It’s the opposite of the Tragedy of the Commons — all of the private property holders, acting in their own self interest

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.

⁴ The 2019 paper is full of amazing tidbits like this: ~1.4% of contributors account for ~90% of the edits to OSM. This roughly adheres to something called the 1% rule of online communities which states that, “1% of Internet users are responsible for creating content, while 99% are merely consumers of that content.”

³ As an astute commenter on Hacker News pointed out, Tesla uses Google Maps for its dashboard. They only use OpenStreetMap for the auto-summon feature, which incidentally has resulted in lots of parking lots being added to OSM by the Teslarati as covered in Jennings’ most recent SOTMUS talk!

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.

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.

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 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.

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.

Quick Jupyter notebook deployment into dashboards is the strong side of Viola but also its downside. You can make coherent Dashboards with Voilà, but that needs you to format your experiments and code in Jupyter notebooks accordingly or suppress and hide unused code and markdowns.

Voilà is primarily a native Jupyter rendering tool. However, you can create interactive reports with widgets in Jupyter notebook using Ipywidgets. You can also use Viola to render any content on Notebooks into Dashboards.

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.

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hold in the U.S. in March, the juggling expanded to diverting employees and packages away from facilities in the hardest-hit areas, like New York City, and finding a way to move personal protection equipment to health care organizations. The company directed more than 150 flights and 1,000 ocean cargo containers to move PPE into and around the U.S., and set up 28 new flight legs just to process Covid tests. “We can flex our networks better than anyone in the business,” boasts Smith.

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.

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.