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OMOP CDM utils in Python

Project description

pyomop SourceRank forthebadge made-with-python PyPI download total Build



The OHSDI OMOP Common Data Model allows for the systematic analysis of healthcare observational databases. This is a python library to use the CDM v6 compliant databases using SQLAlchemy as the ORM. pyomop also supports converting query results to a pandas dataframe (see below) for use in machine learning pipelines. See some useful SQL Queries here.

Installation (stable)

pip install pyomop

Installation (current)

  • git clone this repository and:
pip install -e .


from pyomop import CdmEngineFactory, CdmVocabulary, CdmVector, Cohort, Vocabulary, metadata
from sqlalchemy.sql import select
import datetime

cdm = CdmEngineFactory()  # Creates SQLite database by default

# Postgres example (db='mysql' also supported)
# cdm = CdmEngineFactory(db='pgsql', host='', port=5432,
#                       user='', pw='',
#                       name='', schema='cdm6')

engine = cdm.engine
# Create Tables if required
# Create vocabulary if required
vocab = CdmVocabulary(cdm)
# vocab.create_vocab('/path/to/csv/files')  # Uncomment to load vocabulary csv files

# SQLAlchemy as ORM
session =  cdm.session
session.add(Cohort(cohort_definition_id=2, subject_id=100,

result = session.query(Cohort).all()
for row in result:

# Convert result to a pandas dataframe
vec = CdmVector()
vec.result = result

# Execute a query and convert it to dataframe
vec.sql_df(cdm, 'TEST') # TEST is defined in
print(vec.df.dtypes) # vec.df is a pandas dataframe
# OR
vec.sql_df(cdm, query='SELECT * from cohort')
print(vec.df.dtypes) # vec.df is a pandas dataframe

command-line usage

pyomop -help


  • sqlalchemy > 1.4 may not work. See issue #6. Please downgrade sqlalchemy untill issue #6 is fixed.
pip install sqlalchemy==1.3.24

Other utils

Want to convert FHIR to pandas data frame? Try fhiry

Use the same functions in .NET and Golang!


  • Postgres
  • MySQL
  • SqLite
  • More to follow..

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