מתוך סדנת הכנה ל־CFP של כנס פייקון ישראל 2024

מתוך סדנת הכנה ל־CFP של כנס פייקון ישראל 2024

Fortunately, best practices of clean code in software engineering have existed for many years and can help to avoid these problems before they occur. In this talk, we will review fundamental concepts from the influential book "Clean Code" by Robert C. Martin. The book was written in Java, but I assure you that I have enough examples of bad code also in Python 😃
We will discuss when and how to incorporate these concepts into your daily work, providing practical examples of clean code dos and don'ts in Python.

If you're a team lead, software developer, or data scientist interested in producing better code and spending less time debugging, this talk is for you. Join me to learn how to level up your team's skills and write maintainable, efficient code that will save you valuable time.

In this talk I'll demonstrate how nondeterminism can pose challenges even in simple functions, and how to use dependency injection to address these problems!

No? Good.

But someone had to check the grass isn't greener in hell, so you wouldn't be tempted to, right? And honestly, I've seen the last two running in production sorry i really gotta go ok thanks byeee dodges

Whilst standard automated tools such as bandit or pylint may help discover generic vulnerabilities such as insecure use of pickle or security errors/misconfigurations such insecure autoescape configuration, how can we verify that our custom solutions and guidance have been correctly implemented and more importantly, stay that way, without writing complicated custom rules for these tools?

In this talk we will discuss examples of custom solutions like this but more importantly, we will demonstrate how to continuously verify that the code remain in a secure state with these solutions implemented, on an ongoing basis. We will demonstrate using simple rule syntax provided by the free, open-source, Semgrep tool.

Some example scenarios we’ll discuss:

You should leave with ideas for how you can have custom and specific security guidelines that match your situation and how you can use a simple rule syntax to verify them as well as solve other similar code analysis problems.

Attend this talk this talk, to learn about the vulnerability that brought down the world's largest AI framework. Walk away with the tools to address these bugs in the future, learning how the library was debugged, and how the fix was tested. This talk will cover the technical detail behind how the fix was applied in the open, but also the process of working with security issues. Learn how to work with an Open Source community, while addressing a critical bug and keeping your cool.

Every line of code written is open-source, and in the public record, today.

The map is constructed from crowd sourced anonymized data of millions of driving assistance systems running computer vision processes in consumer vehicles.

In this talk we will share stories from the trenches on how we optimized python workloads to run distributed big data processing.
We will discuss:

• how we approach these kinds of issues
• tools we use to identify and optimize algorithmic python code
• examples of how to write algorithmic code that runs 10x times faster leveraging a range of tools and technologies

In this talk we will explore the AST module capabilities, and its lack of capabilities.
We will learn the general structure of ASTmodule, its class hierarchy and its supported functions. Our focus will be on acquiring the skill to recreate the initial code through its AST representation. It seems that even though transforming python code into an AST is a simple task, the inverse process of transforming AST into code fails to replicate the original code.

We will see some examples of fun code-manipulations. First, we will show how to automatically add meaningful logs to your code. Second, we will experiment with code reordering and last, we will explore ideas to manipulate code for mutant testing purposes.

Python has frameworks that combine code and database management such as Django.
What if we prefer using a simpler framework for running our code, like Fast Api that does not contain a database management functionality?
Alembic tool is a great solution for that. Alembic tool helps us manage our database by creating and running migrations automatically.

This talk will go over the basics:

• Start working with alembic on a Fast Api project.
• The basic functionality that this tool provides and its' main features.
• What are the pros and cons of working with this tool?

In this talk, we will talk about the different patterns of missing data and what are the best practices for handling each. In addition, we will show how to visualize missing data as part of our data exploration phase to understand our data better.

As python is the leading programing language for data scientists and data analysts, we will use pandas, missingno, scikit-learn and other tools to demonstrate those ideas and explore the data.

Assuming all our clients will always be in the same timezone is wrong for a globally used app.
Pythons DateTime and pytz packages can help you localize the information, but what is the “right localization”? And more importantly, how can we assume where to localize from?

When designing a flow that handles DateTime fields in server-client data exchange, timezone conversions are the main issue.

Another challenge is what can we assume about the DateTime data that comes from the client when they send a PUT request with a DateTime field? What should the response assume? What if the client sends a GET request without any additional data?

In this talk, we will address those issues by demonstrating a simple “Timed Assignment App” that can be used globally.
I’ll show the thought process that might go through your mind when designing the time features for this app, try different solutions using python packages, and finally suggest the “aviation method” for solving all the problems before they even happen.

Ever since my first position as an EDA Engineer - Electronics Design Automation Engineer – many years passed, I have been to many development positions, and I still find myself using these abilities I acquired, to improve my performance and excel in my tasks, both at work and my personal projects. Have you ever looked for a solution to ease your tasks in your personal or work life? Have you ever tried to think Automation? Can we really use it outside the defined world of automation jobs?

In this entry level lecture we will review together some real cases I dealt with and how I used Automation as a solution and improvement tool - automating repetitive commands, automating manual work, extracting information out of a big data and even writing a script that writes a reusable code. We will analyze the issues, review some code and discover the Power of Python in these tasks, and together we will train “automation thinking”.

Join me on the full fascinating tour of the automation mindset, and come out with a new and improved skill in your “bag of tricks”.

This talk will take us through the story of modern Python build systems and package managers. We'll learn where they come into play in the development life-cycle, their benefits, and their limitations.
Specifically, we will mention: Hatch, Poetry, PDM, Flit, and Rye.

We will cover dependency resolution and locking with .lock files and how to utilize them for reproducible environments and CI environment caches, facilitating development across large teams.

We will migrate together a Python project from the legacy setup.py file with many additional files into a single pyproject.toml with Poetry.

https://jond01.github.io/blog/pycon-il-23/