Pandas pivot_table not aggregating properly with multiple indexes and values
I've searched everywhere and can't find a clear answer. I'm trying to create a pivot table using Pandas, but I'm running into an issue where the aggregation isn't behaving as expected. I'm using Pandas version 1.3.3 and my DataFrame looks like this: ```python import pandas as pd data = { 'Category': ['A', 'A', 'B', 'B', 'C', 'C'], 'Subcategory': ['X', 'Y', 'X', 'Y', 'X', 'Y'], 'Value': [10, 20, 30, 40, 50, 60], 'Count': [1, 2, 3, 4, 5, 6] } df = pd.DataFrame(data) ``` I want to create a pivot table that shows the sum of `Value` for each `Category` and `Subcategory`, but also includes the count of occurrences. I tried the following code: ```python pivot = df.pivot_table(index=['Category', 'Subcategory'], values=['Value', 'Count'], aggfunc={'Value': 'sum', 'Count': 'sum'}) ``` However, the resulting pivot table seems to be aggregating the counts in a way that I don't expect. Instead of summing the counts correctly, I see that the total counts are higher than expected. The output looks like this: ``` Category Subcategory Count Value A X 1 10 Y 2 20 B X 3 30 Y 4 40 C X 5 50 Y 6 60 ``` I was expecting the counts for each combination of Category and Subcategory to add up correctly, but they seem to be displaying individual values instead. I’ve also tried using `aggfunc='sum'` without specifying the dictionary, but it didn’t help either. How can I fix this to get the aggregated counts correctly alongside the summed values? I'm also unsure if I should be using `groupby` instead. Any insights would be greatly appreciated! For context: I'm using Python on Ubuntu. What am I doing wrong?