In Python, load the data with pandas and compute discount rate from column totals for the whole period, or row by row for each month. On the sample data it returns 7.23.
Discount rate = Discounts ÷ (Revenue after discounts + Discounts) × 100
What you need
Discount rate is the share of gross sales you gave away as discounts: discounts divided by sales before discounts (net revenue plus discounts), times 100. You need these columns, here from the sample file monthly.csv:
- df["revenue"]: revenue (net of discounts)
- df["discounts"]: discounts given
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["discount_rate"] = df["discounts"] / (df["revenue"] + df["discounts"]) * 100
print(df[["month", "discount_rate"]].round(2))
# Whole period: add up the parts first, then divide
discount_rate_total = df["discounts"].sum() / (df["revenue"].sum() + df["discounts"].sum()) * 100
print(round(discount_rate_total, 2))
What you should get
Run against the sample data, the code above returns these values (in percent):
| Month | revenue | discounts | discount_rate |
|---|---|---|---|
| 2026-01 | 4,12,000 | 31,000 | 7.00% |
| 2026-02 | 4,38,500 | 35,200 | 7.43% |
| 2026-03 | 4,71,200 | 29,800 | 5.95% |
| 2026-04 | 4,55,800 | 41,500 | 8.35% |
| 2026-05 | 5,02,300 | 38,800 | 7.17% |
| 2026-06 | 5,48,900 | 44,100 | 7.44% |
| Whole period | 28,28,700 | 2,20,400 | 7.23% |
₹2,20,400 ÷ (₹28,28,700 + ₹2,20,400) × 100 = 7.23%
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 discount rate misleads
- The denominator is sales before discounts. Dividing by revenue after discounts overstates the rate.
- Here "discount rate" means retail discounting. In finance the same words mean the rate used to discount future cash flows, which is unrelated.
- Bundled pricing ("3 for 2") is a discount even if no coupon was used. Count it the same way every period.
The full explanation, with a calculator, is on the Discount rate page.
Same metric, other tools
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- 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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