List of videos

I will use a JupyterLab for the tutorial because it makes a very good teaching tool. You are welcome to use the setup you prefer, i.e editor, IDE, REPL. If you also like to use a JupyterLab, I recommend conda for easy installation. Similarly to virtualenv, conda allows creating isolated environments but allows binary installs for all platforms. There are two ways to install Jupyter via conda: 1. Use Minconda. This is a small install and (after you installed it) you can use the command conda to create an environment: conda create -n pycon2021py39 python=3.9 Now you can change into this environment: conda activate pycon2021py39. The prompt should change to (pycon2021py39). Now you can install JupyterLab: conda install jupyterlab. 2. Install Anaconda and you are ready to go if you don't mind installing lots of packages from the scientific field. 3. Install the dependencies: * Jupyter Lab 2 conda install jupyterlab * more_itertools conda more_itertools * toolz conda install toolz 4. Hint: You do all this in one command: conda create -n pycon2021py39 python=3.9 jupyterlab more-itertools toolz You can create a comparable setup with virtual environments and pip, if you prefer. WORKING WITCH CONDA ENVIRONMENTS After creating a new environment, the system might still work with some stale settings. Even when the command which tells you that you are using an executable from your environment, this might actually not be the case. If you see strange behavior using a command line tool in your environment, use hash -r and try again.

The greatest contribution of the age the decade in which deep learning exploded was not these big models, but a generalized toolkit to train any model by gradient descent. We're now in an era where differential computing can give you the toolkit to train models of any kind. Does a Pythonista well-versed in the PyData stack have to learn an entirely new toolkit, a new array library to have access to this power? This tutorial's answer is as follows: If you can write NumPy code, then with JAX, differential computing is at your fingertips with no need to learn a new array library! In this tutorial, you will learn how to use the NumPy-compatible JAX API to write performant numerical models of the world and train them using gradient-based optimization. Along the way, you will write loopy numerical code without loops, think in data cubes, get your functional programming muscles trained up, generate random numbers completely deterministically (no, this is not an oxymoron!), and preview how to mix neural networks and probabilistic models together... leveraging everything you know about NumPy plus some nearly-learned JAX magic sprinkled in!

What are regular expressions, what are they useful for, and why are they so hard to read? In this tutorial we will break down the regular expression syntax to better understand how they work. We will learn how to dissect regular expressions, how to use regular expressions in Python, and how to make your regular expressions more readable (yes it's possible... sort of). We will learn how to use regular expressions for data validation, data parsing, and data normalization. We'll also discuss when not to use regular expressions.

Excited about programming? Have you heard good things about Python? Now is the time to dive in and start learning how to program. This three hour tutorial covers the basics of the basics of Python. Programming is a wide and deep field, but you only need a taste. You'll learn about variables, expressions, loops, functions, and most importantly: what those words even mean to begin with. This is a tutorial for complete beginners (or those who want to start over again from the beginning.) This tutorial does not include computer science, machine learning, or brain surgery. By the end, we'll have create a few simple games (Guess the Number, Magic 8 Ball, and a Dice Rolling Simulator) as well as how to guide yourself through the next steps on your programming journey.

Dashboards are useful tools for data professionals from all levels and within different industries. From analysts who want to showcase the insights they have uncovered to researchers wanting to explain the results of their experiments, or developers wanting to outline the most important metrics stakeholders should pay attention to in their applications, these dashboards can help tell a story or, with a bit of interactivity, let the audience pick the story they’d like to see. With this in mind, the goal of this tutorial is to help data professionals from diverse fields and at diverse levels tell stories through dashboards using data and Python. The tutorial will emphasize both methodology and frameworks through a top-down approach. Several of the open source libraries included are bokeh, holoviews, and panel. In addition, the tutorial covers important concepts regarding data types, data structures, and data visualization and analysis. Lastly, participants will also learn concepts from the fields where the datasets came from and build a foundation on how to reverse engineer data visualizations they find in the wild.

A spreadsheet is a wonderful invention and an excellent tool for certain jobs. All too often, however, spreadsheets are called upon to perform tasks that are beyond their capabilities. It’s like the old saying, 'If the only tool you have is a hammer, every problem looks like a nail.' However, some problems are better addressed with a screwdriver, with glue, or with a Swiss Army Knife. Python is described by some in the programming world as the Swiss Army Knife of programming languages because of its unrivaled versatility and flexibility in use. This allows its users to solve complex problems relatively easily compared with other programming languages and is one of the reasons why Python has become increasingly popular over time. In this tutorial, we’ll briefly discuss spreadsheets, signs that you might be living in “Excel Hell”, and then we’ll spend the rest of the time learning how to escape it using Python. In the first section, we’ll extend on what spreadsheet users already know about cells, rows, columns, and formulas, and map them to their Python equivalent, such as variables, lists, dictionaries, and functions. At the end of this section, we’ll do an interactive exercise and learn how we can perform a simple calculation, similar to one you might do in Excel, but instead using Python. In the second section, we’ll discuss (and attempt) how we can perform more complex tasks including web scraping, data processing, analysis, and visualization, by utilizing a few popular 3rd party libraries used including Requests, Pandas, Flask, Matplotlib, and others. In the last section, we’ll round out our discussion with a few important concepts in data management, including concept of tidy data, building a data pipeline, and a few strategies (and packages) to use when approaching various data problems, including demo using Apache Airflow. https://github.com/ryansmccoy/spreadsheets-to-dataframes

For most developers, Python packaging feels like a magical (and cryptic) black box. Apps and libraries use a variety of tools and have different packaging challenges. Once you start reading up on this topic, you come across many seemingly random components: setuptools, pip, poetry, wheels, pyproject.toml, MANIFEST.in, virtual environments, zippapp, shiv, pex, and so on. The sheer number of concepts to master can be overwhelming, leading many programmers to conclude that packaging in Python is a mess. Before you despair, join me in this tutorial session where you'll have a chance to learn how to package and publish/deploy your library and/or application through hands-on exercises. Topics include: How and why library packaging differs from application packaging Differences between a source tree/source distribution/wheel Differences between a build back-end and a build front-end (and why we even have this separation) Tools used for packaging your library Tools and techniques used to package your application Testing your package for correctness

Time to take Time Series seriously! From inventory to website visitors, resource planning to financial data, time-series data is all around us. Knowing what comes next is key to success in this dynamically changing world. And for that we need reliable forecasting models. While complex & deep models may be good at forecasting, they typically give us little insight about the underlying patterns in our data. In this tutorial, we'll cover relatively simple yet powerful approaches for time series analysis and seasonality modeling with Pandas. At the end of this session, you will be familiar with the fundamentals of time series analysis, how to decompose time series into trend, seasonality and error component, and how to use our insights to create simple but powerful models for forecasting.

Writing unit tests for your code is widely accepted as a best practice. Learn how to use Pytest, the de-facto testing tool standard, and mock, the built-in library for creating mock objects, to write high-quality tests. Slides: https://github.com/dev-skill-up/pycon-2021-testing