Import Mappings

Import mappings are reusable rules for files that follow a familiar pattern.

They matter most when you import from the same bank, card, payroll export, or spreadsheet layout more than once.

Where you manage them #

Import mappings live in Settings > Features > Mass Transaction Entry.

That section includes an Import Mappings manager where you can:

• create a mapping from clipboard data
• edit an existing mapping
• delete a mapping from the current file

If Mass Transaction Entry is disabled, the mapping manager stays unavailable.

Why mappings matter #

If you import the same kind of file more than once, a saved mapping can turn a tedious repeat job into a quick review-and-save workflow.

What a mapping stores #

A mapping can remember:

• the mapping name
• the expected number of source columns
• the source headers, when the file has a real header row
• the header row position
• the row where actual data starts
• which target field each source column should fill
• how amount signs should be interpreted for amount columns
• any Teach It rules used to extract or normalize values

The main target fields are:

• date
• account
• payee
• category
• label, when Labels is enabled
• amount
• note

Columns you do not need can simply be ignored.

Create a mapping from real sample data #

The manager uses Create Mapping from Clipboard.

That means the easiest way to make a good mapping is:

1. copy a realistic sample of rows from the source file
2. open Settings > Mass Transaction Entry > Import Mappings
3. choose Create Mapping from Clipboard
4. confirm the header row and data start row
5. assign target fields and teach any columns that need cleanup

If the clipboard does not contain CSV or tab-delimited rows, Common Cents will not open the mapping editor from that action.

Header rows and preamble rows matter #

Many bank exports do not start with the real table immediately. They may include title rows, generated-on lines, or blank rows before the actual header.

The mapping editor lets you set both:

• which row is the header row
• which row the real data starts on

That matters because a good mapping should skip the report clutter and land on the actual transaction rows.

How compatible mappings are found #

When you import a new dataset, Common Cents looks for saved mappings that fit that layout.

In practice, that means it compares things like:

• how many source columns the dataset has
• whether the detected headers match a saved headered mapping
• whether a headerless mapping still fits the layout well enough to reuse

If one or more mappings fit, Common Cents lets you choose which one to apply.

A good mapping is practical #

The goal is not to build a perfect theoretical parser. The goal is to reduce repeated cleanup for the real files you actually receive.

If a mapping saves you from fixing the same five columns every month, it is already doing its job.

For the part of a mapping that learns how to clean up text inside a column, continue with Teach It.