Google Charts vs. Jupyter Notebook

We can easily recommend Google Charts to any company that needs a way to visually represent their data. Another great thing about Google Charts is that it is free to use and does not require any membership fees. Although it requires a skilled used to be able to use the charts, the results are great and can be beneficial to any company who is looking to make better decisions.

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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• they're free with Google suite and they have backing in terms of powerful Google apps which can be plugged in to perform multiple actions like using Google sheets to import raw data into Google Charts
• they're the most simple app to use when it comes to creating charts and visual dashboards
• ease of customization
• ease of using custom APIs from developers side to help make any types of charts and dashboards you want

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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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• I would like a couple more introductory videos or a live chat option for when you run into an issue. I think this is a Google-wide problem, not only linked to Google Charts.
• I have run into some issues with the Dynamic Data but also admittedly could potentially dive in deeper and investigate.
• It would be great if Google Charts made it possible to integrate Google Chat into the platform.

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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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Google Charts is a good product. It's widely supported with deep documentation and a large community. But for me, it wasn't customizable enough. When we started with simple charts, it was great, but as we got deeper and more complex, our needs outgrew the library. If I was going forward, I would choose a more barebones library with more freedom and extensibility.

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Google Charts is about as easy to use as the rest of their applications. The UI is very well thought out, allowing you to add what you need, and customize it to your exact liking. The default theme is actually really nice, which helps as most of the time, customizing is not needed.

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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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Google charts is always available and provides fast output.

Connectivity is occasionally very slow.

As a free tool with massively powerful, infinitely customisable charts that can be dynamically updated - Google Charts is my favourite data visualisation tool. However, my hatred of JavaScript does jade my view on it. This is the price of the tool though, and I'm glad it's available for me.

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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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The online training was solely done through self training on google's guide to charts.

Easy to implement

Google Charts stacks up better since it is free and does not have the constant pressure for cost overruns, add-ons, annual maintenance and implementation services. The speed of using Google Charts is quick, saving users potentially weeks in getting up and going. For the readers of websites with limited resources, the application shows up nicely is look and feel with charts. Great way of showing data visually.

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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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Google Charts is essential for quick, easy, reporting. There are opportunities for advanced features.

• teams were easily able to create charts and find out progress on our learning courses and thus take actionable insights to their managers, professors
• students could also know their progress on various courses from one single view, same for instructors in terms of imparting education
• sales team could learn which kind of courses sold more to focus more on those areas

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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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