The best tools for Dashboarding in Python
Numbers do not speak for themselves!. We make them talk.
An essential part of data analysis is communication. We need to arrange information in a comfortable and digestible way to communicate, highlight and visualise critical areas.
Dashboards take your data visualisation to the next level. They connect different visualisation components and make a whole and integrated data visualisation stories. Web application Dashboards also allow users to interact with the data and the visualisation, offering them to see and adjust what they want or need.
It has never been easier to create a dashboard in Python. We have several dashboard tools at our disposal to make coherent data visualisation stories without using the traditional Dashboard powerhouses like Tableau or Power BI.
In this article, I will list out the four most popular Dashboarding tools in Python. I will highlight what they are suitable for, their functionalities and the learning curve.
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Do you want to create dashboards quickly in Python?. — Streamlit is your best option.
Streamlit revolutionises creating web applications with easy to use API and constant feature development. It was only last year October when this open-source tool was launched and no doubt its popularity has increased rapidly in the data science community.
Today, Streamlit boosts more functionalities with its recent introduction of streamlit component, where the developer community adds new functionalities.
Sharing and Deploying Streamlit apps has also become super easy with the new one-click deploying service from streamlit (In Beta). You can now develop and create web applications and dashboards, and deploy them in minutes rather than days, Thanks to Streamlit.
What I like about streamlit is that it has the shortest learning curve of all Python Dashboard creating tools in this list. It offers simple API with excellent documentation and lets you develop applications with less code in pure python.
In simple terms, Streamlit empowers you to focus on what matters rather than thinking about front-end back-end technology stacks to use for your project.
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Do you want to create powerful and advanced dashboards in Pure Python with declarative and reactive programming? — Panel is your best bet.
Panel is an open-source Python library that lets you create custom interactive web apps and dashboards by connecting user-defined widgets to plots, images, tables, or text.
While it is possible to work Streamlit in Jupyter notebooks, we use primarily with Python files. If your favourite data science tool is Jupyter Notebook, then Panel offers extensive support for all plotting libraries.
The learning curve is steeper than Streamlit. However, it is simple to create an interactive web application in Panel, using less code with widgets and parameters.
Deploying and sharing your web applications and dashboards in Panel is easy. You can display your dashboards inside Jupyter Notebooks, render it as Ipywidgets, run it from the command line, or deploy it using popular tools like Heroku, MyBinder or even other cloud platforms.