How to group by multiple columns and calculate custom metrics in Pandas?
I'm updating my dependencies and After trying multiple solutions online, I still can't figure this out. I'm stuck on something that should probably be simple. I'm working with a DataFrame in Pandas (version 1.3.3) that contains sales data, and I need to group the data by multiple columns to calculate some custom metrics. Specifically, I'm trying to group by 'region' and 'product', then calculate the total sales and the average discount for each group. However, I'm running into issues when trying to apply multiple aggregation functions together. Here’s what I’ve tried: ```python import pandas as pd data = { 'region': ['North', 'South', 'North', 'South', 'East', 'East'], 'product': ['A', 'A', 'B', 'B', 'A', 'B'], 'sales': [250, 150, 300, 200, 100, 300], 'discount': [10, 20, 10, 15, 20, 30] } df = pd.DataFrame(data) # Attempting to group by region and product and calculate total sales and average discount result = df.groupby(['region', 'product']).agg({ 'sales': 'sum', 'discount': 'mean' }) print(result) ``` This produces the following output: ``` sales discount region product East A 100 20.0 B 300 30.0 North A 250 10.0 B 300 NaN South A 150 20.0 B 200 15.0 ``` The issue arises with rows where there are no sales for a specific product in a region (like 'North' for product 'B'). I expected to see zero for those products instead of NaN. I tried filling NaN values using `fillna(0)`, but that doesn't seem to work as expected in this context. Is there a way to ensure that I get a complete table with zeros for those combinations? Any help would be appreciated! This is part of a larger application I'm building. Am I missing something obvious? For context: I'm using Python on macOS. Am I missing something obvious? I'm on macOS using the latest version of Python. Has anyone dealt with something similar? This is part of a larger REST API I'm building. I'd be grateful for any help.