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The official Python client library for [Databento](https://databento.com).
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Key features include:
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- Fast, lightweight access to both live and historical data from [multiple markets](https://docs.databento.com/knowledge-base/new-users/venues-and-publishers?historical=python&live=python).
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-[Multiple schemas](https://docs.databento.com/knowledge-base/new-users/market-data-schemas?historical=python&live=python) such as MBO, MBP, top of book, OHLCV, last sale, and more.
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-[Fully normalized](https://docs.databento.com/knowledge-base/new-users/normalization?historical=python&live=python), i.e. identical message schemas for both live and historical data, across multiple asset classes.
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- Provides mappings between different symbology systems, including [smart symbology](https://docs.databento.com/api-reference-historical/basics/symbology?historical=python&live=python) for futures rollovers.
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- Fast, lightweight access to both live and historical data from [multiple markets](https://databento.com/docs/knowledge-base/new-users/venues-and-publishers?historical=python&live=python).
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-[Multiple schemas](https://databento.com/docs/knowledge-base/new-users/market-data-schemas?historical=python&live=python) such as MBO, MBP, top of book, OHLCV, last sale, and more.
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-[Fully normalized](https://databento.com/docs/knowledge-base/new-users/normalization?historical=python&live=python), i.e. identical message schemas for both live and historical data, across multiple asset classes.
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- Provides mappings between different symbology systems, including [smart symbology](https://databento.com/docs/api-reference-historical/basics/symbology?historical=python&live=python) for futures rollovers.
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-[Point-in-time]() instrument definitions, free of look-ahead bias and retroactive adjustments.
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- Reads and stores market data in an extremely efficient file format using [Databento Binary Encoding](https://docs.databento.com/knowledge-base/new-users/dbn-encoding?historical=python&live=python).
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- Event-driven [market replay](https://docs.databento.com/api-reference-historical/helpers/bento-replay?historical=python&live=python), including at high-frequency order book granularity.
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- Support for [batch download](https://docs.databento.com/knowledge-base/new-users/stream-vs-batch?historical=python&live=python) of flat files.
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- Reads and stores market data in an extremely efficient file format using [Databento Binary Encoding](https://databento.com/docs/knowledge-base/new-users/dbn-encoding?historical=python&live=python).
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- Event-driven [market replay](https://databento.com/docs/api-reference-historical/helpers/bento-replay?historical=python&live=python), including at high-frequency order book granularity.
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- Support for [batch download](https://databento.com/docs/knowledge-base/new-users/stream-vs-batch?historical=python&live=python) of flat files.
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- Support for [pandas](https://pandas.pydata.org/docs/), CSV, and JSON.
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## Documentation
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The best place to begin is with our [Getting started](https://docs.databento.com/getting-started?historical=python&live=python) guide.
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The best place to begin is with our [Getting started](https://databento.com/docs/getting-started?historical=python&live=python) guide.
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You can find our full client API reference on the [Historical Reference](https://docs.databento.com/api-reference-historical?historical=python&live=python) and
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[Live Reference](https://docs.databento.com/reference-live?historical=python&live=python) sections of our documentation. See also the
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[Examples](https://docs.databento.com/examples?historical=python&live=python) section for various tutorials and code samples.
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You can find our full client API reference on the [Historical Reference](https://databento.com/docs/api-reference-historical?historical=python&live=python) and
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[Live Reference](https://databento.com/docs/reference-live?historical=python&live=python) sections of our documentation. See also the
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[Examples](https://databento.com/docs/examples?historical=python&live=python) section for various tutorials and code samples.
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## Requirements
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The library is fully compatible with the latest distribution of Anaconda 3.8 and above.
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The minimum dependencies as found in the `pyproject.toml` are also listed below:
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- python = "^3.8"
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- aiohttp = "^3.8.3"
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- databento-dbn = "0.16.0"
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- databento-dbn = "0.17.1"
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- numpy= ">=1.23.5"
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- pandas = ">=1.5.3"
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- pyarrow = ">=13.0.0"
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```
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Note that the API key was also passed as a parameter, which is
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[not recommended for production applications](https://docs.databento.com/knowledge-base/new-users/security-managing-api-keys?historical=python&live=python).
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[not recommended for production applications](https://databento.com/docs/knowledge-base/new-users/security-managing-api-keys?historical=python&live=python).
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Instead, you can leave out this parameter to pass your API key via the `DATABENTO_API_KEY` environment variable:
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