In Python, load the data with pandas and compute markup from column totals for the whole period, or row by row for each month. On the sample data it returns 83.69.
Markup = (Revenue − Cost of goods sold) ÷ Cost of goods sold × 100
What you need
Markup is how much you add on top of cost, as a percentage of cost: revenue minus COGS, divided by COGS, times 100. You need these columns, here from the sample file monthly.csv:
- df["revenue"]: revenue (net of discounts)
- df["cogs"]: cost of goods sold
Step by step
- Install pandas (pip install pandas) and save monthly.csv next to your script.
- Run the script. The first print shows one row per month, the second the figure for the whole period.
- To use your own data, rename your columns to match, or change the names in the code.
The code
import pandas as pd
df = pd.read_csv("monthly.csv")
# Each month
df["markup"] = (df["revenue"] - df["cogs"]) / df["cogs"] * 100
print(df[["month", "markup"]].round(2))
# Whole period: add up the parts first, then divide
markup_total = (df["revenue"].sum() - df["cogs"].sum()) / df["cogs"].sum() * 100
print(round(markup_total, 2))
What you should get
Run against the sample data, the code above returns these values (in percent):
| Month | revenue | cogs | markup |
|---|---|---|---|
| 2026-01 | 4,12,000 | 2,26,600 | 81.82% |
| 2026-02 | 4,38,500 | 2,36,800 | 85.18% |
| 2026-03 | 4,71,200 | 2,59,200 | 81.79% |
| 2026-04 | 4,55,800 | 2,55,200 | 78.61% |
| 2026-05 | 5,02,300 | 2,71,200 | 85.21% |
| 2026-06 | 5,48,900 | 2,90,900 | 88.69% |
| Whole period | 28,28,700 | 15,39,900 | 83.69% |
(₹28,28,700 − ₹15,39,900) ÷ ₹15,39,900 × 100 = 83.69%
Notes for Python
- Dividing by zero in pandas does not raise an error: it gives inf or NaN. Check with df.replace([float("inf"), -float("inf")], float("nan")) before averaging or plotting.
- For the whole period, sum the columns first and then divide. df[col].mean() of the monthly results weights a small month the same as a large one.
- pct_change(), groupby() and resample() extend the same idea to weekly data, product categories and daily rows.
Where markup misleads
- Margin divides by revenue; markup divides by cost. Same numerator, different denominator.
- A markup applied to list price shrinks once discounts are given. Check it on realised revenue, as here.
- Markup on a single product says nothing about the blended figure across your range.
The full explanation, with a calculator, is on the Markup page.
Same metric, other tools
Related metrics in Python (pandas)
Learn more about Python
- Python and SQL for data analysis, side by side — 3 min read
- How to find out what changed in your sales data — 2 min read
About the data on this page
The figures come from a small made-up dataset, printed above in full so you can check every number by hand. Download it as monthly.csv. The results are computed from those rows when the page is built, and the Excel formulas, SQL and Python on these pages are run against the same rows before publishing, so the code and the answers agree.
To get these numbers from your own data without writing formulas, try AnalyzeIt: ask in plain words and it shows the working behind every figure.
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