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Json graph visualization1/23/2024 ![]() ![]() This dataset features connections between US court decisions in the form of citations. It includes over 6.4 million cases going back as far as 1658 and it’s represented by 47 million nodes and links. The goal of the project was to “transform the official print versions of all historical US court decisions into digital files made freely accessible online.” The resulting database took 5 years to complete. We’ve chosen data from Harvard University’s Caselaw Access Project. Our dataset: about Harvard’s Caselaw Access Project So with ReGraph, our Python widget and analysis tool ready, we just need some data to visualize. ReGraph comes with its own advanced graph analysis functions, but it can also translate and visualize existing algorithms, which makes it easy to integrate into an existing project. Once built, we can use the extension directly from Python code in JupyterLab, making it interactive and ready for visualizations.įor graph network analysis and manipulation we’ll use NetworkX, the Python package that’s popular with data scientists. This library synchronizes the underlying data model between the Python code and the data. We’ve based our custom widget on the IPython widgets structure. To integrate ReGraph components with JupyterLab, we’ll create a Python widget, because that’s the language of choice for many data scientists. Not using ReGraph yet? Sign up for a free trial Jupyter graph visualization with ReGraphįirst we need to download and install ReGraph. Designed for React, ReGraph provides a number of fully-reactive, customizable components that fit nicely into an extension or widget. It’s the perfect candidate for integration with JupyterLab. Jupyter’s next generation project, JupyterLab, provides a flexible and extensible environment, making it easy to integrate with third-party components.Īs a front-end web application, ReGraph fits seamlessly in any environment and works with virtually any data repository. We’ve previously written about Jupyter Notebook, a web application that’s popular with data scientists for its versatility, shareability and extensive language support. Project Jupyter supports interactive data science through its software, standards and services. To give you an idea of what you can achieve, we’ll also create beautiful Python graph visualizations from a large and challenging dataset featuring US case law. Creating beautiful and insightful graph visualizations with Python, JupyterLab and ReGraph With powerful layouts, intuitive node grouping, social network analysis and rich styling options, ReGraph helps data scientists organize their data, reveal and highlight patterns, and present their insights to the world in a clear, beautiful way.Īnd here’s the best thing – it’s easy to integrate with JupyterLab, one of the leading tools for working with Python in data science. ReGraph, our graph visualization toolkit for React developers, is designed to build applications that make sense of big data. To find insight in their complex connected data, they need the right tools to access, model, visualize and analyze their data sources. Questions in violation of this rule will be removed or locked.Data scientists often work with large and difficult datasets. Specific assistance questions are allowed so long as they follow the required assistance post guidelines. General open ended career and getting started posts are only allowed in the pinned monthly getting started/careers thread. Problem you are attempting to solve with high specificity.Research you have completed prior to requesting assistance.If you are asking for assistance on a problem, you are required to provide If you post such content on any other day, it will be removed. Sharing your project, portfolio, or any other content that you want to either show off or request feedback on is limited to Showoff Saturday. We do not allow any commercial promotion or solicitation. Please refer to the Reddit 9:1 rule when considering posting self promoting materials. Read and follow reddiquette no excessive self-promotion. Check out /r/ProgrammerHumor/ for this type of content. Specific issues that follow rule 6 are allowed.ĭo not post memes, screenshots of bad design, or jokes. For vague product support questions, please use communities relevant to that product for best results. No vague product support questions (like "why is this plugin not working" or "how do I set up X"). Beginner question? Try the FAQ first! or the WebDev Resources Post then post in the Beginner Questions thread. ![]()
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