Grouping and calculating multiple columns
Now that we have the basics down, let’s take a look at a pd.DataFrame that contains more columns of data. Generally, your pd.DataFrame objects will contain many columns with potentially different data types, so knowing how to select and work with them all through the context of pd.core.groupby.DataFrameGroupBy is important.
How to do it
Let’s create a pd.DataFrame that shows the sales and returns of a hypothetical widget across different region and month values:
df = pd.DataFrame([
["North", "Widget A", "Jan", 10, 2],
["North", "Widget B", "Jan", 4, 0],
["South", "Widget A", "Jan", 8, 3],
["South", "Widget B", "Jan", 12, 8],
["North", "Widget A", "Feb", 3, 0],
["North", "Widget B", "Feb", 7, 0],
["South"...
