This is what the first 1000 lines without any styling looks like: Visualizing the first 1000 lines from our Harvard Caselaw Access Project dataset Let’s visualize a sample of data to give us an idea of its structure. We’ll look at examples once we’ve loaded our first set of data. Relying on the project’s own API to find and download cases, it’s easy to prepare a script which uses keywords to query the cases we want to visualize, and convert the results into the JSON format ReGraph understands. We’ll use graph visualization to find real insight and bring citation source data to life. The data has been the source of other projects that use visualization methods such as heatmaps, scatter graphs and geomaps. It’s already in graph format, with nodes representing cases and links representing citations from one case to another.Ī quick word about citations: In US case law, citations to other cases are often used to identify past judicial decisions in order to prove an existing precedent or to deliver a persuasive argument. 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. Integrate PlantUML seamlessly within many other tools.Data scientists often work with large and difficult datasets. If you're new to PlantUML, begin with the quick start page. For a deep dive into the language specifics, refer to the PlantUML Language Reference Guide. Users can draft a variety of diagrams using a simple and intuitive language. PlantUML is a versatile component that enables swift and straightforward diagram creation. Edit in Dokuwiki Edit in Asciidoc Edit in Markdown□ PlantUML at a Glance
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