# pyTigerGraph
pyTigerGraph is a Python package for connecting to TigerGraph databases. Check out the documentation [here](https://docs.tigergraph.com/pytigergraph/current/intro/).
[](https://pepy.tech/project/pyTigergraph)
[](https://pepy.tech/project/pyTigergraph)
[](https://pepy.tech/project/pyTigergraph)
## Quickstart
### Installing pyTigerGraph
This section walks you through installing pyTigerGraph on your machine.
#### Prerequisites
* Python 3+
* If you wish to use the GDS functionality, install `torch` ahead of time.
#### Install _pyTigerGraph_
To download _pyTigerGraph_, run the following command in the command line or use the appropriate tool of your development environment (anaconda, PyCharm, etc.).:
```sh
pip3 install pyTigerGraph
```
#### Install _pyTigerGraph[gds]_
To utilize the Graph Data Science Functionality, there are a few options:
* To use the GDS functions with **PyTorch Geometric**, install `torch` and `PyTorch Geometric` according to their instructions:
1) [Install Torch](https://pytorch.org/get-started/locally/)
2) [Install PyTorch Geometric](https://pytorch-geometric.readthedocs.io/en/latest/notes/installation.html)
3) Install pyTigerGraph with:
```sh
pip3 install 'pyTigerGraph[gds]'
```
* To use the GDS functions with **DGL**, install `torch` and `dgl` according to their instructions:
1) [Install Torch](https://pytorch.org/get-started/locally/)
2) [Install DGL](https://www.dgl.ai/pages/start.html)
3) Install pyTigerGraph with:
```sh
pip3 install 'pyTigerGraph[gds]'
```
* To use the GDS functions without needing to produce output in the format supported by PyTorch Geometric or DGL.
This makes the data loaders output *Pandas dataframes*:
```sh
pip3 install 'pyTigerGraph[gds]'
```
Once the package is installed, you can import it like any other Python package:
```py
import pyTigerGraph as tg
```
### Getting Started with Core Functions
[](https://www.youtube.com/watch?v=2BcC3C-qfX4)
The video above is a good starting place for learning the core functions of pyTigerGraph. [This Google Colab notebook](https://colab.research.google.com/drive/1JhYcnGVWT51KswcXZzyPzKqCoPP5htcC) is the companion notebook to the video.
Raw data
{
"_id": null,
"home_page": "https://docs.tigergraph.com/pytigergraph/current/intro/",
"name": "pyTigerGraph",
"maintainer": null,
"docs_url": null,
"requires_python": null,
"maintainer_email": null,
"keywords": "TigerGraph, Graph Database, Data Science, Machine Learning",
"author": "TigerGraph Inc.",
"author_email": "support@tigergraph.com",
"download_url": "https://files.pythonhosted.org/packages/2f/5b/81999cc3cec78917c42a13d21c49953e4b8a84ec724cf9dfa6d4a01c4cea/pytigergraph-1.8.3.tar.gz",
"platform": null,
"description": "# pyTigerGraph\n\npyTigerGraph is a Python package for connecting to TigerGraph databases. Check out the documentation [here](https://docs.tigergraph.com/pytigergraph/current/intro/).\n\n[](https://pepy.tech/project/pyTigergraph)\n[](https://pepy.tech/project/pyTigergraph)\n[](https://pepy.tech/project/pyTigergraph)\n\n## Quickstart\n\n### Installing pyTigerGraph\nThis section walks you through installing pyTigerGraph on your machine.\n\n#### Prerequisites\n* Python 3+\n* If you wish to use the GDS functionality, install `torch` ahead of time.