| Name | lydata JSON |
| Version |
0.3.1
JSON |
| download |
| home_page | None |
| Summary | Library for handling lymphatic involvement data |
| upload_time | 2025-07-12 09:54:10 |
| maintainer | None |
| docs_url | None |
| author | None |
| requires_python | >=3.10 |
| license | MIT |
| keywords |
data
lymph
involvement
|
| VCS |
 |
| bugtrack_url |
|
| requirements |
No requirements were recorded.
|
| Travis-CI |
No Travis.
|
| coveralls test coverage |
No coveralls.
|
# Python Library for Loading and Manipulating lyDATA Tables
[](https://github.com/lycosystem/lydata-package/actions/workflows/release.yml)
[](https://github.com/lycosystem/lydata-package/actions/workflows/tests.yml)
[](https://lydata.readthedocs.io/stable/?badge=stable)
[](https://htmlpreview.github.io/?https://github.com/lycosystem/lydata-package/blob/python-coverage-comment-action-data/htmlcov/index.html)
This repository provides a Python library for loading, manipulating, and validating the datasets available on [lyDATA](https://github.com/lycosystem/lydata).
> [!WARNING]
> This Python library is still highly experimental!
>
> Also, it has recently been spun off from the repository of datasets, [lyDATA](https://github.com/lycosystem/lydata), and some things might still not work as expected.
## Installation
### 1. Install from PyPI
You can install the library from PyPI using pip:
```bash
pip install lydata
```
### 2. Install from Source
If you want to install the library from source, you can clone the repository and install it using pip:
```bash
git clone https://github.com/lycosystem/lydata-package
cd lydata-package
pip install -e .
```
## Usage
The first and most common use case would probably listing and loading the published datasets:
```python
>>> import lydata
>>> for dataset_spec in lydata.available_datasets(
... year=2023, # show all datasets added in 2023
... ref="61a17e", # may be some specific hash/tag/branch
... ):
... print(dataset_spec.name)
2023-clb-multisite
2023-isb-multisite
# return generator of datasets that include oropharyngeal tumor patients
>>> first_dataset = next(lydata.load_datasets(subsite="oropharynx"))
>>> print(first_dataset.head())
... # doctest: +ELLIPSIS, +NORMALIZE_WHITESPACE
patient ... positive_dissected
# ... contra
id institution sex ... III IV V
0 P011 Centre Léon Bérard male ... 0.0 0.0 0.0
1 P012 Centre Léon Bérard female ... 0.0 0.0 0.0
2 P014 Centre Léon Bérard male ... 0.0 0.0 NaN
3 P015 Centre Léon Bérard male ... 0.0 0.0 NaN
4 P018 Centre Léon Bérard male ... NaN NaN NaN
[5 rows x 82 columns]
```
And since the three-level header of the tables is a little unwieldy at times, we also provide some shortcodes via a custom pandas accessor. As soon as `lydata` is imported it can be used like this:
```python
>>> print(first_dataset.ly.age)
... # doctest: +ELLIPSIS, +NORMALIZE_WHITESPACE
0 67
1 62
...
261 60
262 60
Name: (patient, #, age), Length: 263, dtype: int64
```
And we have implemented `Q` and `C` objects inspired by Django that allow easier querying of the tables:
```python
>>> from lydata import C
# select patients younger than 50 that are not HPV positive (includes NaNs)
>>> query_result = first_dataset.ly.query((C("age") < 50) & ~(C("hpv") == True))
>>> (query_result.ly.age < 50).all()
np.True_
>>> (query_result.ly.hpv == False).all()
np.True_
```
For more details and further examples or use-cases, have a look at the [official documentation](https://lydata.readthedocs.org/)
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"description": "# Python Library for Loading and Manipulating lyDATA Tables\n\n[](https://github.com/lycosystem/lydata-package/actions/workflows/release.yml)\n[](https://github.com/lycosystem/lydata-package/actions/workflows/tests.yml)\n[](https://lydata.readthedocs.io/stable/?badge=stable)\n[](https://htmlpreview.github.io/?https://github.com/lycosystem/lydata-package/blob/python-coverage-comment-action-data/htmlcov/index.html)\n\nThis repository provides a Python library for loading, manipulating, and validating the datasets available on [lyDATA](https://github.com/lycosystem/lydata).\n\n> [!WARNING]\n> This Python library is still highly experimental!\n>\n> Also, it has recently been spun off from the repository of datasets, [lyDATA](https://github.com/lycosystem/lydata), and some things might still not work as expected.\n\n## Installation\n\n### 1. Install from PyPI\n\nYou can install the library from PyPI using pip:\n\n```bash\npip install lydata\n```\n\n### 2. Install from Source\n\nIf you want to install the library from source, you can clone the repository and install it using pip:\n\n```bash\ngit clone https://github.com/lycosystem/lydata-package\ncd lydata-package\npip install -e .\n```\n\n## Usage\n\nThe first and most common use case would probably listing and loading the published datasets:\n\n```python\n>>> import lydata\n>>> for dataset_spec in lydata.available_datasets(\n... year=2023, # show all datasets added in 2023\n... ref=\"61a17e\", # may be some specific hash/tag/branch\n... ):\n... print(dataset_spec.name)\n2023-clb-multisite\n2023-isb-multisite\n\n# return generator of datasets that include oropharyngeal tumor patients\n>>> first_dataset = next(lydata.load_datasets(subsite=\"oropharynx\"))\n>>> print(first_dataset.head())\n... # doctest: +ELLIPSIS, +NORMALIZE_WHITESPACE\n patient ... positive_dissected\n # ... contra\n id institution sex ... III IV V\n0 P011 Centre L\u00e9on B\u00e9rard male ... 0.0 0.0 0.0\n1 P012 Centre L\u00e9on B\u00e9rard female ... 0.0 0.0 0.0\n2 P014 Centre L\u00e9on B\u00e9rard male ... 0.0 0.0 NaN\n3 P015 Centre L\u00e9on B\u00e9rard male ... 0.0 0.0 NaN\n4 P018 Centre L\u00e9on B\u00e9rard male ... NaN NaN NaN\n[5 rows x 82 columns]\n\n```\n\nAnd since the three-level header of the tables is a little unwieldy at times, we also provide some shortcodes via a custom pandas accessor. As soon as `lydata` is imported it can be used like this:\n\n```python\n>>> print(first_dataset.ly.age)\n... # doctest: +ELLIPSIS, +NORMALIZE_WHITESPACE\n0 67\n1 62\n ...\n261 60\n262 60\nName: (patient, #, age), Length: 263, dtype: int64\n\n```\n\nAnd we have implemented `Q` and `C` objects inspired by Django that allow easier querying of the tables:\n\n```python\n>>> from lydata import C\n\n# select patients younger than 50 that are not HPV positive (includes NaNs)\n>>> query_result = first_dataset.ly.query((C(\"age\") < 50) & ~(C(\"hpv\") == True))\n>>> (query_result.ly.age < 50).all()\nnp.True_\n>>> (query_result.ly.hpv == False).all()\nnp.True_\n\n```\n\nFor more details and further examples or use-cases, have a look at the [official documentation](https://lydata.readthedocs.org/)\n",
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