Practical Pandas With SQL: From Database to DataFrame

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

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.

1. 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.

2. 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.

3. 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.

4. 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.

5. 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.

6. 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.

7. 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.

8. 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.

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