In Python, load the data with pandas and compute repeat customer rate from column totals for the whole period, or row by row for each month. On the sample data it returns 34.41.
Repeat customer rate = Customers who had ordered before ÷ Customers who ordered × 100
What you need
Repeat customer rate is the percentage of customers buying in a period who had bought from you before: returning buyers divided by all buyers, times 100. You need these columns, here from the sample file monthly.csv:
- df["buying_customers"]: customers who ordered
- df["repeat_customers"]: of those, customers who had ordered before
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["repeat_customer_rate"] = df["repeat_customers"] / df["buying_customers"] * 100
print(df[["month", "repeat_customer_rate"]].round(2))
# Whole period: add up the parts first, then divide
repeat_customer_rate_total = df["repeat_customers"].sum() / df["buying_customers"].sum() * 100
print(round(repeat_customer_rate_total, 2))
What you should get
Run against the sample data, the code above returns these values (in percent):
| Month | buying_customers | repeat_customers | repeat_customer_rate |
|---|---|---|---|
| 2026-01 | 640 | 210 | 32.81% |
| 2026-02 | 681 | 228 | 33.48% |
| 2026-03 | 735 | 251 | 34.15% |
| 2026-04 | 702 | 244 | 34.76% |
| 2026-05 | 768 | 270 | 35.16% |
| 2026-06 | 830 | 296 | 35.66% |
| Whole period | 4,356 | 1,499 | 34.41% |
1,499 ÷ 4,356 × 100 = 34.41%
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 repeat customer rate misleads
- Identify customers reliably. Guest checkouts with a new email look like new customers.
- Some define repeat rate as the share of all customers ever who ordered twice or more. That is a different number; say which one you use.
- A big acquisition push lowers repeat rate for a while, because the denominator fills with first-time buyers.
The full explanation, with a calculator, is on the Repeat customer rate page.
Same metric, other tools
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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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