JupyterLab and Plotly work extremely well together, allowing users to quickly and efficiently create interactive charts using an array of programming languages and sources. With JupyterLab’s efficient interface, a multitude of files and notebook kernels may be displayed within a single browser tab. Similarly, Plotly provides APIs for nearly all of the programming languages used for working with data and is supported by an excellent repository of chart examples and documentation. This is extremely useful for organizations and individuals with diverse data collection and measurement needs.
Lindsay Richman demonstrates how to use JupyterLab, Plotly, and Plotly’s Python-based Dash framework to create dynamic charts and interactive reports. Lindsay begins with an overview of JupyterLab and Plotly’s APIs for popular programming languages before explaining how to use JupyterLab (via Jupyter notebooks) with Python and R kernels to create datasets based on time series and sentiment analysis for cryptocurrency data and how to transform the datasets into Plotly charts. You’ll see how the charts can seamlessly be repurposed as components in a Dash web app and combined with live-streaming data and written analysis to create an interactive business intelligence report. Lindsay concludes by running some of the data through a support vector machine (SVM) algorithm to provide actionable insights for a Python-based trading bot.
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