How to implement guide with pandas groupby returning unexpected nan values for aggregation functions
I'm attempting to set up I'm currently working with a DataFrame in Pandas (version 1.3.3) where I need to group data by a specific column and apply aggregation functions. However, I'm working with unexpected `NaN` values when I use the `agg()` function. The DataFrame looks like this: ```python import pandas as pd data = { 'category': ['A', 'A', 'B', 'B', 'C'], 'value': [1, 2, None, 4, 5] } df = pd.DataFrame(data) ``` When I try to group by the `category` column and calculate the sum for the `value` column like this: ```python grouped = df.groupby('category').agg({'value': 'sum'}) print(grouped) ``` I expect to see sums for each category but instead, I'm getting: ``` value category A 3.0 B 4.0 C 5.0 ``` This looks correct for categories A, B, and C, but I'm puzzled because I was expecting the sum for category B, which contains `None`, to somehow handle this gracefully. I also tried using `min()` and `max()` aggregations and still see `NaN` for category B: ```python grouped_min = df.groupby('category').agg({'value': 'min'}) print(grouped_min) ``` The output is: ``` value category A 1.0 B NaN C 5.0 ``` I want to understand why I'm seeing these `NaN` values and how to handle them effectively. I've looked at the documentation but I'm still confused. Shouldn't the aggregation functions ignore `None` values? What am I missing here, and how can I ensure that all categories have valid numeric outputs? Thanks in advance! The project is a microservice built with Python. Any examples would be super helpful.