Mistakes often occur when using aggregation operations in Python

There are a few frequent mistakes people make while working with Pandas’ aggregate operations. Examples with code samples are shown below:

1. Not specifying the correct column(s) for aggregation:

Not supplying the appropriate column(s) on which to execute the aggregation is a typical mistake. This can result in errors or inaccurate results. Make sure you pick the right column or columns for aggregate.

2. Applying aggregation on non-numeric columns:

Only numeric columns can be used with aggregation operations like mean, sum, minimum, maximum, etc. If they are used on non-numeric columns, the output will either be inaccurate or meaningless.

3. Forgetting to group by appropriate columns:

The correct columns to group by must be specified when doing group-wise aggregation. Incorrect results or unauthorized aggregations can come from forgetting to include the required grouping columns.