![]() ![]() ’Dash Enterprise’ was also launched on Microsoft Azure marketplace during the same year. ![]() In 2019 Dash for R was created, and in 2020 Dash for Julia was created, leading to a language-agnostic framework built upon the most common data science programming languages. In 2017 Plotly launched the initial beta version of Dash, with the framework’s initial release coming in early 2019. Founded in 2013, by just 2015 Plotly’s Python and R graphing libraries had become the most downloaded in the world. Dash was created by parent company Plotly, who are already well established within the world of data science, due to their ‘ plotly.py’ and ‘ plotly.js’ Python and JavaScript graphing libraries. Plotly Dash is a reasonably new framework for building machine learning and data science applications. Who Are Dash & What Is the Framework’s Primary Objective? The goal with these articles is not to make you proficient in using each of these frameworks - but rather to detail their strengths, weaknesses, best use cases, and all of the information that you should consider before choosing to utilise one of these frameworks in your own projects. Last week, I released my first review in this series on Streamlit. This will be followed by a comparison article in which I will compare the 4 frameworks directly. Over the next 2 weeks I will be releasing an in-depth critical review of Voilà and Panel. 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. These criteria can be found in the table of contents above, and each of the four frameworks mentioned will be examined under these criteria. 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 four months I have been exploring and critically examining the leading frameworks in the Python dashboarding ecosystem. Bonus: Example Soccer Analytics Dashboard Using Dash.Maintenance - Advantages + Disadvantages.Deployment - Options, Advantages + Disadvantages.Prerequisite Skills Needed (Excluding Python).Development - Advantages + Disadvantages.Who Are Dash & What Is the Framework’s Primary Objective?.Plotly Dash - Everything You Need To Know Table of Contents ![]()
0 Comments
Leave a Reply. |
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |