List of videos

Gajendra Deshpande Founder & Managing Director at Eyesec Cyber Security Solutions Popular programming language index websites (TIOBE index) and developer surveys (Stack Overflow) place Python as one of the fastest-growing programming languages. However, this popularity also puts in the target range of attackers. The attackers perform malicious dependency attacks and use misconfiguration tools to reveal confidential information. Jukka Ruohonen, Kalle Hjerppe, and Kalle Rindell in their research paper ""A Large-Scale Security-Oriented Static Analysis of Python Packages in PyPI"" claimed that they scanned PyPI for security issues in Python packages and found the presence of at least one security issue in about 46% of the Python packages. In addition, security vulnerabilities can be present in the source code of the package. In this talk, we will address the security issues related to python packaging and possible solutions to make python packages secure. The talk begins with the importance of a secure package and vulnerabilities in the Python package index. Then, I will discuss Python packages such as Bandit for identifying common security issues in Python code and “safety” for dependency check. Next, I will discuss verifying and signing Python packages using GPG. Finally, I will discuss general guidelines for secure coding practices in Python. Outline 1. Importance of a secure package and vulnerabilities in python package index. (05 Minutes) 2. Bandit for identifying common security issues in Python code (4 Minutes) 3. Safety for dependency check (4 Minutes) 4. Verifying and signing PyPI and conda packages using GPG and Twine (4 Minutes) 5. General guidelines for secure coding practices in Python (5 Minutes) 6. Summary and Questions (3 Minutes) Other talks at this conference 🚀🪐 https://www.conf42.com​/python2022 — 0:00 Intro 0:24 Talk

Joshua Arvin Lat CTO at NuWorks Interactive Labs Taking care of the overall security of systems and applications running in cloud environments is not easy. Manually performing security operations and auditing procedures on a regular basis take a lot of skill, time, and discipline. At the same time, auditing the different processes, systems, and applications used by organizations involves the usage of several tools. For example, scanning network environments may involve the usage of tools such as **Nmap**, and scanning container images and servers may involve both open source and commercial vulnerability scanners. Some of the major challenges encountered when performing security assessments can be solved through proper automation and integration of tools. In this session, we will make our lives easier by using **Python** to automate different security tasks and responsibilities and use it with different security tools and services. We will talk about the tips and tricks on how to design and build your own custom security tools to solve real-world requirements. These include **Infrastructure as Code** solutions, code design patterns, data engineering techniques, and even basic penetration testing techniques. Other talks at this conference 🚀🪐 https://www.conf42.com​/python2022 — 0:00 Intro 0:24 Talk

Andrew Knight Developer Advocate at Applitools Have you ever seen those ""@"" tags on top of Python functions and classes? Those are called *decorators*. Decorators, in their simplest form, wrap functions around functions. That might sound confusing at first, but it's actually pretty useful. A simple decorator could measure execution times, add startup steps, or automatically repeat calls. Decorators are one of Python's niftiest language features, and they help programmers write DRY (Don't Repeat Yourself) code. In this talk, we'll learn all about decorators: 1. How they wrap functions 2. How to write our own decorators 3. How to do cool tricks with arguments, classes, and nesting 4. How to use popular decorators 5. How to decide when decorators are (and aren't) the *right* solution We'll walk through plenty of example code together. We'll also touch lightly on Functional Programming (FP) and Aspect-Oriented Programming (AOP) concepts to build a firm understanding about how decorators work. After this talk, you should be able to use decorators effectively in your own Python projects! Other talks at this conference 🚀🪐 https://www.conf42.com​/python2022 — 0:00 Intro 0:24 Talk

