{"id":26475825,"url":"https://github.com/databento/databento-python","last_synced_at":"2026-04-08T02:01:43.865Z","repository":{"id":40996330,"uuid":"440757282","full_name":"databento/databento-python","owner":"databento","description":"The official Python client library for 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databento-python\n\n[![test](https://github.com/databento/databento-python/actions/workflows/test.yml/badge.svg?branch=dev)](https://github.com/databento/databento-python/actions/workflows/test.yml)\n![python](https://img.shields.io/pypi/pyversions/databento.svg)\n[![pypi-version](https://img.shields.io/pypi/v/databento)](https://pypi.org/project/databento)\n[![license](https://img.shields.io/github/license/databento/databento-python?color=blue)](./LICENSE)\n[![code-style: black](https://img.shields.io/badge/code%20style-black-000000.svg)](https://github.com/psf/black)\n[![Slack](https://img.shields.io/badge/join_Slack-community-darkblue.svg?logo=slack)](https://to.dbn.to/slack)\n\nThe official Python client library for [Databento](https://databento.com).\n\nKey features include:\n- Fast, lightweight access to both live and historical data from [multiple markets](https://databento.com/docs/faqs/venues-and-publishers).\n- [Multiple schemas](https://databento.com/docs/schemas-and-data-formats/whats-a-schema?historical=python\u0026live=python) such as MBO, MBP, top of book, OHLCV, last sale, and more.\n- [Fully normalized](https://databento.com/docs/standards-and-conventions/normalization?historical=python\u0026live=python), i.e. identical message schemas for both live and historical data, across multiple asset classes.\n- Provides mappings between different symbology systems, including [smart symbology](https://databento.com/docs/api-reference-historical/basics/symbology?historical=python\u0026live=python) for futures rollovers.\n- [Point-in-time]() instrument definitions, free of look-ahead bias and retroactive adjustments.\n- Reads and stores market data in an extremely efficient file format using [Databento Binary Encoding](https://databento.com/docs/standards-and-conventions/databento-binary-encoding?historical=python\u0026live=python).\n- Event-driven [market replay](https://databento.com/docs/api-reference-historical/helpers/bento-replay?historical=python\u0026live=python), including at high-frequency order book granularity.\n- Support for [batch download](https://databento.com/docs/faqs/streaming-vs-batch-download?historical=python\u0026live=python) of flat files.\n- Support for [pandas](https://pandas.pydata.org/docs/), CSV, and JSON.\n\n## Documentation\nThe best place to begin is with our [Getting started](https://databento.com/docs/quickstart?historical=python\u0026live=python) guide.\n\nYou can find our full client API reference on the [Historical Reference](https://databento.com/docs/api-reference-historical?historical=python\u0026live=python) and\n[Live Reference](https://databento.com/docs/reference-live?historical=python\u0026live=python) sections of our documentation. See also the\n[Examples](https://databento.com/docs/examples?historical=python\u0026live=python) section for various tutorials and code samples.\n\n## Requirements\nThe library is fully compatible with distributions of Anaconda 2023.x and above.\nThe minimum dependencies as found in the `pyproject.toml` are also listed below:\n- python = \"^3.10\"\n- aiohttp = \"^3.8.3\"\n- databento-dbn = \"~0.53.0\"\n- numpy = \"\u003e=1.23.5\"\n- pandas = \"\u003e=1.5.3\"\n- pip-system-certs = \"\u003e=4.0\" (Windows only)\n- pyarrow = \"\u003e=13.0.0\"\n- requests = \"\u003e=2.25.1\"\n- zstandard = \"\u003e=0.21.0\"\n\n## Installation\nTo install the latest stable version of the package from PyPI:\n\n    pip install -U databento\n\n## Usage\nThe library needs to be configured with an API key from your account.\n[Sign up](https://databento.com/signup) for free and you will automatically\nreceive a set of API keys to start with. Each API key is a 32-character\nstring starting with `db-`, that can be found on the API Keys page of your [Databento user portal](https://databento.com/portal/keys).\n\nA simple Databento application looks like this:\n\n```python\nimport databento as db\n\nclient = db.Historical('YOUR_API_KEY')\ndata = client.timeseries.get_range(\n    dataset='GLBX.MDP3',\n    symbols='ES.FUT',\n    stype_in='parent',\n    start='2022-06-10T14:30',\n    end='2022-06-10T14:40',\n)\n\ndata.replay(callback=print)  # market replay, with `print` as event handler\n```\n\nReplace `YOUR_API_KEY` with an actual API key, then run this program.\n\nThis uses `.replay()` to access the entire block of data\nand dispatch each data event to an event handler. You can also use\n`.to_df()` or `.to_ndarray()` to cast the data into a Pandas `DataFrame` or numpy `ndarray`:\n\n```python\ndf = data.to_df()  # to DataFrame\narray = data.to_ndarray()  # to ndarray\n```\n\nNote that the API key was also passed as a parameter, which is\n[not recommended for production applications](https://databento.com/docs/portal/api-keys?historical=python\u0026live=python).\nInstead, you can leave out this parameter to pass your API key via the `DATABENTO_API_KEY` environment variable:\n\n```python\nimport databento as db\n\n# Pass as parameter\nclient = db.Historical('YOUR_API_KEY')\n\n# Or, pass as `DATABENTO_API_KEY` environment variable\nclient = db.Historical()\n```\n\n## License\nDistributed under the [Apache 2.0 License](https://www.apache.org/licenses/LICENSE-2.0.html).\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fdatabento%2Fdatabento-python","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fdatabento%2Fdatabento-python","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fdatabento%2Fdatabento-python/lists"}