\n\n#### Install _pyTigerGraph_\n\nTo download _pyTigerGraph_, run the following command in the command line or use the appropriate tool of your development environment (anaconda, PyCharm, etc.).:\n\n```sh\npip3 install pyTigerGraph\n```\n\n#### Install _pyTigerGraph[gds]_\n\nTo utilize the Graph Data Science Functionality, there are a few options:\n* To use the GDS functions with **PyTorch Geometric**, install `torch` and `PyTorch Geometric` according to their instructions:\n\n 1) [Install Torch](https://pytorch.org/get-started/locally/)\n\n 2) [Install PyTorch Geometric](https://pytorch-geometric.readthedocs.io/en/latest/notes/installation.html)\n\n 3) Install pyTigerGraph with:\n ```sh\n pip3 install 'pyTigerGraph[gds]'\n ```\n\n* To use the GDS functions with **DGL**, install `torch` and `dgl` according to their instructions:\n\n 1) [Install Torch](https://pytorch.org/get-started/locally/)\n\n 2) [Install DGL](https://www.dgl.ai/pages/start.html)\n\n 3) Install pyTigerGraph with:\n ```sh\n pip3 install 'pyTigerGraph[gds]'\n ```\n\n* To use the GDS functions without needing to produce output in the format supported by PyTorch Geometric or DGL.\nThis makes the data loaders output *Pandas dataframes*:\n```sh\npip3 install 'pyTigerGraph[gds]'\n```\n\nOnce the package is installed, you can import it like any other Python package:\n\n```py\nimport pyTigerGraph as tg\n```\n### Getting Started with Core Functions\n\n[](https://www.youtube.com/watch?v=2BcC3C-qfX4)\n\nThe video above is a good starting place for learning the core functions of pyTigerGraph. [This Google Colab notebook](https://colab.research.google.com/drive/1JhYcnGVWT51KswcXZzyPzKqCoPP5htcC) is the companion notebook to the video.\n",
"bugtrack_url": null,
"license": "Apache 2",
"summary": "Library to connect to TigerGraph databases",
"version": "1.8.3",
"project_urls": {
"Bug Reports": "https://github.com/tigergraph/pyTigerGraph/issues",
"Homepage": "https://docs.tigergraph.com/pytigergraph/current/intro/",
"Source": "https://github.com/tigergraph/pyTigerGraph"
},
"split_keywords": [
"tigergraph",
" graph database",
" data science",
" machine learning"
],
"urls": [
{
"comment_text": "",
"digests": {
"blake2b_256": "df437f96581052f61640160f419ea3adfd72b89ea9845e04492f900e0b37a810",
"md5": "f5deb158ab9aab75282361b83c6b0256",
"sha256": "204c9bebcd6fc6c46d356cdfb47bbbac6b16fc93b465c4faf28e6c7ad82b98b3"
},
"downloads": -1,
"filename": "pyTigerGraph-1.8.3-py3-none-any.whl",
"has_sig": false,
"md5_digest": "f5deb158ab9aab75282361b83c6b0256",
"packagetype": "bdist_wheel",
"python_version": "py3",
"requires_python": null,
"size": 357989,
"upload_time": "2024-12-05T01:46:43",
"upload_time_iso_8601": "2024-12-05T01:46:43.838966Z",
"url": "https://files.pythonhosted.org/packages/df/43/7f96581052f61640160f419ea3adfd72b89ea9845e04492f900e0b37a810/pyTigerGraph-1.8.3-py3-none-any.whl",
"yanked": false,
"yanked_reason": null
},
{
"comment_text": "",
"digests": {
"blake2b_256": "2f5b81999cc3cec78917c42a13d21c49953e4b8a84ec724cf9dfa6d4a01c4cea",
"md5": "b91d90bd688d0e3fd5216fe97e755ce0",
"sha256": "23d611fc5bd3216bc94692167bd90cbb090f3cad56ae1411e97bbb04ba4348bf"
},
"downloads": -1,
"filename": "pytigergraph-1.8.3.tar.gz",
"has_sig": false,
"md5_digest": "b91d90bd688d0e3fd5216fe97e755ce0",
"packagetype": "sdist",
"python_version": "source",
"requires_python": null,
"size": 277696,
"upload_time": "2024-12-05T01:46:47",
"upload_time_iso_8601": "2024-12-05T01:46:47.768495Z",
"url": "https://files.pythonhosted.org/packages/2f/5b/81999cc3cec78917c42a13d21c49953e4b8a84ec724cf9dfa6d4a01c4cea/pytigergraph-1.8.3.tar.gz",
"yanked": false,
"yanked_reason": null
}
],
"upload_time": "2024-12-05 01:46:47",
"github": true,
"gitlab": false,
"bitbucket": false,
"codeberg": false,
"github_user": "tigergraph",
"github_project": "pyTigerGraph",
"travis_ci": false,
"coveralls": false,
"github_actions": true,
"lcname": "pytigergraph"
}
JSON
