Aggregation is the process of implementing any mathematical operation on a dataset or a subset of it. Aggregation is one of the many techniques in pandas that's used to manipulate the data in the dataframe for data analysis.
The Dataframe.aggregate() function is used to apply aggregation across one or more columns. Some of the most frequently used aggregations are as follows:
- sum: Returns the sum of the values for the requested axis
- min: Returns the minimum of the values for the requested axis
- max: Returns the maximum of the values for the requested axis
We can apply aggregation in a DataFrame, df, as df.aggregate() or df.agg().
Since aggregation only works with numeric type columns, let's take some of the numeric columns from the dataset and apply some aggregation functions to them:
# new dataframe that consist length,width,height,curb-weight and...
