How to Join Two CSV Files on a Shared Column

Sometimes two CSV files belong together. One file has orders and a customer ID. Another has customer names and the same ID. Joining them lets you put the matching information into one new CSV.

You do not need to copy rows by hand or build a long spreadsheet formula. You choose the matching column in each file and the tool combines the rows that belong together.

What joining two CSV files means

Think of the first CSV as your main list and the second CSV as the extra information you want to add.

For example:

Match customer_id in the first file with id in the second file.

The column names do not have to be the same. The values just need to identify the same thing on both sides.

Choose a good matching column

A useful matching column is something stable, such as:

• customer ID,
• order ID,
• product SKU,
• email address,
• another code that means the same thing in both files.

Avoid matching on names when a proper ID exists. Two people can have the same name, and the same person can appear with slightly different spelling.

Spaces around a value are ignored. Letter case is normally important, but you can choose to ignore case when ABC and abc should count as the same value.

Pick the join type you need

If you are mainly adding extra details to a main file, a left join is usually the easiest choice.

Decide what repeated keys should do

Sometimes the same matching value appears more than once in the second file.

You can choose:

• the first matching row,
• the last matching row,
• every matching row.

Choosing every match can create several output rows from one row in the first file. That is correct when the repeated rows are meaningful, but surprising when the duplicates are accidental.

If you do not expect duplicates, check the duplicate count in the result before you use the joined file.

How to join two CSV files

1. 01

   Add the left file

   Choose your main CSV and check the tool page for the current file-size limit.

2. 02

   Add the right file

   Choose the CSV that contains the information you want to match. The tool page also shows the combined-size limit.

3. 03

   Choose the matching column in each file

   The column names may be different as long as their values refer to the same thing.

4. 04

   Choose inner, left, or full join

   Pick the result that matches your goal.

5. 05

   Choose how repeated keys should be handled

   Use the first, last, or every matching row from the right file.

6. 06

   Run the join and check the counts

   Review matched and unmatched rows before downloading the new CSV.

Check the result before you use it

The result shows how many rows matched and how many did not.

If you expected every order to have a customer, but many left-side rows are unmatched, check the matching columns and the values inside them. A missing prefix, different letter case, or changed ID can stop a match.

If the result has more rows than expected, check whether the right file contains repeated keys and whether you selected every match.

Open the downloaded CSV once before replacing any original file or sending the result to another system.

What happens to your files?

Both CSV files are processed on your device. They are not uploaded to our servers.

The original files are not changed. The tool creates a separate joined CSV for you to download.

This is useful for customer lists, order exports, product catalogs, and other files you would rather not upload.

Practical limits

The tool page shows the current per-file and combined-size limits from the shipped configuration.

Files above those limits are not supported by this tool. For larger joins, use a database or another data tool designed for larger files.

Simple example

You have 500 orders in one CSV and 480 customers in another. After a left join, the result shows 492 matched orders and 8 unmatched orders.

Those 8 rows are the ones to investigate. They may contain an old customer ID, a typo, or a customer that is missing from the second file.

Result: one joined CSV, plus clear unmatched rows to review.

Join your CSV files

See the transformation

Two tables meet on one exact lookup key.

Add matching customer details to each order while keeping missing or repeated matches visible.

orders.csv · customer_id

orderkeyamount

1001C-4284.50

1002C-9142.75

customers.csv · id

keynametier

C-42Aminapro

C-42AminaVIP

Result

orderkeynametier

1001C-42Aminapro

1002C-91——