Pandas groupby using column values
In this second video how to groupby using pandas and as part of expanding the data analytics information of this website, we are looking to explain how you can use a groupby selection but only using the column values and not the column names.
Below we import our data into a dataframe, and then group as follows:
- Aggregate function
- Using the cut function and assigning values to bins.
- Assigning labels to the data frame output based on the bin values.
Why would you want to use Pandas groupby and column values?
This video looks to help understand the why going by values might be easier than column names:
- Column names can change from project to project, using by values allows easy implementation of getting the output regardless of the names used.
- You could apply this to any Python class, and as long as you can inherit will allow the code to run smoothly.
- Implementing by value allows a clear understanding of the desired output as the values are clearly understood to generate what is required.
- You need to understand how data within your data set falls within a particular cohort:
- This use of values in different programs just needs to change, the underlying logic remains the same.
- Using column names still means that to group them, the logic still needs to be written.