How to implement guide with dataframe.groupby() in pandas not retaining original index on aggregation
Hey everyone, I'm running into an issue that's driving me crazy... I'm having trouble with the `groupby()` function in Pandas when trying to aggregate data while keeping the original index intact. I want to group by a specific column and calculate the mean of another but need the original indices to remain in the resulting DataFrame. Here's what I've tried so far: ```python import pandas as pd data = { 'category': ['A', 'B', 'A', 'B', 'A'], 'value': [10, 20, 30, 40, 50] } df = pd.DataFrame(data) # Attempting to group by 'category' and calculate the mean result = df.groupby('category')['value'].mean() print(result) ``` This code outputs the mean values but loses the original indices. I've also experimented with using `as_index=False` but that didn't work for my case. The output looks like this: ``` category A 30.0 B 30.0 Name: value, dtype: float64 ``` What I actually need is a DataFrame that retains the original index, like so: ``` category value 0 A 10 1 B 20 2 A 30 3 B 40 4 A 50 ``` I'm using Pandas version 1.3.3. Any suggestions on how to achieve this while still using the `groupby()` method? Thanks in advance for your help! For context: I'm using Python on Windows 10. Any help would be greatly appreciated! I'm using Python 3.11 in this project. Has anyone dealt with something similar?