---
id: icd-mappings
version: "0.6.2"
license: MIT
license_treatment: permissive
maintenance: active
---

# icd-mappings — This python tool enables a variety of mappings between ICD diagnostic codes (International Classification of Diseases) with a single line of code.

License: permissive · Maintenance: active · Downloads: 210.3K/mo

## What it is and what it does

ICD-Mappings is a Python library that translates between ICD diagnostic code systems and clinical classification ontologies. It provides two main classes: Mapper for converting codes between systems (ICD-9 CM to ICD-10 CM, ICD-9 to CCS, ICD-10 to CCSR, and several others), and Validator for checking whether a code is valid within a given ontology. The library handles both single codes and iterables like lists or pandas Series, returning None for unmapped or invalid codes.

The package bundles pre-computed mappings for multiple clinical classification systems including Clinical Classification Software (CCS and CCSR), Chronic Condition Indicators (CCI and CCIR), and Pediatric Complex Chronic Conditions categories. It supports parent-code inference for certain targets (CCI and CCIR) by default, allowing truncated codes to map when all their children agree. Data was last refreshed on 2026-04-27 for ICD-10 chapter and block ranges.

Use it for:

- Convert ICD-9 diagnostic codes to ICD-10 equivalents for healthcare data migration or compliance with updated coding standards.
- Classify ICD codes into broader clinical categories (CCS) to aggregate diagnoses for epidemiological analysis or quality reporting.
- Identify chronic conditions from diagnostic codes using CCI or CCIR for risk stratification or comorbidity tracking.
- Validate incoming ICD-9 or ICD-10 diagnostic and procedure codes in healthcare data pipelines before processing.
- Map pediatric diagnoses to complex chronic condition categories for specialized pediatric population studies.
- Batch-process healthcare claims or EHR data to standardize coding across multiple ICD versions.

## Worth the install?

AI-flagged interpretation of the facts above — verify before relying.

Maps ICD diagnostic codes between versions (ICD-9 to ICD-10 and vice versa) and to clinical classification systems like CCS, CCI, and pediatric complex chronic conditions categories with a single function call.

Yes. The package is actively maintained, has no known vulnerabilities, low install friction, and solves a real problem in healthcare data workflows. It is well-suited for anyone working with ICD codes in Python, from data analysts to healthcare IT teams. The MIT license poses no restrictions for typical use.

## Install

pip install icd-mappings
uv add icd-mappings
poetry add icd-mappings

## Installing icd-mappings

Before you install: Low friction: pure Python wheel with only importlib-resources as a runtime dependency. Active maintenance with a recent release on 2026-04-27 and last commit on 2026-06-25.

License in practice: MIT license permits commercial and private use, modification, and distribution with minimal restrictions—suitable for most healthcare applications.

Quickstart:

pip install icd-mappings

from icdmappings import Mapper

mapper = Mapper()
result = mapper.map('29410', source='icd9', target='ccs')
print(result)  # '653'

Verify before relying:

- Whether mappings reflect the most current ICD-10 and clinical classification standards beyond the 2026-04-27 extraction date
- Performance characteristics when mapping large batches of codes (thousands or millions)
- Completeness of coverage for all valid ICD-9 and ICD-10 diagnostic codes in the underlying datasets

## Package facts

- License: MIT (permissive)
- Python support: supports_current
- Install friction: low
- Maintenance: active
- Downloads: 210.3K/month (top 15,000 on PyPI)
- Known vulnerabilities: none known

## Tags

ICD code mapping, ICD-9 to ICD-10 conversion, medical code classification, clinical classification software, ICD diagnostic code validator, chronic condition indicator, healthcare code translation, healthcare-data, medical-coding, icd-classification

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