Pandas groupby from the start
In this first video about pandas groupby and as part of expanding the data analytics information of this website, we are looking to explain how you can use a groupby selection to sort your data into similar datasets better so they can be better analysed.
In the video below, we import our data into a dataframe, and then group as follows:
- Directly naming the column
- Through get_group
- Using a loop
- Utilising a lambda function
Why would you want to use Pandas groupby?
As you work on a project, numerous scenarios will be presented to you that will make it hard to understand your data as an example:
- Similar errors dotted across a large data set, manually looking for them.
- The errors that appear can be in multiple columns.
- Errors can be anywhere and not follow any pattern.
- You need to understand how data within your data set falls within a particular cohort of data.
- As an example in a marketing campaign, you may need to group a set of customers who have not bought a particular product in the past so that they are not included, helps target the prospects who most likely might buy from you.
As a result, when you find your errors, the need to be able to group them for further analysis helps to understand quickly:
(A) How big of a problem it is.
(B) Capture pieces of data that allow a trace back to the root cause.
(C) Put a process in place to remediate the problems before they become a big problem and loss of customers.