Brought to you by NumFOCUS Foundation and O’Reilly Media
The official Jupyter Conference
Aug 21-22, 2018: Training
Aug 22-24, 2018: Tutorials & Conference
New York, NY

JupyterLab and Plotly: A data visualization power couple

Lindsay Richman (McKinsey & Co.)
4:10pm–4:50pm Friday, August 24, 2018
Data visualization, Kernels, Usage and application
Location: Nassau Level: Beginner
Average rating: ****.
(4.50, 2 ratings)

Who is this presentation for?

  • Data visualization engineers, data scientists, consultants, digital media specialists, and digital operations specialists

Prerequisite knowledge

  • Familiarity with basic terminal commands, the Jupyter Notebook, and a programming language like Python, Julia, or R (useful but not required)

What you'll learn

  • Learn how to use JupyterLab, the Jupyter Notebook, and Plotly to create dynamic data visualizations


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.

Photo of Lindsay Richman

Lindsay Richman

McKinsey & Co.

Lindsay Richman is a digital operations specialist at McKinsey & Company, where she programs in Python and JavaScript, primarily working in the areas of data visualization, frontend web development, and robotics. Lindsay uses machine learning and AI to help streamline operations, improve product quality, and drive informed decision making.