLAlchemy | Database queries from Python |
What you can do
Exploratory data analysis
Profile datasets, find distributions, and surface patterns automatically.
Statistical analysis
Hypothesis tests, regression, ANOVA, and correlation analysis.
Machine learning
Classification, regression, clustering, and zero-shot predictions with TabPFN.
Forecasting
ARIMA, exponential smoothing, and ML-based time series forecasting.
Anomaly detection
Statistical and ML methods to find outliers, fraud, and quality issues.
Feature engineering
Transform raw data into model-ready features with encoding, scaling, and creation.
Who it’s for
- Analysts — Run statistical tests and build models without leaving your spreadsheet
- Data scientists — Prototype faster with instant Python execution and visualization
- Researchers — Process experimental data with proper statistical methods
- Business users — Get data science results through natural language, no coding required