JupyterLab Inline Interactive Plots
JupyterLab is an open-source, web-based IDE (Integrated Development Environment) for working with Jupyter-Notebooks, data and data analysis. It allows working with R, Python, and Markdown among other languages to cater to the various needs of its users. This article focuses on creating interactive inline plots with Python-3 in a Jupyter Notebook inside JupyterLab.
A Jupyter notebook is composed of cells that can contain code (like Python code) or information (like some text created using markdown). When a cell containing code to output a graph/plot is executed, JupyterLab either shows it in a new window outside the notebook or shows it inside the notebook between the cells. The output between the cells is called an inline plot. An example is shown in the figure below.
Figure 1: Screenshot showing an inline plot in a jupyter notebook.
The plot in the image above is static/non-interactive. But sometimes we need to interact with the plot with actions like zoom, pan, etc. A plot which allows these actions is called an interactive plot. If such an interactive plot is placed between the cells of a jupyter notebook, it is now an inline interactive plot.
We start with the installation and setup of JupyterLab on a personal computer system and then proceed to create inline interactive plots with Matplotlib, Plotly and Bokeh in order.
How to Use JupyterLab Inline Interactive Plots
This article shows how to create inline interactive plots in JupyterLab with Python-3 programming language. It assumes basic familiarity with JupyterLab/Jupyter Notebooks and Python-3. By the end of the article, the reader will be able to understand and create inline interactive plots with Matplotlib, Bokeh, and Plotly plotting libraries inside a Jupyter-Notebook (in JupyterLab) using Python-3.