Clean, Filter and Calculate

Fix the date column

Dates loaded as text can't be sorted or grouped by month properly. Convert them:

orders["order_date"] = pd.to_datetime(orders["order_date"])
orders.info()

order_date is now datetime64. You can pull out parts of the date:

orders["year"] = orders["order_date"].dt.year
orders["month"] = orders["order_date"].dt.to_period("M")

Add a revenue column

Revenue for each line is quantity × unit price, minus the discount:

orders["revenue"] = (
    orders["quantity"] * orders["unit_price"] * (1 - orders["discount_pct"] / 100)
)
orders["revenue"].sum()

Total revenue is ₦830,541,245. Notice there's no loop: pandas does the maths for every row at once.

Filter rows

Put a condition inside square brackets to keep only the matching rows:

big = orders[orders["quantity"] >= 20]
len(big)                  # 1,032 lines
big["revenue"].sum()

Combine conditions with & (and) or | (or), with brackets around each one:

discounted_2026 = orders[(orders["discount_pct"] > 0) & (orders["year"] == 2026)]

Sort

orders.sort_values("revenue", ascending=False).head(5)

This shows the five biggest order lines. Use ascending=True (the default) for smallest first.

Check for problems

Real data is rarely this clean. These checks are worth running on any dataset:

orders.isna().sum()          # missing values per column
orders.duplicated().sum()    # fully duplicated rows
(orders["quantity"] <= 0).sum()  # impossible values

This dataset passes all three, but make it a habit.

Try it

1. Convert order_date to a date and add year, month and revenue columns.
2. Check the total revenue is ₦830,541,245.
3. How many order lines had a 10% discount? How much revenue did they bring in?
4. Show the 10 biggest order lines from 2026.