Python and code execution

Sourcetable’s AI runs Python code under the hood to perform complex data transformations, statistical analysis, and visualizations. You don’t need to write Python yourself — the AI generates and executes it based on your natural language instructions.

How it works

When you ask the AI to perform an operation that benefits from code execution, it:

Generates Python code tailored to your request
Executes it against your spreadsheet data
Returns the results (tables, charts, statistics, or transformed data)
Optionally shows you the code it ran

Available libraries

Library	Category	Capabilities
pandas	Data manipulation	Dataframes, groupby, merge, pivot, filtering, reshaping, time series
NumPy	Numerical computing	Arrays, linear algebra, random sampling, mathematical operations
SciPy	Scientific computing	Statistical tests (t-test, chi-square, ANOVA), distributions, optimization, signal processing
StatsModels	Statistical analysis	Regression (OLS, logistic, Poisson), time series (ARIMA, SARIMAX), hypothesis testing
scikit-learn	Machine learning	Classification, regression, clustering, dimensionality reduction, preprocessing, model evaluation
matplotlib	Static visualization	Publication-quality charts, custom layouts, export to image
plotly	Interactive visualization	Interactive charts, 3D plots, dashboards, hover tooltips
seaborn	Statistical visualization	Heatmaps, pair plots, distribution plots, regression plots
bokeh	Interactive visualization	Browser-based interactive charts, streaming data
transformers	NLP / Deep learning	Hugging Face models for text classification, summarization, translation, sentiment analysis
TabPFN	AutoML	In-context learning for tabular prediction and classification without training
BeautifulSoup	Web scraping	HTML/XML parsing, data extraction from web pages
SQLAlchemy	Database	ORM, database connectivity, query building
DuckDB	In-browser SQL	Fast analytical queries on dataframes and files, process multi-GB datasets
huggingface-hub	Model hub	Access and download models from the Hugging Face Hub
Pyodide	Python runtime	Python execution in the browser via WebAssembly
dbt	Data transformation	SQL-based data transformation pipelines
Apache Arrow	Data processing	Columnar in-memory data format for fast analytics

Example operations

Data normalization

Ask: “Normalize the revenue column using min-max scaling”

from sklearn.preprocessing import MinMaxScaler
import pandas as pd

scaler = MinMaxScaler()
df['revenue_normalized'] = scaler.fit_transform(df[['revenue']])

Statistical testing

Ask: “Run a t-test comparing revenue between Group A and Group B”

from scipy import stats

group_a = df[df['group'] == 'A']['revenue']
group_b = df[df['group'] == 'B']['revenue']
t_stat, p_value = stats.ttest_ind(group_a, group_b)
# Returns: t-statistic, p-value, and interpretation

Time series forecasting

Ask: “Forecast next quarter’s revenue using ARIMA”

from statsmodels.tsa.arima.model import ARIMA

model = ARIMA(df['monthly_revenue'], order=(1, 1, 1))
results = model.fit()
forecast = results.forecast(steps=3)

Correlation heatmap

Ask: “Show me a correlation heatmap of all numeric columns”

import seaborn as sns
import matplotlib.pyplot as plt

corr = df.select_dtypes(include='number').corr()
sns.heatmap(corr, annot=True, cmap='coolwarm', center=0)

Feature engineering

Ask: “Create a customer lifetime value feature from order history”

import pandas as pd

clv = df.groupby('customer_id').agg(
    total_spend=('order_total', 'sum'),
    order_count=('order_id', 'count'),
    avg_order_value=('order_total', 'mean'),
    tenure_days=('order_date', lambda x: (x.max() - x.min()).days)
)
clv['predicted_ltv'] = clv['avg_order_value'] * clv['order_count'] * 1.2

When Python is used vs. spreadsheet formulas

The AI uses Python when…	The AI uses spreadsheet formulas when…
Statistical tests or ML models are needed	Simple calculations (SUM, AVERAGE, IF)
Complex data reshaping or pivoting	Lookups (VLOOKUP, INDEX/MATCH)
Multi-step data transformations	Cell-level formatting or conditional logic
Generating charts or visualizations	Basic math across columns
Processing external data (web scraping, APIs)	String manipulation (CONCATENATE, TRIM)
Working with very large datasets	Single-column operations

Viewing and modifying the code

To see the code the AI executed:

Ask “Show me the code you used” or “What Python did you run?”
The AI displays the full code in the chat response
You can copy the code, modify it, and ask the AI to re-run your version

SQL execution

The AI also runs SQL queries using DuckDB:

SELECT region, SUM(revenue) as total
FROM sheet_data
WHERE year = 2024
GROUP BY region
ORDER BY total DESC

SQL queries can target both spreadsheet data (via DuckDB) and connected databases.

MCP execution

The execute_mcp tool lets the AI call external tools through the Model Context Protocol. This extends capabilities to:

Web scraping via Apify MCP connector
Custom APIs through configured MCP servers
Third-party tools that support the MCP protocol

Performance notes

Python execution runs server-side, not in your browser
Large operations are processed asynchronously
Results stream back as they’re ready
The AI manages memory and execution limits automatically

Getting started

Spreadsheet

AI features

Data science

Superagents

Tools

Visualizations

Templates

Connectors

Data

Collaboration

Stock trading

Financial analysis

Python and code execution

How it works

Available libraries

Example operations

Data normalization

Statistical testing

Time series forecasting

Correlation heatmap

Feature engineering

When Python is used vs. spreadsheet formulas

Viewing and modifying the code

SQL execution

MCP execution

Performance notes

​How it works

​Available libraries

​Example operations

​Data normalization

​Statistical testing

​Time series forecasting

​Correlation heatmap

​Feature engineering

​When Python is used vs. spreadsheet formulas

​Viewing and modifying the code

​SQL execution

​MCP execution

​Performance notes

How it works

Available libraries

Example operations

Data normalization

Statistical testing

Time series forecasting

Correlation heatmap

Feature engineering

When Python is used vs. spreadsheet formulas

Viewing and modifying the code

SQL execution

MCP execution

Performance notes