Handling Duplicate Rows in Pandas GroupBy with Custom Aggregation Function
I'm currently working with a DataFrame that contains multiple entries for the same group, and I need to apply a custom aggregation function. However, I'm working with issues with the output when rows are duplicated. For example, I have the following DataFrame: ```python import pandas as pd data = { 'group': ['A', 'A', 'B', 'B', 'C'], 'value': [10, 20, 10, 30, 10] } df = pd.DataFrame(data) ``` When I try to group by 'group' and apply a custom aggregation function that calculates the average of 'value', I get unexpected results because the duplicate entries are not handled as I intended: ```python def custom_agg(x): return sum(x) / len(x) result = df.groupby('group')['value'].agg(custom_agg) print(result) ``` The output I get is correct in terms of calculation, but I want to ensure that the custom aggregation function properly acknowledges duplicates without affecting performance significantly. For instance, for group 'A', it should properly account for duplicates by applying the average to the unique values instead. I'm using Pandas version 1.5.3. I also tried using `drop_duplicates()` before the aggregation, but that led to a loss of important context in my analysis. I was wondering if there's a more elegant way to handle this situation within the GroupBy operation itself without preprocessing the DataFrame. Any suggestions on how to achieve this would be greatly appreciated! I'm working on a CLI tool that needs to handle this. Has anyone else encountered this?