The calculation of π to an insane number of digits is something that has an interesting history. This talk will look at historic algorithms for the calculation π and implementation of the algorithms in Python. And we will meditate upon how lucky we are to have computers to do the calculation. In doing this we will see things in the Python standard library that make it possible to calculate the crazy values needed in modern algorithms (e.g. one over a factorial cubed). The final demonstration is the calculation of π to 100 significant digits.
Help! I want to run a simple task on a schedule how do I do that? Wait, remember when I said "a simple task", well it just got more complicated. And why isn't the data ready when my job runs?! Oh no, yesterdays job failed...what do I do?
These are all common problems lots of data analysts/engineers and scripters encounter. How do you solve them in a sustainable way? This intro talk is helpful for all who think about job scheduling.
Make neuroimaging results easier to digest for patients and equip technicians with an enhanced toolkit featuring improved visualization and statistical analysis capabilities through MNE, an open-source Python Library.
With the rise in AI, there is more focus on Python dependency management and SCA scanning. Python's dependency management system makes it easy for developers to leave dependencies out of the manifest. This means that almost every SCA tool that relies on a manifest will be wrong. We show how leveraging program analysis techniques one can avoid the pitfalls of these so-called phantom dependencies.
This talk is about an open-source educational project based on Python: blupants.com. My goal is to present it to the audience and hopefully find more people passionate about the topic like me who would be willing to help as open-source contributors. Attendees will learn how to use the REST API to control robots and how to extend it to new platforms and robots.
What's the difference between "sudo apt install python3.12" and "wget ... && ./configure && make && make test && sudo make install"?
Do I need to use a virtual environment in my docker container?
I installed python, but it didn't come with pip.
Let's take a look at python on Debian. It runs both your OS and your web app, and it's weirder than you think.
Ansible is a fantastic automation tool written in Python but sometimes playbook logic gets very complicated to do seemingly simple tasks. In our talk we will show how writing a custom Ansible module is not as hard as you might think and how leveraging Python for more complicated logic lets you write easier to navigate playbooks.
Additionally we will review:
- A light Ansible overview
- Explanations of Ansible modules and plugins
- How testing fits in
Presenting on an open source project I started and have been working on since three years ago. It is a library named PyPDFForm which has a variety of utilities making processing PDF forms easier with Python. I'll discuss what sparked this idea, give a little coding session to demo some of the library's functionalities, and talk about the future of the library.
Here is a brief layout of what I'm currently planning on for the speak.
* First 15 minutes: Intro. Some background, both personal and about the library. What sparked this idea and just general motivations.
* Next 10 minutes: A coding session which demos some basic but essence functionalities of the library.
* Final 5 minutes: Talk about the future of the library. What are some restrictions it has now. What are some improvements that can be done. And QA.
Some PyPDFForm links:
The paywall at Wired.com, TheAtlantic.com and other websites implement a paywall with javascript. The source page that is sent has the entire article. This talk is about the use of the BeautifulSoup package (which manipulates HTML documents) to read what the web site sends.
In this session we will perform a full exploration of the Snowpark ML Toolkit using dbt Python models on the dbt Cloud without any package installation. You will learn how Snowflake improves on familiar ML libraries like Scikit-Learn and XGBoost to make them scale within its scalable runtime.
During this session you will:
- Start with 50,000 rows and scale to 50 million rows on various data transformations with the toolkit
- Train and predict a dbt Python model with XGBoost
- Switch between SQL and Python transformations in a pipeline where dbt takes care of the boilerplate code when pushing to Snowflake