Move from Excel to Python with Pandas Transcripts
Chapter: Course conclusion and review
Lecture: Practical example
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in the final section of this course, we went through a real life example to bring many of the concepts together.
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We worked with multiple different file types and brought them together to build a very nice
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summary report that answered an important business question. Along the way, we highlighted some of the benefits to this approach versus Excel.
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So when you are done with this analysis, you have a Jupiter notebook. It has documented steps that you can use for future
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analysis. It's easy to rerun and model the analysis. So in our examples, as we changed our commissions rates,
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all we had to do was change a small amount of code and rerun and evaluate the output. The examples we walked through were relatively small Excel files,
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but there's no reason the code that we developed couldn't run on much larger data sets that would be very impractical to run in an Excel file.
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Finally, we've just touched the tip of the iceberg for all the capabilities that you
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have in the Python ecosystem. We could take this analysis that we've done and build
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on it and build more complex visualizations machine learning algorithms, orm or complex analysis to answer critical business problems.