| Name | viadot2 JSON |
| Version |
2.2.10
JSON |
| download |
| home_page | None |
| Summary | A simple data ingestion library to guide data flows from some places to other places. |
| upload_time | 2025-07-22 10:20:29 |
| maintainer | None |
| docs_url | None |
| author | None |
| requires_python | >=3.10 |
| license | None |
| keywords |
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| VCS |
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| bugtrack_url |
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| requirements |
No requirements were recorded.
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# Viadot
[](https://rye.astral.sh)
[](https://img.shields.io/badge/style-ruff-41B5BE?style=flat)
---
**Documentation**: <a href="https://dyvenia.github.io/viadot/" target="_blank">https://viadot.docs.dyvenia.com</a>
**Source Code**: <a href="https://github.com/dyvenia/viadot/tree/main" target="_blank">https://github.com/dyvenia/viadot/tree/main</a>
---
A simple data ingestion library to guide data flows from some places to other places.
## Getting Data from a Source
Viadot supports several API and RDBMS sources, private and public. Currently, we support the UK Carbon Intensity public API and base the examples on it.
```python
from viadot.sources.uk_carbon_intensity import UKCarbonIntensity
ukci = UKCarbonIntensity()
ukci.query("/intensity")
df = ukci.to_df()
print(df)
```
**Output:**
| | from | to | forecast | actual | index |
| ---: | :---------------- | :---------------- | -------: | -----: | :------- |
| 0 | 2021-08-10T11:00Z | 2021-08-10T11:30Z | 211 | 216 | moderate |
The above `df` is a pandas `DataFrame` object. It contains data downloaded by `viadot` from the Carbon Intensity UK API.
## Loading data to a destination
Depending on the destination, `viadot` provides different methods of uploading data. For instance, for databases, this would be bulk inserts. For data lakes, it would be file uploads.
For example:
```python hl_lines="2 8-9"
from viadot.sources import UKCarbonIntensity
from viadot.sources import AzureDataLake
ukci = UKCarbonIntensity()
ukci.query("/intensity")
df = ukci.to_df()
adls = AzureDataLake(config_key="my_adls_creds")
adls.from_df(df, "my_folder/my_file.parquet")
```
## Getting started
### Prerequisites
We use [Rye](https://rye-up.com/). You can install it like so:
```console
curl -sSf https://rye.astral.sh/get | bash
```
### Installation
```console
pip install viadot2
```
### Configuration
In order to start using sources, you must configure them with required credentials. Credentials can be specified either in the viadot config file (by default, `$HOME/.config/viadot/config.yaml`), or passed directly to each source's `credentials` parameter.
You can find specific information about each source's credentials in [the documentation](https://viadot.docs.dyvenia.com/references/sources/sql_sources).
### Next steps
Check out the [documentation](https://viadot.docs.dyvenia.com) for more information on how to use `viadot`.
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"description": "# Viadot\n\n[](https://rye.astral.sh)\n[](https://img.shields.io/badge/style-ruff-41B5BE?style=flat)\n\n---\n\n**Documentation**: <a href=\"https://dyvenia.github.io/viadot/\" target=\"_blank\">https://viadot.docs.dyvenia.com</a>\n\n**Source Code**: <a href=\"https://github.com/dyvenia/viadot/tree/main\" target=\"_blank\">https://github.com/dyvenia/viadot/tree/main</a>\n\n---\n\nA simple data ingestion library to guide data flows from some places to other places.\n\n## Getting Data from a Source\n\nViadot supports several API and RDBMS sources, private and public. Currently, we support the UK Carbon Intensity public API and base the examples on it.\n\n```python\nfrom viadot.sources.uk_carbon_intensity import UKCarbonIntensity\n\nukci = UKCarbonIntensity()\nukci.query(\"/intensity\")\ndf = ukci.to_df()\n\nprint(df)\n```\n\n**Output:**\n| | from | to | forecast | actual | index |\n| ---: | :---------------- | :---------------- | -------: | -----: | :------- |\n| 0 | 2021-08-10T11:00Z | 2021-08-10T11:30Z | 211 | 216 | moderate |\n\nThe above `df` is a pandas `DataFrame` object. It contains data downloaded by `viadot` from the Carbon Intensity UK API.\n\n## Loading data to a destination\n\nDepending on the destination, `viadot` provides different methods of uploading data. For instance, for databases, this would be bulk inserts. For data lakes, it would be file uploads.\n\nFor example:\n\n```python hl_lines=\"2 8-9\"\nfrom viadot.sources import UKCarbonIntensity\nfrom viadot.sources import AzureDataLake\n\nukci = UKCarbonIntensity()\nukci.query(\"/intensity\")\ndf = ukci.to_df()\n\nadls = AzureDataLake(config_key=\"my_adls_creds\")\nadls.from_df(df, \"my_folder/my_file.parquet\")\n```\n\n## Getting started\n\n### Prerequisites\n\nWe use [Rye](https://rye-up.com/). You can install it like so:\n\n```console\ncurl -sSf https://rye.astral.sh/get | bash\n```\n\n### Installation\n\n```console\npip install viadot2\n```\n\n### Configuration\n\nIn order to start using sources, you must configure them with required credentials. Credentials can be specified either in the viadot config file (by default, `$HOME/.config/viadot/config.yaml`), or passed directly to each source's `credentials` parameter.\n\nYou can find specific information about each source's credentials in [the documentation](https://viadot.docs.dyvenia.com/references/sources/sql_sources).\n\n### Next steps\n\nCheck out the [documentation](https://viadot.docs.dyvenia.com) for more information on how to use `viadot`.\n",
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