esDynamic Integrations

The core of extensible programming is defining functions. Python allows mandatory and optional arguments, keyword arguments, and even arbitrary argument lists. Whether you're new to programming or an experienced developer, it's easy to learn and use Python. Python can be easy to pick up whether you're a first-time programmer or you're experienced with other languages. The following pages are a useful first step to get on your way to writing programs with Python! The community hosts conferences and meetups to collaborate on code, and much more. Python's documentation will help you along the way, and the mailing lists will keep you in touch. The Python Package Index (PyPI) hosts thousands of third-party modules for Python. Both Python's standard library and the community-contributed modules allow for endless possibilities.

With esChecker, fasten your release cycles, dramatically reduce testing and delivery costs, and mitigate risks. Don't compromise your digitalization, leverage your mobile application security with automated testing within your CI/CD process. With a unique dynamic analysis feature, esChecker automatically executes the mobile application binary on unsafe devices and gives immediate feedback on your protections. Like any other IT system component, mobile apps must be designed, developed, and maintained with security in mind. They are the entry point to the system and require special attention. Compared to pentesting, a MAST tool enables a shorter, quicker, and more efficient security testing process to better control the application's code as it progresses. It’s about code verification integrated into a development cycle and it gives immediate feedback, allows compliance, and can be integrated into a DevSecOps process.

Project Jupyter exists to develop open-source software, open-standards, and services for interactive computing across dozens of programming languages. JupyterLab is a web-based interactive development environment for Jupyter notebooks, code, and data. JupyterLab is flexible, configure and arrange the user interface to support a wide range of workflows in data science, scientific computing, and machine learning. JupyterLab is extensible and modular, write plugins that add new components and integrate with existing ones. The Jupyter Notebook is an open-source web application that allows you to create and share documents that contain live code, equations, visualizations and narrative text. Uses include, data cleaning and transformation, numerical simulation, statistical modeling, data visualization, machine learning, and much more. Jupyter supports over 40 programming languages, including Python, R, Julia, and Scala.