Pandas DataFrame groupby returning inconsistent results with categorical data
I've been struggling with this for a few days now and could really use some help... I'm working on a project and hit a roadblock. I'm encountering an issue when using `groupby` on a DataFrame that contains categorical data. My DataFrame looks like this: ```python import pandas as pd df = pd.DataFrame({ 'category': pd.Categorical(['A', 'B', 'A', 'C', 'B', 'C']), 'value': [10, 20, 30, 40, 50, 60] }) ``` When I try to group by the `category` column and sum the `value` column, I expect to get a consistent total for each category. However, when I execute the following code: ```python result = df.groupby('category')['value'].sum() print(result) ``` I get the output: ``` category A 40 B 70 C 100 Name: value, dtype: int64 ``` This output seems correct, but when I try to convert my `category` column to a regular object type using `df['category'] = df['category'].astype(str)`, and then run the same `groupby` operation: ```python df['category'] = df['category'].astype(str) result = df.groupby('category')['value'].sum() print(result) ``` I get an unexpected result: ``` category A 40 B 50 C 100 Name: value, dtype: int64 ``` In this case, the sum for category 'B' is incorrect. I also noticed that if I have an empty category in the categorical data, the results change further. I have tried creating the categorical data with `ordered=False` and indexing on the DataFrame using `sort_index()`, but that didn't resolve the issue. Why does converting to a string type cause such an inconsistency in the results, and how can I avoid this behavior when dealing with categorical data in Pandas? I am using pandas version 1.3.5, and I would appreciate any insights into how to handle this correctly. Thanks in advance! My development environment is Windows. How would you solve this? This is my first time working with Python 3.9.