While working with the pandas dataframes, our analysis may require us to split our data by certain criteria. Groupby mechanics amass our dataset into various classes in which we can perform exercises and make changes, such as the following:
- Grouping by features, hierarchically
- Aggregating a dataset by groups
- Applying custom aggregation functions to groups
- Transforming a dataset groupwise
The pandas groupby method performs two essential functions:
- It splits the data into groups based on some criteria.
- It applies a function to each group independently.
To work with groupby functionalities, we need a dataset that has multiple numerical as well as categorical records in it so that we can group by different categories and ranges.
Let's take a look at a dataset of automobiles that enlists the different features and attributes of cars, such as symbolling, normalized...
