How to efficiently group by multiple columns and apply custom aggregation in Pandas?
I'm following best practices but I'm learning this framework and I've searched everywhere and can't find a clear answer... I'm currently working with a large DataFrame in Pandas (version 1.5.3) that contains sales data for different products across various regions. I need to group this data by both the 'region' and 'product_id' columns, and then apply a custom aggregation to calculate the total sales as well as the average discount applied per product in each region. Here is a snippet of the DataFrame I'm working with: ```python import pandas as pd data = { 'region': ['North', 'North', 'East', 'East', 'South', 'South'], 'product_id': [101, 102, 101, 102, 101, 102], 'sales': [200, 300, 150, 400, 500, 200], 'discount': [10, 20, 5, 10, 15, 25] } df = pd.DataFrame(data) ``` When I try to group by 'region' and 'product_id' using the following code: ```python result = df.groupby(['region', 'product_id']).agg({'sales': 'sum', 'discount': 'mean'}) ``` I expect to get a DataFrame that shows the total sales and average discount applied for each product in each region. However, I'm running into performance optimization because my DataFrame can grow quite large, with millions of rows. I also explored using `Dask` for parallel processing, but I'm unclear how to correctly implement the groupby aggregation using it. Here’s what I tried with Dask: ```python import dask.dataframe as dd ddf = dd.from_pandas(df, npartitions=4) result = ddf.groupby(['region', 'product_id']).agg({'sales': 'sum', 'discount': 'mean'}).compute() ``` The above Dask code runs without behavior, but I noticed it's still not performing as optimally as I would like, especially with larger datasets. How can I optimize this grouping and aggregation process in Pandas or Dask? Are there any best practices I should follow for efficient aggregation on large DataFrames? For context: I'm using Python on Ubuntu. Thanks in advance! Could someone point me to the right documentation?