In Python, load the data with pandas and compute purchase frequency from column totals for the whole period, or row by row for each month. On the sample data it returns 1.10.
Purchase frequency = Number of orders ÷ Number of unique customers who ordered
What you need
Purchase frequency is the average number of orders each customer placed in a period: total orders divided by the number of distinct customers who ordered. You need these columns, here from the sample file monthly.csv:
- df["orders"]: orders
- df["buying_customers"]: customers who ordered
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["purchase_frequency"] = df["orders"] / df["buying_customers"]
print(df[["month", "purchase_frequency"]].round(2))
# Whole period: add up the parts first, then divide
purchase_frequency_total = df["orders"].sum() / df["buying_customers"].sum()
print(round(purchase_frequency_total, 2))
What you should get
Run against the sample data, the code above returns these values:
| Month | orders | buying_customers | purchase_frequency |
|---|---|---|---|
| 2026-01 | 702 | 640 | 1.10 |
| 2026-02 | 748 | 681 | 1.10 |
| 2026-03 | 812 | 735 | 1.10 |
| 2026-04 | 771 | 702 | 1.10 |
| 2026-05 | 846 | 768 | 1.10 |
| 2026-06 | 917 | 830 | 1.10 |
| Whole period | 4,796 | 4,356 | 1.10 |
4,796 ÷ 4,356 = 1.10
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 purchase frequency misleads
- Count unique customers, not customer records. Guest checkouts with the same email or phone are often the same person.
- Adding monthly figures together does not give a yearly frequency, because the same customer appears in several months.
- Promotions pull orders forward. A spike in frequency during a sale is often followed by a dip.
The full explanation, with a calculator, is on the Purchase frequency page.
Same metric, other tools
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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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