In Python, load the data with pandas and compute cart abandonment rate from column totals for the whole period, or row by row for each month. On the sample data it returns 72.36.
Cart abandonment rate = (Carts created − Orders) ÷ Carts created × 100
What you need
Cart abandonment rate is the percentage of shopping carts that never become an order: carts created minus completed orders, divided by carts created, times 100. You need these columns, here from the sample file monthly.csv:
- df["add_to_carts"]: sessions with an add-to-cart
- df["orders"]: orders
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["cart_abandonment_rate"] = (df["add_to_carts"] - df["orders"]) / df["add_to_carts"] * 100
print(df[["month", "cart_abandonment_rate"]].round(2))
# Whole period: add up the parts first, then divide
cart_abandonment_rate_total = (df["add_to_carts"].sum() - df["orders"].sum()) / df["add_to_carts"].sum() * 100
print(round(cart_abandonment_rate_total, 2))
What you should get
Run against the sample data, the code above returns these values (in percent):
| Month | add_to_carts | orders | cart_abandonment_rate |
|---|---|---|---|
| 2026-01 | 2,480 | 702 | 71.69% |
| 2026-02 | 2,700 | 748 | 72.30% |
| 2026-03 | 2,950 | 812 | 72.47% |
| 2026-04 | 2,690 | 771 | 71.34% |
| 2026-05 | 3,150 | 846 | 73.14% |
| 2026-06 | 3,380 | 917 | 72.87% |
| Whole period | 17,350 | 4,796 | 72.36% |
(17,350 − 4,796) ÷ 17,350 × 100 = 72.36%
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 cart abandonment rate misleads
- Use sessions with a cart, not add-to-cart clicks, as the denominator.
- An order from a cart created in an earlier session still counts as a recovered cart. Over a month this evens out; over a day it does not.
- Cart abandonment and checkout abandonment are different stages. Do not compare one brand's cart figure with another's checkout figure.
The full explanation, with a calculator, is on the Cart abandonment 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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