Pandas: guide with aggregating multiple columns using groupby and custom aggregation functions
I'm a bit lost with I'm trying to aggregate a DataFrame using `groupby` with multiple columns and custom aggregation functions, but I keep working with unexpected results. Here's a snippet of the DataFrame I'm working with: ```python import pandas as pd data = { 'category': ['A', 'A', 'B', 'B', 'C'], 'value1': [10, 20, 30, 40, 50], 'value2': [1, 2, 3, 4, 5] } df = pd.DataFrame(data) ``` I want to group by `category` and aggregate `value1` by summing it, while for `value2`, I want to calculate the mean. I expected the following result: ``` category value1 value2 0 A 30 1.5 1 B 70 3.5 2 C 50 5.0 ``` However, when I try to perform the aggregation like this: ```python df_grouped = df.groupby('category').agg({ 'value1': 'sum', 'value2': 'mean' }) ``` I get a DataFrame that looks correct, but when I try to print it out with `print(df_grouped)`, the output doesn't seem to reflect the expected mean for `value2`. Instead, I see the following: ``` value1 value2 category A 30 2.0 B 70 4.0 C 50 5.0 ``` Notice that the mean for `value2` seems to be incorrect. I also tried resetting the index with `df_grouped.reset_index()`, but the mean doesn't change. I am using pandas version 1.5.0, and I suspect that it may have something to do with how the aggregation is processed. Can someone explain what I'm missing or if there's a bug in the way `agg` handles multiple functions in this case? For context: I'm using Python on macOS. Any ideas what could be causing this? Thanks, I really appreciate it!