Move from Excel to Python with Pandas Transcripts
Chapter: Course conclusion and review
Lecture: Grouping and joining data
0:00 in Chapter six. We continue to look at pandas in more detail.
0:03 We looked at how we can aggregate group and merge data together.
0:08 We talked about the group by function and how useful it can be toe work across
0:12 multiple columns of our data frame.
0:14 This example. We can perform different mathematical functions on the quantity and extended amount columns
0:21 and then group by the company and product to build a really nice summary table of
0:26 our data that gives us tremendous amounts of insight with very little code.
0:31 And once we go into this in more detail,
0:33 their whole bunch of aggregation options that are available in pandas for US toe use on
0:40 our data frames. In many instances you can replicate the pivot table that you would
0:46 do in excel with pandas as well.
0:48 Weaken specifying index, a column and various values to be aggregated to build summary reports
0:55 that are very powerful and very similar to what we do in Excel with the pivot
0:59 Finally, we talked about how we can bring multiple data frames together and excel
1:05 You would typically use a copy and paste to add additional rose to a worksheet
1:12 with pandas. You can use the can cat function toe.
1:14 Add to data frames together on top of each other.
1:17 If you want to merge data similar to what you would do with an XlV,
1:21 look up to create a combined data frame,
1:24 you would use the merge command.
1:25 It's way more powerful than the XlV.
1:28 Look up because you can do multiple types of joins on your data frames.