Search Results at Talk Python Training

Python memory management is often a black box to most developers. You probably know that Python uses reference counting, but how can you write code most efficiently to work with it? Did you know it also uses garbage collection? Do you know when that kicks it and its performance implications? With this course, you'll learn all of these concepts and more. You will explore them with concrete code examples, not just theories. And you will learn to optimize your code to both use less memory and to run faster.

Unlock the full velocity of modern data science with Polars, the blazing-fast DataFrame library built in Rust and delivered in Python. In this hands-on course you'll move from spreadsheet limitations and sluggish pandas workflows to lightning-quick analyses that handle millions of rows in the blink of an eye. Whether you're wrangling CSVs, taming messy Excel files, or joining multi-gigabyte datasets, "Polars for Power Users" shows you exactly how to translate everyday data tasks into clean, Pythonic code that runs 10-100× faster than traditional approaches. Through a pragmatic mix of short lectures, live REPL sessions, and a capstone case study on global GDP, you'll master Polars' expressive syntax, lazy evaluation engine, and productivity-boosting features such as pivot tables, joins, and Excel-style date accessors.

Python 3 has been out for a while and is the best Python version to start any new project. Now that Python 2 has been end-of-lifed, it is worth looking into porting Python 2 projects to Python 3. Not only is Python 3 faster than previous versions, it is also easier to use now that features like Unicode strings are standard. If you haven't had the chance to program in Python 3, this course is for you. We will cover the updates and new features found in Python 3. You will see examples of using the latest and greatest, and then try your hand going through the labs trying out these new features. Once you are done with this course you will pine for using these features in your code.

One of the areas Python really shines is in building clean and powerful web applications. Once you know the language basics, this course will teach you everything you need to build data-driven, modern web applications in Python.

Have you ever wanted to create a startup or small web-based business? Do you know a little of the Python language? Great, that's a huge first step. But there are many elements to launching a successful online business and this course will fill in the gaps and help you realize your dreams.

One of the areas Python really shines is in building clean and powerful web applications. Once you know the language basics, this course will teach you everything you need to build data-driven, modern web applications in Python with the Flask web framework.

If you work with Excel and have ever thought - "there has to be a better way" then this course is for you! Excel has been used to automate and power businesses and solve business users' problems for years. But there are many drawbacks with relying so heavily on Excel for your data processing needs. In this course, you will see how Python's modern data science libraries will allow you to solve the same problems while avoiding the pitfalls and limitations inherent in Excel.

While pandas is one of the true pillars of Python's data science tech stack, there will be times when you outgrow it. For example, if you need to leverage all 16 cores on your system rather than just 1 to get an answer much faster or if you have more data than will fit into your RAM, or even onto your disk. That's where Dask comes to the rescue. It has an API that is broadly compatible with pandas' but scales Python computation across cores, and across computers to bring you blazing fast analysis of data that exceeds what any single computer can handle. That's just the beginning! Dask is a free and open source library that helps scale your data science workflows and provides a complete framework for distributed computing in Python. This course will get you up to speed with Dask and show you how to easily convert pandas workloads to blazing Dask clusters (locally across cores or scaled-out across cloud servers). Future courses will get you up to speed with Dask and Pythonic distributed computation in other settings, such as machine learning.

Python 3.11 is one of the most significant releases in years. It's also one of the biggest with the change log weighing in at over 175,000 words of updates. You may have seen the headline features. But have you had a chance to explore them with code and really see how they can make your apps better? This 2-hour course is your guided tour of the changes to Python with this important release.

If you work with audio and video content, a whole new world of possibilities has opened up with the advent of AI. Now that it is nearly instant and free to extract highly accurate text from spoken word. And with that text, we can expand the possibilities in many directions. In this course, we will build a realistic and real-world app working with audio from podcasts. We will summarize them, generate transcripts, and even create specialized GPTs for that content. All of that will be done with amazing technologies such as FastAPI, Pydantic, MongoDB, HTMX and AssemblyAI.