Python lesson
groupby, part 1
Read it left to right: group by Store, pick the Revenue column, add up each group. Abuja made 195 and Lagos 264. It's a pivot table in one line.
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groupby splits the rows into groups, then you summarise each group:
df.groupby("Store")["Revenue"].sum()Abuja 195
Lagos 264Key points
groupby("Store")Split the rows into groups, one per store.
["Revenue"]Pick the column to summarise.
.sum()Add up each group.
Put together: df.groupby("Product")["Revenue"].sum() gives each product's total.
Practice
1. Build a line that totals revenue for each product.
df.groupby("Product")["Revenue"].sum()2. Build a line that totals quantity for each store.
df.groupby("Store")["Qty"].sum()3. Find the total quantity sold for each product.
df.groupby("Product")["Qty"].sum()4. Find the total revenue for each store.
df.groupby("Store")["Revenue"].sum()5. Find the total revenue for each day.
df.groupby("Day")["Revenue"].sum()Learn it by doing. This lesson is a short hands-on level on Bampaa: see it work, then build and write it yourself, with instant feedback.
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