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Category: Jupyter Notebooks

  • Using Ipywidgets in Jupyter Notebooks

    Using Ipywidgets in Jupyter Notebooks

    Introduction to Ipywidgets Ipywidgets are a powerful tool that can enhance the interactivity and functionality of Jupyter Notebooks. These widgets allow users to create interactive elements such as sliders, buttons, dropdown menus, and text boxes, making it easier to visualize data, manipulate parameters, and customize the user experience. With ipywidgets, you can create dynamic and…

  • Creating Charts with Python and Seaborn

    Data visualization is a crucial part of understanding and communicating insights from data. Among the many Python libraries available for visualization, Seaborn stands out for its simplicity, flexibility, and ability to create aesthetically pleasing charts with minimal code. In this blog post, we’ll explore how to use Seaborn to create a variety of charts in Jupyter Notebooks,…

  • Classifying Technical Documentation with TensorFlow: Building a GitHub README Classifier

    In this tutorial, I’ll walk you through how to build a machine learning system that automatically classifies GitHub README files by programming language. This practical application of natural language processing demonstrates how to use TensorFlow to analyze and categorize technical documentation. To provide a simple development environment we’re going to use a Jupyter Notebook. Each…

  • Interactive Mapping with Python: Building a Transit Visualization using Folium

    Folium makes it easy to create beautiful interactive web maps in Python by combining the power of Python’s data handling with the Leaflet.js mapping library. While there are many ways to showcase Folium’s capabilities, building a transit system visualization provides a perfect practical example – combining real world data and various layer types. In this…

  • Getting Started with Jupyter Notebooks in VS Code

    Introduction Jupyter notebooks are powerful interactive computing environments that have revolutionized how we work with data and code. Originally developed for Python (from IPython notebooks), they’ve evolved into a language-agnostic platform that combines live code execution, rich text documentation, visualizations, and computational outputs all in one document. Think of them as a digital laboratory notebook…