A Python script that turns your contact exports into a clean CSV ready to import into your mailing service

Example result

python merge_sales.py --month 2026-10

2026-11-02 09:14:03 INFO  Folder: sales/2026-10, 9 files found
2026-11-02 09:14:03 INFO  shop-01.csv: 1,284 rows
2026-11-02 09:14:04 INFO  shop-02.xlsx: 967 rows
2026-11-02 09:14:04 WARN  shop-07.csv: 3 rows without a price skipped (lines 88, 412, 913)
2026-11-02 09:14:05 INFO  Done: 10,412 rows -> report-2026-10.xlsx (sheets "Sales" and "Totals")

[columns]
date = Date, Sale date, Day
product = Product, Item, Name
quantity = Qty, Quantity, Pcs
price = Price, Unit price

def read_shop_file(path: Path, columns: dict[str, list[str]]) -> pd.DataFrame:
    """Reads one shop's file and gives it the report's column names."""
    table = pd.read_excel(path) if path.suffix == ".xlsx" else pd.read_csv(path, encoding="utf-8-sig")
    renamed = {}
    for name, variants in columns.items():
        found = next((c for c in table.columns if c.strip() in variants), None)
        if found is None:
            raise ReportError(f"{path.name}: no column for '{name}' (looked for {', '.join(variants)})")
        renamed[found] = name
    table = table.rename(columns=renamed)[list(columns)]
    table["shop"] = path.stem
    return table

About this service

Contacts exported from a shop, a CRM or event sign-ups rarely match what a mailing service wants to import: other column names, first and last name in one cell, tags in odd places, people who unsubscribed still on the list. This Python script reads your exports and writes one clean CSV in your service's import layout: names split if needed, fields and tags mapped, malformed emails and duplicates left out and, from Standard, only people with a consent mark kept and unsubscribed ones removed, with a file of dropped rows for you to check. It is for your own contacts who agreed to hear from you, never bought or scraped lists. It runs on your computer and does not connect to the service; you import the file yourself.

What is included

• What you getA ZIP archive with the Python script, a README with step-by-step instructions and, from Standard, a settings file with field and tag mapping; the code is also shown on the order page.
• Your service's layoutBy its import template or sample file
• Consent respectedFrom Standard, only people who agreed, no unsubscribed
• Duplicates and typos outMalformed addresses and repeats left out
• Dropped rows keptA separate file showing what was left out and why
• Readable codeWith comments, easy to add a field later

How it works

1. 1
   SamplesYou send made-up rows from each export and your service's import format
2. 2
   CodeWe write the script and the guide to running it
3. 3
   Try itYou run it, look through the dropped rows and import a small test list
4. 4
   ChangesYou ask for changes within your package and send any error message

What we need from you

The header and 10-20 rows of each export with made-up names and emails but the same layout, which column shows consent and which shows unsubscribing, the mailing service you use and its import template or a link to its import help page, and how tags or segments should be set. How the contacts were collected (your shop, sign-up form, events). Never send the real list.

We will ask you:

• Where do the contacts come from?
• Which mailing service will you import into?
• How is consent recorded?
• Do you need tags or segments?

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Questions and answers

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