Practical Pandas With Sql: From Database To Dataframe

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

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 scratchConnect Python to SQL with safe, reusable practicesUnderstand the power differences between SQL and Pandas—and when to use whichWrite advanced queries with CTEs, aggregations, and window functionsMaster performance tuning with indexes, query pushdown, and chunkingBuild secure, parameterized queries that protect against SQL injectionThis 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 & DependenciesBefore 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 DatabaseEvery 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 PythonHere 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 CapabilitiesNow 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 CTEsAggregations 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 & RankingsThis 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 & ChunkingWith 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 QueriesFinally, 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.

Overview

Section 1: Introduction

Lecture 1 A Very Quick Welcome

Lecture 2 Course Resources

Section 2: Virtual Environment And Dependencies

Lecture 3 Creating A Virtual Environment

Lecture 4 Installing Dependencies

Section 3: Setting Up The Database

Lecture 5 Creating The Database

Lecture 6 Installing psql

Lecture 7 Seeding The Database

Section 4: Connecting From Python

Lecture 8 A Quick Test

Lecture 9 The Anatomy Of The Connection URL

Lecture 10 Using Environment Variables

Lecture 11 Constructing The URL From Environment Variables

Lecture 12 Extra Concepts Corner - Pandas Engine, Connections, Pools, Queries

Section 5: Foundational SQL And Pandas Capabilities

Lecture 13 SQL vs Pandas Conceptual Takeaway

Lecture 14 Skill Challenge - Shortest Films

Lecture 15 Skill Challenge - Solution

Lecture 16 Joins and Merges

Section 6: Common Table Expressions And Advanced Pandas Aggregations

Lecture 17 Aggregations: CTEs, CASE WHEN, HAVING

Lecture 18 Skill Challenge - Revenue Analytics by Customer Segment

Lecture 19 Skill Challenge - Solution

Section 7: Window Functions And rank(), window()

Lecture 20 Why Window Functions Exist?

Lecture 21 Ranks And TOP N Analytics

Lecture 22 Rolling Windows And Running Totals

Section 8: Performance And Chunking

Lecture 23 Push Filtering Into SQL

Lecture 24 Indexes And EXPLAIN ANALYZE

Lecture 25 Pandas Chunked Aggregation

Section 9: Parameterized Queries

Lecture 26 SQL Injection And Parameter Binding

Lecture 27 Parameterizing Multi-Value Filters

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