cycquery
--------------------------------------------------------------------------------
[](https://pypi.org/project/cycquery)
[](https://github.com/VectorInstitute/cycquery/actions/workflows/code_checks.yml)
[](https://github.com/VectorInstitute/cycquery/actions/workflows/integration_tests.yml)
[](https://github.com/VectorInstitute/cycquery/actions/workflows/docs_deploy.yml)
[](https://codecov.io/gh/VectorInstitute/cycquery)
[](https://github.com/VectorInstitute/cycquery/blob/main/LICENSE)
``cycquery`` is a tool for querying relational databases using a simple Python API. It is specifically developed to query
Electronic Health Record (EHR) databases. The tool is a wrapper around [SQLAlchemy](https://www.sqlalchemy.org/) and can be used
to write SQL-like queries in Python, including joins, conditions, groupby aggregation and many more.
## 🐣 Getting Started
### Installing cycquery using pip
```bash
python3 -m pip install cycquery
```
### Query postgresql database
```python
from cycquery import DatasetQuerier
import cycquery.ops as qo
querier = DatasetQuerier(
dbms="postgresql",
port=5432,
host="localhost",
database="dbname",
user="usename",
password="password",
)
# List all tables.
querier.list_tables()
# Get some table.
table = querier.schema.sometable()
# Filter based on some condition (e.g. substring match).
table = table.ops(qo.ConditionSubstring("col1", "substr"))
# Run query to get data as a pandas dataframe.
df = table.run()
# Create a sequential list of operations to perform on the query.
ops = qo.Sequential(
qo.ConditionIn("col2", [1, 2]),
qo.DropNulls("col3"),
qo.Distinct("col1")
)
table = table.ops(ops)
# Run query to get data as a pandas dataframe.
df = table.run()
```
## 🧑🏿💻 Developing
### Using poetry
The development environment can be set up using
[poetry](https://python-poetry.org/docs/#installation). Hence, make sure it is
installed and then run:
```bash
python3 -m poetry install
source $(poetry env info --path)/bin/activate
```
In order to install dependencies for testing (codestyle, unit tests, integration tests),
run:
```bash
python3 -m poetry install --with test
```
API documentation is built using [Sphinx](https://www.sphinx-doc.org/en/master/) and
can be locally built by:
```bash
python3 -m poetry install --with docs
cd docs
make html SPHINXOPTS="-D nbsphinx_allow_errors=True"
```
### Contributing
Contributing to ``cycquery`` is welcomed.
See [Contributing](https://vectorinstitute.github.io/cycquery/api/contributing.html) for
guidelines.
## 📚 [Documentation](https://vectorinstitute.github.io/cycquery/)
Raw data
{
"_id": null,
"home_page": "https://github.com/VectorInstitute/cycquery",
"name": "cycquery",
"maintainer": null,
"docs_url": null,
"requires_python": "<3.12,>=3.9",
"maintainer_email": null,
"keywords": null,
"author": "Vector AI Engineering",
"author_email": "cyclops@vectorinstitute.ai",
"download_url": "https://files.pythonhosted.org/packages/f0/c7/b244ec3135b7d1e4fc1cd8485c2f97b86a6db1ef11d7576b8b7675ea1c2b/cycquery-0.1.7.tar.gz",
"platform": null,
"description": "cycquery\n--------------------------------------------------------------------------------\n\n[](https://pypi.org/project/cycquery)\n\n[](https://github.com/VectorInstitute/cycquery/actions/workflows/code_checks.yml)\n[](https://github.com/VectorInstitute/cycquery/actions/workflows/integration_tests.yml)\n[](https://github.com/VectorInstitute/cycquery/actions/workflows/docs_deploy.yml)\n[](https://codecov.io/gh/VectorInstitute/cycquery)\n[](https://github.com/VectorInstitute/cycquery/blob/main/LICENSE)\n\n``cycquery`` is a tool for querying relational databases using a simple Python API. It is specifically developed to query\nElectronic Health Record (EHR) databases. The tool is a wrapper around [SQLAlchemy](https://www.sqlalchemy.org/) and can be used\nto write SQL-like queries in Python, including joins, conditions, groupby aggregation and many more.