[100% Off] Practical Pandas With Sql: From Database To Dataframe

Master querying, joins, and aggregations across Python and SQL—learn window functions, parameterized queries, and more

Added on September 22, 2025 IT & Software 3 min read

What you’ll learn

Safely connect Python to SQL databases using environment variables and connection URLs
Write
optimize
and debug SQL queries—from joins and aggregations to advanced CTEs and window functions
Translate SQL logic into Pandas and vice versa
knowing when each tool is the best fit
Apply performance techniques like indexing
pushdown
and chunking to handle large datasets
Protect against SQL injection and write secure
parameterized queries
Set up Python virtual environments and manage dependencies with confidence

Requirements

Basic familiarity with Python (variables
functions
packages)
Some familiarity with SQL and Pandas
Previous exposure to data analysis concepts will help
but is not essential

Description

This hands-on course bridges the critical gap between SQL, Pandas and python—the three pillars of modern data work.

The course is designed for data analysts, developers, and aspiring data scientists who want to develop confident fluency across the data analytics stack.

By the end, you’ll walk away with the skills to:

Set up and seed databases from scratch
Connect Python to SQL with safe, reusable practices
Understand the power differences between SQL and Pandas—and when to use which
Write advanced queries with CTEs, aggregations, and window functions
Master performance tuning with indexes, query pushdown, and chunking
Build secure, parameterized queries that protect against SQL injection

This course is designed not just to show you the “how,” but also to explain the “why”—so every tool and technique you learn becomes part of a bigger framework for solving real-world data challenges.

We start with the foundations and build layer by layer, until you can confidently handle tough data problems end-to-end.

Virtual Environment & Dependencies
Before writing a single query, you’ll learn how to set up a clean virtual environment. This ensures your projects are portable, reproducible, and reliable—no more “it works on my machine” headaches. You’ll see how to manage dependencies properly, so that the same codebase can run smoothly on any system.
Setting Up the Database
Every serious data project needs a robust backend. You’ll provision a cloud-based Postgres instance in a few clicks, and then seed your database with data. Whether you’re on Mac (with libpq) or Windows (with the Postgres installer), you’ll have step-by-step guidance to get up and running quickly.
Connecting From Python
Here we bridge the two worlds: you’ll learn how to build a safe and flexible connection layer between Python and SQL. By using environment variables and connection URLs, you’ll avoid leaking credentials. You’ll also see how to plug SQL directly into Pandas for immediate analysis.
Foundational SQL & Pandas Capabilities
Now that everything’s connected, we’ll explore the building blocks: comparing how SQL and Pandas handle the same tasks. Through intuitive challenges, you’ll master joins and merges, learning when to use one tool over the other.
Advanced Aggregations with CTEs
Aggregations go way beyond a simple GROUP BY. You’ll learn CASE WHEN logic, the power of HAVING filters, and the CTE (Common Table Expression) pattern. Each has a Pandas equivalent, so you’ll gain a dual fluency that makes switching between tools effortless.
Window Functions & Rankings
This is where analytics gets powerful. You’ll dive into window functions like RANK(), rolling windows, and running totals, and then map these to Pandas’ own capabilities. These techniques let you answer business-critical questions about trends, rankings, and cumulative behavior.
Performance & Chunking
With bigger data comes bigger challenges. You’ll learn about query pushdown, where the database does the heavy lifting; about indexes, which can supercharge your queries; and about chunked processing in Pandas, which makes it possible to work with millions of rows without exhausting memory.
Parameterized Queries
Finally, we cover how to keep your code both secure and scalable. By using parameterized queries, you’ll eliminate the risks of SQL injection and keep your SQL clean, even as query complexity grows.

This isn’t just theory—you’ll apply each concept through hands-on challenges that mirror real-world data problems. By the end, you won’t just know the syntax; you’ll know how to think about data in ways that make you faster, safer, and more effective than most analysts and engineers in the field.