Move from Excel to Python with Pandas Transcripts
Chapter: Course conclusion and review
Lecture: Grouping and joining data

Login or purchase this course to watch this video and the rest of the course contents.
0:00 in Chapter six. We continue to look at pandas in more detail. We looked at how we can aggregate group and merge data together.
0:09 We talked about the group by function and how useful it can be toe work across multiple columns of our data frame.
0:15 This example. We can perform different mathematical functions on the quantity and extended amount columns
0:22 and then group by the company and product to build a really nice summary table of
0:27 our data that gives us tremendous amounts of insight with very little code. And once we go into this in more detail,
0:34 their whole bunch of aggregation options that are available in pandas for US toe use on
0:41 our data frames. In many instances you can replicate the pivot table that you would do in excel with pandas as well.
0:49 Weaken specifying index, a column and various values to be aggregated to build summary reports
0:56 that are very powerful and very similar to what we do in Excel with the pivot
1:00 Finally, we talked about how we can bring multiple data frames together and excel
1:06 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.
1:15 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,
1:22 look up to create a combined data frame, you would use the merge command. It's way more powerful than the XlV.
1:29 Look up because you can do multiple types of joins on your data frames.

Talk Python's Mastodon Michael Kennedy's Mastodon