Kalyan Prasad Data Scientist & Analytics Manager at Creative Crewz Historically, networks have been studied extensively in graph theory, an area of mathematics. After many applications to several different subjects including physics, health science, and sociology, over the last years, network analysis has become an active topic not only in data science but also in finance. In a nutshell, a network is a system with nodes connected by linkages. Network analysis is popular to describe the characteristics or behaviors of complex networks. There has been also some research conducted to model the stock market using networks. The motivation is that the performances of certain stocks are often correlated, either because of the general market direction or the cyclicity of the same segments of the market. To model the stock market using network analysis, different stocks are represented as different nodes. However, defining the interaction, or creating edges, between different nodes is rather non-intuitive, unlike some physical networks, such as friendship network, in which interaction between different nodes can be defined explicitly. A traditional way to create edges between different nodes for stock market is to look at the correlations of some defined attributes. In our case, we analyze one of the reputed stock index data and identifies stock relationships in it. We propose a model that can depict such relationships and create networks of stocks. We investigate and create different networks according to the degree of correlation of stocks. Finally, we will visualize and evaluate our results accordingly. In this talk, we are going to cover the following points: • Introduction to Networks • History & why graphs • Finance evolution in networks • Understanding Network structure • Leveraging the power of Python Graphs • Real-time finance usage of network analysis using two examples(hands-on) • Wrap-up #Goal By the end of the talk, I will make sure that: • How is data connected with other data? • How do these financial connections matter? • How do complex systems move in time in the stock market? I promise you; it is an interesting one! Other talks at this conference 🚀🪐 https://www.conf42.com​/python2022 — 0:00 Intro 0:24 Talk

Pawel Skrzypek - CEO at Omphalos Fund & Anna Warno - Data Scientist at 7bulls.com Presentation of the advanced ensemble techniques for time series forecasting. The latest state of the art forecasting methods are ensembled using innovative techniques, to improve accuracy and robustness of predictions. During the session we will make an introduction to the forecaster’s ensemble approach and after that we will present a live demo based on real examples. The Temporal Fusion Transformer and N-Beats forecaster will be ensembled using various techniques and the results will be presented in the live session. In the conclusion we will share some suggestions regarding design of efficient and robust time series forecasting solutions. Other talks at this conference 🚀🪐 https://www.conf42.com​/python2022 — 0:00 Intro 0:24 Talk

Moshe Zadka Principal Engineer at Palo Alto Networks The application has been written. The container build is done on each merge. The last thing you want is to mess around with that. But there are things missing. There's no integration with metrics. There are no readiness checks, so nodes get traffic before they are ready. Luckily, every modern container deployment system allows for "side-cars": container images that are deployed alongside the main application containers. Writing a quick web application in Python to serve these missing bits, to be deployed as a side-car, is a popular solution. How do you do this? How do you integrate into your container management system? And when is it better to modify the original application? Other talks at this conference 🚀🪐 https://www.conf42.com​/python2022 — 0:00 Intro 0:24 Talk

Yonatan Goldschmidt Principal Engineer at Granulate In this talk, we will demonstrate through low-overhead profiling tools for user-land applications, specifically high-level ones. eBPF is a good basis for profiling tools in general; PyPerf, a BCC-based open-source tool of that kind, provides low-overhead Python applications profiling. This talk will walk through CPython internals and will then dive into PyPerf. It will then present a comparison to traditional profiling methods and will review the benefits of basing on eBPF vs. user-land, system-calls-based profilers, and how eBPF provides unique seamlessness and full transparency for Python applications. Other talks at this conference 🚀🪐 https://www.conf42.com​/python2022 — 0:00 Intro 0:24 Talk

Gil Zilberfeld Wizard of Testing at TestinGil "TDD is great, but it won't work on our legacy code" - I hear that a lot. That's why people don't even give TDD a try. Their code is killing their hope. TDD's basic examples are, well, basic, and have no relationship to real-world code. But it can work on legacy code, and everyone's got that. You just need to remember a few techniques, stick to the principles, and you can start doing TDD in your application code tomorrow. In this session I'll show how to do it, the techniques and principles involved. And I'll show how to add TDD code inside an ugly application. No more excuses then. It's possible to do TDD right there in your own legacy code. Let's do it. Key points - TDD may not seem applicable in “real code”, but the principles apply - Use characterization tests as a safety net - Use test-first principles to add new features and fix bugs - Refactor before and after adding code Other talks at this conference 🚀🪐 https://www.conf42.com​/python2022 — 0:00 Intro 0:24 Talk

Jeronimo Medina Madruga IT Technician at Universidade Federal de Pelotas This presentation is based on my master's thesis in the field of mathematics education, which is addressing the possibilities of using python for teaching mathematics. To get to this topic, it was necessary to research the current state of using Python and other languages in the classroom in Brazil and worldwide. This made it possible to understand many of the difficulties and advantages of working with programming languages as a tool to help teachers. Therefore, the idea is to pass on these lessons so that other educators can try to implement different experiences in their classes. Other talks at this conference 🚀🪐 https://www.conf42.com​/python2022 — 0:00 Intro 0:24 Talk