Trouble with Python 3.8 and Pandas GroupBy returning unexpected NaN values
I'm currently using Python 3.8 with Pandas for data manipulation, and I've run into an scenario when trying to group data and calculate the mean. I have a DataFrame containing sales data with columns for 'Region', 'Sales', and 'Profit'. When I apply the `groupby` function and calculate the mean on 'Profit', I'm getting unexpected NaN values in the result, which doesn't seem right based on my data. Here’s the code I'm using: ```python import pandas as pd # Sample data data = { 'Region': ['East', 'West', 'East', 'West', 'East'], 'Sales': [250, 150, 300, 200, 100], 'Profit': [50, 30, None, 20, 10] } df = pd.DataFrame(data) # Grouping by 'Region' and calculating mean of 'Profit' result = df.groupby('Region', as_index=False)['Profit'].mean() print(result) ``` I expected to see the mean profit for each region without any NaN values, but instead I get: ``` Region Profit 0 East 30.0 1 West 25.0 ``` Why is the mean returning NaN for some groups, and how can I handle this situation properly? I've tried using `dropna()` before the `groupby`, but it didn't resolve the scenario. Any advice would be appreciated!