How to calculate click-to-open rate in Python (pandas)

What you need

Click-to-open rate (CTOR) is the percentage of people who opened an email and then clicked a link in it: unique clicks divided by unique opens, times 100. You need these columns, here from the sample file monthly.csv:

• df["email_opened"]: emails opened
• df["email_clicked"]: emails clicked

Step by step

1. Install pandas (pip install pandas) and save monthly.csv next to your script.
2. Run the script. The first print shows one row per month, the second the figure for the whole period.
3. 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["ctor"] = df["email_clicked"] / df["email_opened"] * 100
print(df[["month", "ctor"]].round(2))

# Whole period: add up the parts first, then divide
ctor_total = df["email_clicked"].sum() / df["email_opened"].sum() * 100
print(round(ctor_total, 2))

What you should get

Run against the sample data, the code above returns these values (in percent):

6,650 ÷ 53,130 × 100 = 12.52%

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 click-to-open rate misleads

• Use unique counts on both sides.
• Low opens can make CTOR jumpy; check the counts behind it.
• It does not measure revenue; follow clicks through to orders where you can.

The full explanation, with a calculator, is on the Click-to-open rate (CTOR) page.

Same metric, other tools

• Click-to-open rate in Excel and Google Sheets
• Click-to-open rate in SQL

Related metrics in Python (pandas)

• How to calculate email open rate in Python (pandas)
• How to calculate email click-through rate 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.