Panel enables sharing of state between application pages, leading to the creation of complex multi-page applications. Panel: Explicitly supports multi-page applications, and can do so in multiple different ways - such as using Tabs, Pipelines, and Templates. Voilà: Not explicitly or out of the box, with potential workarounds not really akin to a multi-page applications. Sharing of data between application pages is non-trivial at present. Dash: Explicitly supports multi-page applications. Streamlit: Not explicitly or out of the box, but potential workarounds can be found on the Streamlit support forum. The Best: Panel or Voilà The Worst: Dash Multi-Page Application Support Panel: All of the main Python plotting libraries - Matplotlib, Seaborn, Altair, Plotly, Bokeh, PyDeck, GraphViz, and even the R ‘ ggplot’ library. Voilà: All of the main Python plotting libraries, including Matplotlib’s Pyplot library, Seaborn, Altair, Plotly, Bokeh, PyDeck, and GraphViz. External libraries exist for alternative plotting libraries - namely Seaborn/Matplotlib, Altair/Vega-Lite, and Bokeh - within Dash, however these libraries are not very robust, and the level of interaction with the outputted graphs is not at the same level as Plotly-produced graphs. Dash: Primarily built for use with the ‘ plotly.py’ Python graphing library. At present the level of interactivity you can include in Streamlit plots is limited. Also provide their own native plotting library with line charts, area charts, bar charts, and maps. Streamlit: All of the main Python plotting libraries, including Matplotlib’s Pyplot library, Seaborn, Altair, Vega-Lite, Plotly, Bokeh, PyDeck, and GraphViz. The Best: Dash & Voilà The Worst: Streamlit & Panel Python Graphing Library Support Streamlit: Python only Dash: Python, R, Julia Voilà: Python, C++, Julia Panel: Python only Panel: Creating a flexible framework for making dashboard applications which are not tied a specific GUI, with code that works the same in Python script files and Jupyter/IPython notebooks. Voilà: Converting Jupyter/IPython notebooks into stand-alone interactive web-based dashboard applications, enabling a smooth transition from the exploratory phase of data analysis to the communication of the resulting data insights. Dash: Closing the gap between Data Science and the rest of the organization. Streamlit: Turning Python scripts into shareable, interactive dashboard applications as quickly as possible. Let’s begin! The Frameworks’ Primary Objectives To view the original articles on Streamlit, Plotly Dash, Voilà, & Panel, click the links provided. The purpose of this article is not to go over everything discussed in the original 4 article series, but to briefly compare the content matter of those articles in one place. This allowed me to get a feel for each of the technologies and experience their advantages and disadvantages first-hand. For my comparison to be more insightful and realistic I have exposed myself to each of the frameworks over a period of months, and have created a shared example dashboard application utilising each of the four frameworks. The new, more streamlined criteria can be found in the table of contents above, and each of the four frameworks mentioned will be examined under these criteria. These titles are no longer relevant in the scope of a direct comparison, hence, they have been excluded from this comparison article. Some of the titles used in this original 4-part series were included to provide context of each framework’s background, as well as providing information that may have been necessary to get started using each framework. For those of you who have came from my previous articles, note that I have slightly amended the comparison criteria which I have used in the initial 4-part series - which looked at each framework individually. As such, I opted to focus my research entirely on these four dashboarding frameworks, as I wanted to focus in-depth on the industry leaders, as opposed to the breadth of dashboarding frameworks available.Īs no solidified criteria exist for reviewing and comparing dashboarding frameworks, I had to create my own comparison criteria which made sense in the context of choosing one particular dashboarding framework over another. The current industry leaders in this space are Streamlit, Plotly Dash, Voilà, and Panel. Over the past five months I have been exploring and critically examining the leading frameworks in the Python dashboarding ecosystem.
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