Group, Join and Chart

Summarise with groupby

groupby is the pandas version of a pivot table. Total revenue by year:

orders.groupby("year")["revenue"].sum()

Revenue by month, then the best and worst months:

monthly = orders.groupby("month")["revenue"].sum()
monthly.idxmax(), monthly.max()   # December 2025 was the best month
monthly.idxmin(), monthly.min()

Join tables with merge

The orders table only has product_id and customer_id. The names, categories and regions live in two other tables. Load them and merge:

base = "https://academy.cloudtechanalytics.com/datasets/sales/"
products = pd.read_csv(base + "products.csv")
customers = pd.read_csv(base + "customers.csv")

sales = (
    orders
    .merge(products, on="product_id")
    .merge(customers, on="customer_id")
)
sales.columns

merge matches rows where the key column has the same value, like a lookup in Excel.

Answer business questions

sales.groupby("category")["revenue"].sum().sort_values(ascending=False)

sales.groupby("region")["revenue"].sum().sort_values(ascending=False)

Lagos brings in ₦411,162,300, about half of all revenue, followed by South West and North West.

Make charts

pandas can draw charts directly:

by_region = sales.groupby("region")["revenue"].sum().sort_values()
by_region.plot(kind="barh", title="Revenue by region (₦)")

monthly.plot(title="Monthly revenue (₦)")

Use kind="barh" for comparing categories and a line (the default) for trends over time.

Share your analysis

Add text cells to explain what you found in plain words, not just code. Then save the notebook to GitHub (File → Save a copy in GitHub) or share the Colab link. That's a portfolio project.

Try it

1. Merge the three tables into sales.
2. Find revenue by channel. Which channel is biggest?
3. Find the three products with the highest revenue.
4. Make one bar chart and one line chart, and write three sentences summarising what they show.