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Posted on • Originally published at blog.meetupfeed.io

Python: 6 Most Watched Videos in August 2022

Python tech talks were high on the list of the most watched videos on MeetupFeed in August. That’s why we decided to hand select the ones our audience fancied the most. In the spotlight: Optimization, Solving Wordle, Hacking, Compiling and A/B Test Analysis.

Optimizing Code | Anna Astori
Have you experienced issues with your Python code while wrangling new data sets and crunching big numbers? Have you wondered what are the places where you could make your Python scripts run faster? In this session, learn options to speed up your data processing operations.

Solving Wordle with Python & Selenium and Then Running it in GitHub Actions | Michael Mintz
In 2022, the Wordle game took over the world by storm. As curious automation engineers, it is our duty to find an automated solution to solve Wordle for us. Michael took on that responsibility and created a working Wordle-solver using Python and Selenium. As a bonus, he got that solution to run in GitHub Actions. In this presentation/demonstration, you’ll learn how all the pieces come together to make this happen.

How to Hack (Legally) | Karen Miller
When it comes to hacking, trainees are at risk of legal implications and developing bad habits. In this talk, Karen aims to provide an overview of best practices and trusted resources available to attendees who wish to develop penetration testing skills safely, with an emphasis on Python.

Compiling Python Programs into Differentially Private Ones | Johan Leduc & Nicolas Grislain
Working with privacy-sensitive data today, whether it is in health-care, insurance or any other industry, is a complex and slow process often involving long manual reviews by compliance teams. The recent development of differential privacy helped standardize what privacy protection means. As such it has the potential to unlock the automation and scaling of data analysis on privacy sensitive data. To help realize this promise, Johan and Nicolas designed and built a framework in which an analyst can write data analysis jobs with common data-science tools and languages: SQL, numpy, pandas, scikit-learn, and have them compiled into differentially private jobs executed remotely on the sensitive data. In this talk, they will describe how a user expresses his job declaratively in python and how his python code is analyzed and compiled, before it is run and a result is eventually returned.

Stats Don’t Have to Be Scary: Automatic A/B Test Analysis| Kristie Wirth
Learn how Kristie’s workplace manages to analyze dozens of concurrent A/B tests with millions of data points! She’ll discuss their previous manual analysis process, some things that have changed, and do a down-to-earth walkthrough of how you too can use Python to automate analyzing your tests.

Understanding Trends Driving the Usage of Python | Rachel Stephens
How does Python fit among programming languages? We’ll start with a zoomed out view of programming language trends and then drill into Python specifically. Our goal is to explore where, why and how Python is being used, and then discuss future opportunities and threats to the language.

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