December 2024
Intermediate to advanced
576 pages
12h 58m
English
Content preview from Data Visualization in R and Python
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18Dash Dashboards
For developing dashboards with the Dash framework, it is recommended using a Python Interactive Development Environment (IDE). The support for Jupyter Notebook and JupyterLab exists, but there are some differences, and, in general, a Python IDE will serve you much better in this case.
For the Dash installation, the steps described in the official documentation should be carefully followed (https://plotly.com/python/getting-started/).
If JupyterLab is chosen as the development tool, it offers three usage modes: inline (dashboard rendering is shown within the notebook), jupyterlab (rendering is created in a new tab of JupyterLab), or external (a new tab in the predefined web browser is opened and the rendering is presented). Mode inline could be used only for truly simple dashboards, other than that it has too many limitations. Mode jupyterlab offers more flexibility, but it is an intermediate alternative between the inline and the web browser with not much use. The rendering in the web browser should be chosen as the favorite option, showing the dashboard in its natural environment and permitting mangling with traditional HTML settings and CSS style sheets, instead of just setting inline directives. There also exist (at least at the time of writing) some not-well-documented incompatibilities between the version of the Jupyter library for supporting Dash dashboards (package jupyter-dash and its dependencies) and the more recent Python versions (e.g., v.3.10), ...
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