Here's top 3 integration methods and what you can do with Python. Let us then explore some of the options available to us, to invigorate Excel with the superpowers of Python!
Microsoft Excel — you either love it or hate it! If you are like most people, you have probably gone through early life without even hearing about Excel. You may even have gone to college or university, graduated with top marks and still had minimal experience with Excel.
That was until you made it to industry and got yourself a job. You then found out that if Excel were to disappear for an hour, the whole world would come to a halt!
Now, you probably know that there is very little that Excel cannot do! Having spent many years in investment banking, I can tell you that every time I think I’ve seen it all, I come across another spreadsheet someone’s put together! The possibilities are really endless.
The main limitations of Excel today, however, is around larger data sets. The larger the data set, the more difficulties you face in Excel. In a data-driven, instant gratification kind of world, people want things to happen instantly; they hate waiting! There is also an expectation to constantly push the boundaries and provide ever-increasing levels of functionality.
At the same time, people hate change. People are comfortable in Excel, and they don’t really want to move away from it. It is, therefore, our job to make that transition easier. Provide more speed, more functionality, without ever leaving the spreadsheet.
Until Excel can support big data, this is where Python comes in! Integrating Excel with Python allows us to supercharge the functionality we offer to our users through Excel. It allows our users to remain in their familiar, easy to understand Excel-world, all the while Python can take care of some of the heavy lifting! It provides for an intermediate step to this data-driven world until both Excel and our less technical colleagues catch up.
Let us then explore some of the options available to us, to invigorate Excel with the superpowers of Python!
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Data Science and Analytics market evolves to adapt to the constantly changing economic and business environments. Our latest survey report suggests that as the overall Data Science and Analytics market evolves to adapt to the constantly changing economic and business environments, data scientists and AI practitioners should be aware of the skills and tools that the broader community is working on. A good grip in these skills will further help data science enthusiasts to get the best jobs that various industries in their data science functions are offering.
In this article, see if there are any differences between software developers and software engineers. What you’re about to read mostly revolves around my personal thoughts, deductions, and offbeat imagination. If you have different sentiments, add them in the comment section, and let’s dispute! So, today’s topic…
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