How can I use groupby in Pandas for advanced data analysis?

How can I use groupby in Pandas for advanced data analysis?

You can use the groupby function in Pandas to group your data by one or more columns and then apply various aggregation functions. What specific analysis are you looking to perform?

I want to calculate the average sales per region and also get the count of transactions.

You can achieve this by using groupby on the 'region' column and then applying agg to calculate both the average and count. Here's an example:

import pandas as pd

df.groupby('region').agg({'sales': 'mean', 'transaction_id': 'count'})

Can I also filter the groups based on a condition?

Yes, you can filter groups by using the filter method after grouping. For example, to get regions with average sales greater than a certain threshold:

df.groupby('region').filter(lambda x: x['sales'].mean() > threshold)

What if I want to group by multiple columns?

You can group by multiple columns by passing a list to the groupby method. For example:

df.groupby(['region', 'product']).agg({'sales': 'sum'})