Jupyter Notebook vs. Mapbox

I've created a number of daisy chain notebooks for different workflows, and every time, I create my workflows with other users in mind. Jupiter Notebook makes it very easy for me to outline my thought process in as granular a way as I want without using innumerable small. inline comments.

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For services that require maps and basic geo-functionality in production, Mapbox is one of the greatest choices out there. They're free, provide much more refined/modern productions compared to Google maps, and have very good support on different platforms. For services that require higher-computation products, like matrix routing, optimization, etc..., the prices can get quite high very quickly, and you should consider moving those services to an on-premise server at that point.

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• Simple and elegant code writing ability. Easier to understand the code that way.
• The ability to see the output after each step.
• The ability to use ton of library functions in Python.
• Easy-user friendly interface.

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• Documentation is clean, easy to read, and very well organized which makes it easy to get going or learn something new
• The online user interface makes it quick and easy to edit maps, upload new datasets, view analytics, or get API keys
• Great integrations with Tableau and easy to develop for Android, iOS, or Web

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• Need more Hotkeys for creating a beautiful notebook. Sometimes we need to download other plugins which messes [with] its default settings.
• Not as powerful as IDE, which sometimes makes [the] job difficult and allows duplicate code as it get confusing when the number of lines increases. Need a feature where [an] error comes if duplicate code is found or [if a] developer tries the same function name.

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• The product is not always that easy to use, aimed at professionals understanding colors and palettes etc.
• Being a full cloud product it can be complicated working with the interface when your connectivity is not great.
• Tutorials and samples could be more visual and could cover more ideas on how to use Mapbox.

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Jupyter is highly simplistic. It took me about 5 mins to install and create my first "hello world" without having to look for help. The UI has minimalist options and is quite intuitive for anyone to become a pro in no time. The lightweight nature makes it even more likeable.

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It is a good tool to use. We can perform various customisations; I always end up exploring and finding a new feature that can be used in my work somewhere. And one good thing is that is actually quite reasonable in terms of cost, with the free tier being quite adequate

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I haven't had a need to contact support. However, all required help is out there in public forums.

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With Jupyter Notebook besides doing data analysis and performing complex visualizations you can also write machine learning algorithms with a long list of libraries that it supports. You can make better predictions, observations etc. with it which can help you achieve better business decisions and save cost to the company. It stacks up better as we know Python is more widely used than R in the industry and can be learnt easily. Unlike PyCharm jupyter notebooks can be used to make documentations and exported in a variety of formats.

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One feature that made me go in favor of Mapbox was its stellar documentation. Google Maps and Bing Maps are the other alternatives I considered, but the learning curve with both of them is steeper than it is with Mapbox. Also, Mapbox Studio gives newbies a very simple, clean and easy to use environment to make and store maps online

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• Positive impact: flexible implementation on any OS, for many common software languages
• Positive impact: straightforward duplication for adaptation of workflows for other projects
• Negative impact: sometimes encourages pigeonholing of data science work into notebooks versus extending code capability into software integration

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• Mapbox is the only service that has all the products we need to release our product to the market. Without Mapbox, we would've spent far more time integrating multiple different map/geo services like Mapbox and HERE maps together.
• Mapbox was sometimes expensive in the testing period, and we would've definitely moved some of the services on-premise to save money if we had the time.
• Mapbox has functionality for traffic-aware routing in many countries, as well as matrix-structured routing data, which is what enables our service to function. Having all of this integrated within an API allows us to easily scale our service to multiple different cities/countries in a matter of days.

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