How to Set Up Quadratic for Data Analysis

Quadratic lets you analyze data using Python, SQL, JavaScript, or plain English inside a spreadsheet. You do not need to install anything locally or configure an environment. The entire platform runs in your browser, and a free tier gives you full access to core features with limited AI usage.

This guide covers every step from creating an account to building your first automated analysis.

Why Quadratic matters for data work

Traditional spreadsheets hit a wall when datasets grow past a few thousand rows or when you need statistical analysis beyond basic formulas. The typical workaround is exporting to a Jupyter notebook or a BI tool, which means context-switching and version control headaches.

Quadratic solves this by embedding Python (with Pandas, NumPy, Plotly, and Scikit-Learn pre-installed), SQL, and JavaScript directly into spreadsheet cells. You write code in one cell and the output populates the grid just like a formula result. The platform is SOC 2 Type II and HIPAA compliant, encrypts data with AES-256 at rest and TLS 1.2+ in transit, and is trusted by teams at Bloomberg, Apple, Google, and BCG.

As of April 2026, Quadratic has over 250,000 registered users globally.

Step 1: Create your account

Go to app.quadratichq.com and sign up with your email or Google account. No credit card is required for the free tier. Once logged in, you land on a dashboard showing your recent files and templates.

Click "New File" to create a blank spreadsheet. The interface looks familiar if you have used Google Sheets or Excel, with a grid of cells, a toolbar, and a formula bar at the top.

Step 2: Import your data

Quadratic supports multiple data sources out of the box. You can drag and drop a CSV or Excel file directly onto the grid, and the data populates instantly. For larger workflows, use the Connections panel (accessible from the left sidebar) to link a live database.

Supported connections include PostgreSQL, MySQL, Snowflake, Google Analytics, Mixpanel, and Plaid. Once connected, you write SQL queries in a cell and the results fill the spreadsheet automatically. The connection stays live, so refreshing the query pulls updated data without re-importing files.

For this guide, start with a CSV. Download any public dataset (the WHO global health statistics CSV works well for practice) and drag it into your Quadratic file.

Step 3: Use the AI assistant

Click the AI chat icon in the bottom-right corner or press Cmd+K (Ctrl+K on Windows). Type a plain English question like "What is the average life expectancy by region?" and Quadratic generates the appropriate Python or SQL code, executes it, and places the result in your grid.

The AI assistant works in two modes. In the first mode, it generates code cells that you can inspect and edit. In the second mode, it answers questions conversationally without modifying the spreadsheet. You can switch between modes depending on whether you want to build something or just explore.

A practical tip: be specific with column names. Instead of asking "summarize this data," ask "calculate the median value of the GDP_per_capita column grouped by Region." Specificity gives the AI enough context to write accurate code on the first attempt.

Step 4: Write a Python cell manually

Even if you have never written Python, Quadratic makes it accessible. Click any empty cell, then select "Python" from the cell type dropdown (or press Ctrl+/). Type the following:

import pandas as pd

df = q.cells("A1:F100")
summary = df.describe()
summary

The q.cells() function reads data from your grid into a Pandas DataFrame. The describe() method returns count, mean, standard deviation, min, max, and quartiles for every numeric column. Press Shift+Enter to run the cell. The output table appears directly in the grid starting from the cell below.

Pre-installed libraries include Pandas, NumPy, SciPy, Plotly, Scikit-Learn, and Requests. You do not need to pip install anything. If you need a library that is not included, the AI assistant can often find an alternative approach using the built-in set.

Step 5: Run a SQL query

If your data lives in a connected database, click an empty cell and select "SQL" as the cell type. Write a standard query:

SELECT region, AVG(life_expectancy) as avg_le
FROM health_data
GROUP BY region
ORDER BY avg_le DESC

The result populates the grid as a table. You can reference this output in other cells using standard spreadsheet formulas or Python cells, which means you can chain SQL data extraction with Python-based statistical analysis in a single file.

Step 6: Build a chart

Quadratic uses Plotly for visualization. In a Python cell, write:

import plotly.express as px

df = q.cells("H1:I10")
fig = px.bar(df, x=df.columns[0], y=df.columns[1], title="Average Life Expectancy by Region")
fig

Press Shift+Enter. The chart renders inline in the spreadsheet. You can resize it by dragging the cell borders. Plotly charts are interactive by default, so hovering over bars shows exact values.

For users who prefer a no-code approach, the AI assistant can also generate charts. Ask "create a bar chart of average life expectancy by region from cells H1 to I10" and it writes the Plotly code for you.

Step 7: Share and collaborate

Click the Share button in the top-right corner to invite teammates by email. Quadratic supports real-time multiplayer editing with live cursors, similar to Google Sheets. Permission levels include viewer, editor, and admin.

Every change is tracked in version history, accessible from File > Version History. You can restore any previous state with a single click. For teams that need data governance, the enterprise plan adds role-based access control and self-hosted deployment options.

Common issues and fixes

The most frequent issue new users encounter is browser compatibility. Quadratic works best on Chrome. Firefox and Safari have limited support and may experience rendering issues with charts or slow code execution. If the AI assistant is unresponsive, check that you have not exceeded the free tier's monthly AI prompt limit.

Large file imports (over 50MB) can time out in the browser. For datasets of that size, connect directly to a database rather than uploading a file.

What comes next

Once comfortable with the basics, explore scheduled tasks (a newer Quadratic feature that lets you automate data refreshes and reports on a timer) and PDF import for pulling structured data from documents directly into the grid. If you want to skip the setup entirely and analyze data from a file upload with no configuration, tools like VSLZ handle the full pipeline from a single prompt.

Quadratic fills a specific gap: it gives spreadsheet users a path into code-based analysis without forcing them to leave the grid. For anyone spending hours copying data between Excel and Python notebooks, that alone saves meaningful time each week.