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Practical Pandas With SQL: From Database to DataFrame
Rating: 4.6 out of 5(5 ratings)
1,063 students

Practical Pandas With SQL: From Database to DataFrame

Master querying, joins, and aggregations across Python and SQL—learn window functions, parameterized queries, and more
Created byAndy Bek
Last updated 7/2026
English
English [Auto],

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

Course content

9 sections • 27 lectures • 3h 7m total length

  • A Very Quick Welcome0:59

    Begin with three quick points, download the course resources, and dive into a focused exploration of SQL and pandas, including advanced topics and additional grounding content.

  • Course Resources0:08

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.


  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.

Who this course is for:

  • Beginner to intermediate Python developers who want to add SQL and database skills
  • Python data analysts looking to level up by bridging SQL and Pandas into a unified workflow
  • Developers working with Postgres who want to build secure, efficient data pipelines

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