Move from Excel to Python with Pandas Transcripts
Chapter: Course conclusion and review
Lecture: Grouping and joining data
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in Chapter six. We continue to look at pandas in more detail. We looked at how we can aggregate group and merge data together.
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We talked about the group by function and how useful it can be toe work across multiple columns of our data frame.
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This example. We can perform different mathematical functions on the quantity and extended amount columns
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and then group by the company and product to build a really nice summary table of
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our data that gives us tremendous amounts of insight with very little code. And once we go into this in more detail,
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their whole bunch of aggregation options that are available in pandas for US toe use on
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our data frames. In many instances you can replicate the pivot table that you would do in excel with pandas as well.
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Weaken specifying index, a column and various values to be aggregated to build summary reports
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that are very powerful and very similar to what we do in Excel with the pivot
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Finally, we talked about how we can bring multiple data frames together and excel
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You would typically use a copy and paste to add additional rose to a worksheet with pandas. You can use the can cat function toe.
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Add to data frames together on top of each other. If you want to merge data similar to what you would do with an XlV,
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look up to create a combined data frame, you would use the merge command. It's way more powerful than the XlV.
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Look up because you can do multiple types of joins on your data frames.