> ## Documentation Index
> Fetch the complete documentation index at: https://docs.bensevern.dev/docs/llms.txt
> Use this file to discover all available pages before exploring further.

# Mapping recipes

> Copy-paste InferMap snippets for common jobs: map a messy export to your canonical schema, reuse a saved mapping as a validation gate, and add a custom alias or domain pack.

The [config matrix](/docs/infermap/config-matrix) lists every `MapEngine` knob and vocabulary, and [Mapping](/docs/infermap/mapping) explains how the scorers combine. This page is the task-shaped shortcut: a few complete snippets for the jobs you actually reach for.

## Map a messy export to your canonical schema

**You have** a source file whose column names don't match your target. **You want** the best 1:1 mapping and the renamed frame.

```python theme={null}
import infermap
import polars as pl

result = infermap.map("crm_export.csv", "canonical_customers.csv")
for m in result.mappings:
    print(f"{m.source} -> {m.target}  ({m.confidence:.0%})  {m.reason}")

renamed = result.apply(pl.read_csv("crm_export.csv"))   # rename source columns to target names
```

Each mapping carries a confidence in `0.0..1.0` and a human-readable reason, so a low-confidence pair is easy to spot and override before you apply it.

## Reuse a saved mapping as a validation gate

**You have** recurring files from the same source. **You want** to pin the mapping once and fail fast when a new drop is missing a required field.

```python theme={null}
result = infermap.map("crm_export.csv", "canonical_customers.csv")
result.to_config("crm_mapping.yaml")            # commit this
```

Then validate each incoming file against the pinned config — non-zero exit on a missing required field makes it a CI gate:

```bash theme={null}
infermap validate new_drop.csv --config crm_mapping.yaml \
  --required first_name,last_name,email --strict
```

Apply the pinned mapping without re-inferring:

```bash theme={null}
infermap apply new_drop.csv --config crm_mapping.yaml --output clean.csv
```

## Add a custom alias or domain pack

**You have** source names your target schema doesn't know about (`cust_ref` really is `customer_id`). **You want** InferMap to resolve them without lowering `min_confidence`.

```yaml theme={null}
# mapping_rules.yaml — engine config (domains, aliases, scorer weights)
domains: [finance]
aliases:
  customer_id: [cust_ref, acct_no]
  email: [email_addr, contact_email]
scorers:
  FuzzyName:
    weight: 0.4
```

```python theme={null}
from infermap import MapEngine

engine = MapEngine(config_path="mapping_rules.yaml")   # loads domains + aliases + scorer weights
result = engine.map("crm_export.csv", "canonical_customers.csv")
```

Aliases feed the `AliasScorer` (weight 0.95), so a known alias resolves with high confidence instead of relying on the fuzzy-name tie-breaker. (Note this is the engine **config** file — distinct from the saved **mapping** an earlier `result.to_config(...)` writes, which `infermap apply`/`from_config` consume.)