LM User Interface

Python can be used in various development environments, providing flexibility for different types of users. These interfaces allow programmers to write, test, and execute Python code efficiently.

Local Interfaces

  • Visual Studio Code (VS Code): VS Code is a lightweight, versatile code editor that supports Python through extensions. It offers advanced features such as IntelliSense (code suggestions), debugging, and version control integration. VS Code is widely used due to its flexibility, customization options, and support for various programming languages.

  • PyCharm: PyCharm is an Integrated Development Environment (IDE) designed specifically for Python development. It provides robust features such as code completion, refactoring, error detection, and integrated testing tools. It is available in both free (Community) and paid (Professional) versions.

  • RStudio: Although originally designed for the R programming language, RStudio also supports Python development. It allows users to run Python scripts, work with Jupyter notebooks, and integrate with conda environments. This makes RStudio especially attractive for data scientists and statisticians who work with both R and Python in a unified workflow.

  • Thonny: Thonny is a beginner-friendly Python IDE developed for teaching and learning. It features a simple interface, a built-in debugger, and a variable explorer, making it especially suitable for those new to programming.

Online Interfaces

  • Jupyter uni-Marburg: Jupyter uni-Marburg is a new platform provided by Philipps-Universität Marburg, specially designed to support learning and research activities. Students can log in using their university account, making it very convenient and secure. Most of the essential libraries you need for data analysis, scientific computing, and visualization are already pre-installed, so you can start coding immediately without setup hassles.

    Jupyter uni-Marburg offers both classic Jupyter Notebook and JupyterLab environments.

    • Jupyter Notebook: Allows you to combine code, text, images, and plots in a single, interactive document. It is ideal for experiments, prototyping, and creating well-documented workflows.
    • JupyterLab: Provides a more flexible and powerful interface, where you can open multiple notebooks, terminals, text editors, and file browsers side by side. This makes it easier to organize complex projects and workflows.

    Because of its browser-based interface, you can access your work from anywhere, and it’s especially useful for courses and collaborative projects. Overall, it is an excellent tool for anyone at uni-Marburg who wants to explore Python and data science in a practical, hands-on way.

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  • Jupyter Notebooks: Jupyter is a web-based interactive environment that allows users to create documents containing live code, equations, visualizations, and narrative text. It’s popular for data analysis, machine learning, and educational purposes. Its interactive nature makes it ideal for testing code in real-time and documenting workflows.

  • Google Colab: Similar to Jupyter, Google Colab is a cloud-based notebook environment. It provides additional benefits like free access to GPUs and the ability to collaborate with others in real-time. It’s widely used in research and machine learning communities.

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