From the course: Python in Excel: Data Outputs in Custom Data Visualizations and Algorithms

Breaking down Excel and Python processes

From the course: Python in Excel: Data Outputs in Custom Data Visualizations and Algorithms

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Breaking down Excel and Python processes

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- The idea behind Python in Excel is that Python extends the capabilities of Excel rather than replacing it. Just because we can use Python, that doesn't necessarily mean we should. Before we dive into Excel and Python, let's talk about how the modeling process and data in general works on a high-level tool agnostic view. This process involves extracting data, cleaning, organizing, analyzing, modeling, and visualizing it. Excel can perform a lot of these steps, Python can also perform a lot of these steps. However, there are some parts of the process that Python is much better suited for, because as a data science programming language, it's built for working with data. It's much more efficient and scalable for running these models like anomaly detection and clustering. We can also use Python to create visuals for time series models and dendrograms for hierarchical clustering. The idea behind Excel and Python is to focus on the business users who might not be that familiar with Python. The goal for this course is to show how this functionality can bridge the two. Developers can write Python code, and then put it in Excel so that it connects between the Python code and Excel. Business users can adjust inputs or look at the outputs of running Python without having to know a lot of the code themselves. A question that I get all the time is, "Is the cloud secure?" The cloud for running Python in Excel is hosted and built by Microsoft, so the answer is yes as it's supported by Azure. Now we know a little bit more about both Python and Excel. Let's start talking about how to extract, transform, and load data in Power Query.

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