Column Mapping

For file-based sources (CSV/XLSX), DataPorter adds an interactive mapping step between upload and parsing. Users see their file's actual column headers and map each one to a target field via dropdowns.

File Header          Target Field
+-----------+        +---------------+
| Prenom    |   ->   | First Name  v |
+-----------+        +---------------+
+-----------+        +---------------+
| Nom       |   ->   | Last Name   v |
+-----------+        +---------------+

Dropdowns are pre-filled from the Target's csv_mapping when headers match. Users can adjust any mapping before continuing to the preview step.

Required fields

Required target fields are marked with * in the dropdown labels. If any required field is left unmapped, a warning banner appears listing the missing fields. This validation is client-side only -- it warns but does not block submission.

Duplicate detection

If two file headers are mapped to the same target field, the affected rows are highlighted with an orange border and a warning message appears. This helps catch accidental duplicate mappings before parsing.

Mapping Templates

Mappings can be saved as reusable templates. When starting a new import, users select a saved template from a dropdown to auto-fill all column mappings at once. Templates are stored per-target, so each import type has its own template library.

Managing templates

• Inline: Check "Save as template" in the mapping form and give it a name
• CRUD: Use the "Mapping Templates" link on the imports index page to create, edit, and delete templates

When a template is loaded, the "Save as template" checkbox is hidden since the user is already working from an existing template.

Mapping Priority

When parsing, mappings are resolved in priority order:

1. User mapping -- from the mapping UI (config["column_mapping"])
2. Code mapping -- from the Target DSL (csv_mapping)
3. Auto-map -- parameterize headers to match column names

Non-file sources (JSON, API) skip the mapping step entirely.