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

# Python SDK - Mapping concepts across vocabularies

> Map concepts between OMOP vocabularies using the OMOPHub Python SDK - crosswalk SNOMED, ICD-10, LOINC, and RxNorm codes from Python applications.

## Get Mappings for a Concept

Find how a concept maps to other vocabularies:

```python theme={null}
result = client.mappings.get(201826)  # Type 2 diabetes mellitus
for mapping in result["data"]["mappings"]:
    print(f"{mapping['relationship_id']}: {mapping['target_concept_name']}")
```

## Filter by Target Vocabulary

Get mappings to a specific vocabulary only:

```python theme={null}
result = client.mappings.get(
    201826,
    target_vocabulary="ICD10CM",
)
for mapping in result["data"]["mappings"]:
    print(f"{mapping['target_concept_id']}: {mapping['target_concept_name']}")
```

## Include Invalid Mappings

Include deprecated or invalid mappings in results:

```python theme={null}
result = client.mappings.get(
    201826,
    target_vocabulary="ICD10CM",
    include_invalid=True,
)
```

## Use Specific Vocabulary Version

Query mappings from a specific vocabulary release:

```python theme={null}
result = client.mappings.get(
    201826,
    target_vocabulary="ICD10CM",
    vocab_release="2025.1",
)
```

## Map Multiple Concepts

Map a batch of concept IDs to a target vocabulary:

```python theme={null}
result = client.mappings.map(
    target_vocabulary="ICD10CM",
    source_concepts=[201826, 4329847],  # Diabetes, MI
)
for mapping in result["data"]["mappings"]:
    source = mapping["source_concept_id"]
    target = mapping["target_concept_id"]
    print(f"{source} -> {target}")
```

## Map Using Vocabulary Codes

Map concepts directly using vocabulary codes instead of OMOP concept IDs:

```python theme={null}
result = client.mappings.map(
    target_vocabulary="RxNorm",
    source_codes=[
        {"vocabulary_id": "SNOMED", "concept_code": "387517004"},  # Acetaminophen
        {"vocabulary_id": "SNOMED", "concept_code": "108774000"},  # Anastrozole
    ],
)
for mapping in result["data"]["mappings"]:
    print(f"{mapping['source_concept_name']} -> {mapping['target_concept_name']}")
```

<Note>
  Use `source_codes` when you have vocabulary-specific codes (e.g., SNOMED codes from your source system).
  Use `source_concepts` when you already have OMOP concept IDs.
  You cannot use both parameters in the same request.
</Note>

## Map with Specific Mapping Type

Filter mappings by type:

```python theme={null}
result = client.mappings.map(
    target_vocabulary="ICD10CM",
    source_concepts=[201826, 192671],
    mapping_type="direct",  # "direct", "equivalent", "broader", "narrower"
)
```

## Common Use Case: SNOMED to ICD-10

**Option 1: Direct mapping using vocabulary codes (recommended)**

```python theme={null}
result = client.mappings.map(
    target_vocabulary="ICD10CM",
    source_codes=[
        {"vocabulary_id": "SNOMED", "concept_code": "44054006"},  # Type 2 diabetes
    ],
)
for m in result["data"]["mappings"]:
    print(f"  -> {m['target_concept_name']} ({m['target_concept_code']})")
```

**Option 2: Using OMOP concept IDs**

```python theme={null}
# First get the OMOP concept ID
concept = client.concepts.get_by_code("SNOMED", "44054006")

# Then map using the concept ID
mappings = client.mappings.get(
    concept["concept_id"],
    target_vocabulary="ICD10CM",
)

print(f"Mapping {concept['concept_name']}:")
for m in mappings["data"]["mappings"]:
    print(f"  -> {m['target_concept_name']} (confidence: {m['confidence']})")
```