Move from Excel to Python with Pandas Transcripts
Chapter: Course conclusion and review
Lecture: Practical example

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0:00 in the final section of this course, we went through a real life example to bring many of the concepts together.
0:07 We worked with multiple different file types and brought them together to build a very nice
0:13 summary report that answered an important business question. Along the way, we highlighted some of the benefits to this approach versus Excel.
0:21 So when you are done with this analysis, you have a Jupyter notebook. It has documented steps that you can use for future
0:28 analysis. It's easy to rerun and model the analysis. So in our examples, as we changed our commissions rates,
0:36 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,
0:45 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.
0:55 Finally, we've just touched the tip of the iceberg for all the capabilities that you
1:00 have in the Python ecosystem. We could take this analysis that we've done and build
1:06 on it and build more complex visualizations machine learning algorithms, orm or complex analysis to answer critical business problems.

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