Pandas groupby with custom aggregation function not returning expected results
I'm trying to figure out Could someone explain Hey everyone, I'm running into an issue that's driving me crazy. Quick question that's been bugging me - I'm encountering an issue with the `groupby` method in pandas while trying to apply a custom aggregation function. The goal is to group my DataFrame by a specific category and compute a custom metric based on multiple columns. However, my computed results are not matching my expectations. Here's a simplified version of my DataFrame: ```python import pandas as pd data = { 'category': ['A', 'A', 'B', 'B', 'C', 'C'], 'value1': [10, 20, 30, 40, 50, 60], 'value2': [1, 2, 3, 4, 5, 6] } df = pd.DataFrame(data) ``` I want to group by `category` and compute a custom metric that averages `value1` and sums `value2`, returning a new DataFrame. Here’s the aggregation function I wrote: ```python def custom_agg(group): return pd.Series({ 'avg_value1': group['value1'].mean(), 'sum_value2': group['value2'].sum() }) ``` When I try to apply this function with: ```python result = df.groupby('category').apply(custom_agg) ``` I’m getting the following result: ``` avg_value1 sum_value2 category A 15.0 3 B 35.0 7 C 55.0 11 ``` This part looks fine, but when I try to reset the index afterward: ```python result.reset_index(inplace=True) ``` I get an unexpected DataFrame with the extra index level: ``` category avg_value1 sum_value2 0 A 15.0 3 1 B 35.0 7 2 C 55.0 11 ``` I wanted the `category` to be a regular column instead of an index. I've also tried using `as_index=False` in `groupby()`, but it didn't help with applying my custom function. Is there a way to achieve the desired output while maintaining the `category` as a normal column? Any insights would be greatly appreciated! Is there a better approach? I'm working in a Windows 10 environment. I'm working on a CLI tool that needs to handle this.