Python lesson
Several totals at once
Lagos: 264 in total, 52.8 an order, 5 orders. Abuja: 195, 65 and 3. To split further, group by two columns in a list.
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.agg takes a list of summaries and gives a column for each:
df.groupby("Store")["Revenue"].agg(["sum", "mean", "count"])sum mean count
Abuja 195 65 3
Lagos 264 52.8 5Key points
.agg(["sum", "mean", "count"])A list of summaries, one column for each.
groupby(["Store", "Product"])Two columns in a list: a group for every store and product pair.
Same shapedf.groupby(group)[column], then .agg([...]) or one summary.
Put together: df.groupby("Store")["Revenue"].agg(["sum", "mean", "count"]).
Practice
1. Build a line that shows the total, average and count of revenue for each store.
df.groupby("Store")["Revenue"].agg(["sum", "mean", "count"])2. Build a line that totals revenue for each store and product pair.
df.groupby(["Store", "Product"])["Revenue"].sum()3. Find the total, average and count of revenue for each store.
df.groupby("Store")["Revenue"].agg(["sum", "mean", "count"])4. Find the total quantity sold for each store and product pair.
df.groupby(["Store", "Product"])["Qty"].sum()5. Your manager asks for each product's biggest and smallest order. Show the max and min revenue for each product.
df.groupby("Product")["Revenue"].agg(["max", "min"])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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