Skip to content

Pivotal documentation

Pivotal is a data analysis language for Python. It offers a concise syntax for common data operations that compiles to Pandas, Polars or DuckDB code. With comprehensive JupyterLab and VS Code support Pivotal provides a friendly entry point to the Python data ecosystem.

Why Pivotal? Getting Started Tutorial User Guide Syntax Reference Pandas Cheatsheet

A live-demo of Pivotal in Jupyter Lab is available via Binder:

JupyterLab demo

Key features

Readable, writable syntax - write data transformations in a concise declarative syntax that feels familiar to SQL and Pandas users

Multiple backends - compile to Pandas, Polars, or in-process DuckDB SQL

JupyterLab and VS Code integration - syntax highlighting, autocomplete, %%pivotal cell magic, interactive object viewer and explorer, and GUI controls

AI support - ask an LLM or coding agent to generate, run, verify, and compare Pivotal code via the Pivotal MCP server

Comprehensive data pipelines - build full workflows with data-quality checks, pipeline functions, column loops, and loadable config / metadata values

Plotting and tables - create charts and publication-ready tables with simple syntax, backed by matplotlib and Great Tables

Data packages - export DataFrames, charts, and tables to a single Frictionless data package

Python integration - call Python functions, load Python variables, and mix Pivotal and Python code as needed

Install

pip install pivotal-lang

This installs the full feature set - Pandas, Polars, DuckDB, Great Tables.

For a minimal Pandas-only install:

pip install --no-deps pivotal-lang
pip install lark pandas matplotlib

Quick example

%%pivotal
load "orders.csv" as orders

with orders as monthly
    filter status == "complete"
    assign month = left(date, 7)
    group by month
        agg sum amount as revenue, count id as n_orders
    sort month

from pivotal import DSLParser

parser = DSLParser()
parser.execute("""
load "orders.csv" as orders

with orders as monthly
    filter status == "complete"
    assign month = left(date, 7)
    group by month
        agg sum amount as revenue, count id as n_orders
    sort month
""")

# monthly_report.pivotal
load "orders.csv" as orders

with orders as monthly
    filter status == "complete"
    assign month = left(date, 7)
    group by month
        agg sum amount as revenue, count id as n_orders
    sort month

pivotal monthly_report.pivotal