\n\n## \ud83d\udc23 Getting Started\n\n### Installing cycquery using pip\n\n```bash\npython3 -m pip install cycquery\n```\n\n### Query postgresql database\n\n```python\nfrom cycquery import DatasetQuerier\nimport cycquery.ops as qo\n\n\nquerier = DatasetQuerier(\n dbms=\"postgresql\",\n port=5432,\n host=\"localhost\",\n database=\"dbname\",\n user=\"usename\",\n password=\"password\",\n)\n# List all tables.\nquerier.list_tables()\n\n# Get some table.\ntable = querier.schema.sometable()\n# Filter based on some condition (e.g. substring match).\ntable = table.ops(qo.ConditionSubstring(\"col1\", \"substr\"))\n# Run query to get data as a pandas dataframe.\ndf = table.run()\n\n# Create a sequential list of operations to perform on the query.\nops = qo.Sequential(\n\tqo.ConditionIn(\"col2\", [1, 2]),\n\tqo.DropNulls(\"col3\"),\n\tqo.Distinct(\"col1\")\n)\ntable = table.ops(ops)\n# Run query to get data as a pandas dataframe.\ndf = table.run()\n```\n\n## \ud83e\uddd1\ud83c\udfff\u200d\ud83d\udcbb Developing\n\n### Using poetry\n\nThe development environment can be set up using\n[poetry](https://python-poetry.org/docs/#installation). Hence, make sure it is\ninstalled and then run:\n\n\n```bash\npython3 -m poetry install\nsource $(poetry env info --path)/bin/activate\n```\n\nIn order to install dependencies for testing (codestyle, unit tests, integration tests),\nrun:\n\n```bash\npython3 -m poetry install --with test\n```\n\nAPI documentation is built using [Sphinx](https://www.sphinx-doc.org/en/master/) and\ncan be locally built by:\n\n```bash\npython3 -m poetry install --with docs\ncd docs\nmake html SPHINXOPTS=\"-D nbsphinx_allow_errors=True\"\n```\n\n### Contributing\n\nContributing to ``cycquery`` is welcomed.\nSee [Contributing](https://vectorinstitute.github.io/cycquery/api/contributing.html) for\nguidelines.\n\n\n## \ud83d\udcda [Documentation](https://vectorinstitute.github.io/cycquery/)\n\n",
"bugtrack_url": null,
"license": "Apache-2.0",
"summary": "A tool to query EHR databases",
"version": "0.1.7",
"project_urls": {
"Documentation": "https://vectorinstitute.github.io/cycquery/",
"Homepage": "https://github.com/VectorInstitute/cycquery",
"Repository": "https://github.com/VectorInstitute/cycquery"
},
"split_keywords": [],
"urls": [
{
"comment_text": "",
"digests": {
"blake2b_256": "2f31c76850176adf455295de1d32b3be40d6c82d2f05311d2387226f1ba11612",
"md5": "e4165c95ffcc6c6a146cb5bb26fad4d5",
"sha256": "34598b88ff8679b236cb0542af2c88e85f9d2244c619948755c64adc84e3ab33"
},
"downloads": -1,
"filename": "cycquery-0.1.7-py3-none-any.whl",
"has_sig": false,
"md5_digest": "e4165c95ffcc6c6a146cb5bb26fad4d5",
"packagetype": "bdist_wheel",
"python_version": "py3",
"requires_python": "<3.12,>=3.9",
"size": 50888,
"upload_time": "2024-04-18T16:19:04",
"upload_time_iso_8601": "2024-04-18T16:19:04.433202Z",
"url": "https://files.pythonhosted.org/packages/2f/31/c76850176adf455295de1d32b3be40d6c82d2f05311d2387226f1ba11612/cycquery-0.1.7-py3-none-any.whl",
"yanked": false,
"yanked_reason": null
},
{
"comment_text": "",
"digests": {
"blake2b_256": "f0c7b244ec3135b7d1e4fc1cd8485c2f97b86a6db1ef11d7576b8b7675ea1c2b",
"md5": "6b3f69898624c40f889d43efd3f22a77",
"sha256": "8b5371b67a1483becf3bc9520090ff39b884febac7d6b1243555be50e6bf4508"
},
"downloads": -1,
"filename": "cycquery-0.1.7.tar.gz",
"has_sig": false,
"md5_digest": "6b3f69898624c40f889d43efd3f22a77",
"packagetype": "sdist",
"python_version": "source",
"requires_python": "<3.12,>=3.9",
"size": 45095,
"upload_time": "2024-04-18T16:19:06",
"upload_time_iso_8601": "2024-04-18T16:19:06.552284Z",
"url": "https://files.pythonhosted.org/packages/f0/c7/b244ec3135b7d1e4fc1cd8485c2f97b86a6db1ef11d7576b8b7675ea1c2b/cycquery-0.1.7.tar.gz",
"yanked": false,
"yanked_reason": null
}
],
"upload_time": "2024-04-18 16:19:06",
"github": true,
"gitlab": false,
"bitbucket": false,
"codeberg": false,
"github_user": "VectorInstitute",
"github_project": "cycquery",
"travis_ci": false,
"coveralls": false,
"github_actions": true,
"lcname": "cycquery"
}
JSON
