> ## 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.

# Data science overview

> Run data science workflows in your spreadsheet with Python, ML, and statistics — no environment setup required.

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.

## How it works

**1. Prepare your data**

Import data via file upload, connector, or paste. Sourcetable handles CSV, XLSX, Parquet, JSON, and 15+ other formats.

**2. 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."

**3. Review results**

The AI writes and executes Python code, then writes results back to your spreadsheet. You can view, modify, and re-run the generated code.

## 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

> [card] [Exploratory data analysis](https://sourcetable.com/docs/data-science/eda.md)
>
> Profile datasets, find distributions, and surface patterns automatically.

> [card] [Statistical analysis](https://sourcetable.com/docs/data-science/statistics.md)
>
> Hypothesis tests, regression, ANOVA, and correlation analysis.

> [card] [Machine learning](https://sourcetable.com/docs/data-science/machine-learning.md)
>
> Classification, regression, clustering, and zero-shot predictions with TabPFN.

> [card] [Forecasting](https://sourcetable.com/docs/data-science/forecasting.md)
>
> ARIMA, exponential smoothing, and ML-based time series forecasting.

> [card] [Anomaly detection](https://sourcetable.com/docs/data-science/anomaly-detection.md)
>
> Statistical and ML methods to find outliers, fraud, and quality issues.

> [card] [Feature engineering](https://sourcetable.com/docs/data-science/feature-engineering.md)
>
> 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


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