# mltable: machine learning table data toolkit
MLTable is a Python package that provides fast, flexible data loading functions designed to make accessing "tabular" data easy and intuitive. MLTable will help you to abstract the schema definition for tabular data so that it is easier to materialize the table into a Pandas dataframe.
MlTable can be leveraged upon delimited text files, parquet files, delta lake, json-lines files from a cloud object store or local disk.
## Main Features
Here are a few things that mltable does well:
- Flexible **sampling and filtering** functionality on large data
- Robust IO tools for loading data from **flat** files (CSV and delimited), **parquet** files, **delta lake** and **json-lines files**
- Capturing and defining **schema** contained in flat files
- Fast materialization of data into Pandas DataFrame
## Getting started
You can install MLTable package via pip.
```bash
pip install mltable
```
Please note MLTable package is pre-installed on AzureML compute instances.
## Documentation
The official documentation is hosted on [working with tables](https://learn.microsoft.com/en-us/azure/machine-learning/how-to-mltable?view=azureml-api-2&tabs=cli).
MLTable artifact’s metadata file is called MLTable which adheres to the [AzureML MLTable schema](https://learn.microsoft.com/en-us/azure/machine-learning/reference-yaml-mltable).
# Release History
## 1.6.1 (2024-01-24)
### Features Added
- added authrization support
- `MLTable.save()` bug fixes
## 1.5.0 (2023-08-14)
### Features Added
- `MLTable.save()` supports cloud storage. Please find more details [here](https://learn.microsoft.com/en-us/azure/machine-learning/how-to-mltable?view=azureml-api-2&tabs=cli).
- `from_delta_lake` supports pulling latest version by default
### Bugs Fixed
- Fix `support_multi_line` issue for `MLTable.from_delimited_files`
## 1.4.1 (2023-06-19)
### Bugs Fixed
- Relaxing cryptography library dependency to allow versions greater than 41.*.*
## 1.4.0 (2023-05-31)
### Features Added
- Updating runtime dependencies
- Improved error handling and argument validation
## 1.3.0 (2023-04-07)
### Features Added
- bugfix (user error mapping, mltable save/load roundtrip)
## 1.2.0 (2023-02-22)
### Features Added
- bugfix (mltable save/load, validation schema)
## 1.1.0 (2023-01-26)
### Features Added
- bugfix (fix schema, flake8 errors)
- improve logging and exception message
## 1.0.0 (2022-12-05)
### Features Added
- factory apis(from_delta_lake)
- Authoring apis(convert_column_types, save, skip etc)
## 0.1.0b4 (2022-10-05)
### Features Added
- Factory apis(from_paths, from_delimited_files, from_parquet_files, from_json_lines_files).
- Authoring apis(keep_columns, drop_columns, take_random_sample, take etc).
- Support mltable load from data asset uri
## 0.1.0b3 (2022-06-30)
## 0.1.0b2 (2022-05-23)
## 0.1.0b1 (2022-05-17)
### Features Added
- Initial public preview release to load into pandas dataframe
Raw data
{
"_id": null,
"home_page": "https://docs.microsoft.com/python/api/overview/azure/ml/?view=azure-ml-py",
"name": "mltable",
"maintainer": "",
"docs_url": null,
"requires_python": ">=3.6,< 4.0",
"maintainer_email": "",
"keywords": "",
"author": "Microsoft Corp",
"author_email": "",
"download_url": "",
"platform": null,
"description": "# mltable: machine learning table data toolkit \r\nMLTable is a Python package that provides fast, flexible data loading functions designed to make accessing \"tabular\" data easy and intuitive. MLTable will help you to abstract the schema definition for tabular data so that it is easier to materialize the table into a Pandas dataframe. \r\nMlTable can be leveraged upon delimited text files, parquet files, delta lake, json-lines files from a cloud object store or local disk. \r\n\r\n## Main Features \r\n\r\nHere are a few things that mltable does well: \r\n\r\n- Flexible **sampling and filtering** functionality on large data \r\n\r\n- Robust IO tools for loading data from\u202f **flat** files\u202f(CSV and delimited),\u202f**parquet** files, **delta lake** and **json-lines files** \r\n\r\n- Capturing and defining **schema** contained in flat files \r\n\r\n- Fast materialization of data into Pandas DataFrame\r\n\r\n## Getting started \r\n\r\nYou can install MLTable package via pip.\r\n```bash\r\npip install mltable\r\n``` \r\n\r\nPlease note MLTable package is pre-installed on AzureML compute instances. \r\n\r\n## Documentation \r\n\r\nThe official documentation is hosted on [working with tables](https://learn.microsoft.com/en-us/azure/machine-learning/how-to-mltable?view=azureml-api-2&tabs=cli). \r\n\r\nMLTable artifact\u2019s metadata file is called \u202fMLTable\u202fwhich adheres to the\u202f[AzureML MLTable schema](https://learn.microsoft.com/en-us/azure/machine-learning/reference-yaml-mltable). \r\n\r\n# Release History\r\n\r\n## 1.6.1 (2024-01-24)\r\n### Features Added\r\n- added authrization support\r\n- `MLTable.save()` bug fixes\r\n\r\n## 1.5.0 (2023-08-14)\r\n### Features Added\r\n- `MLTable.save()` supports cloud storage. Please find more details [here](https://learn.microsoft.com/en-us/azure/machine-learning/how-to-mltable?view=azureml-api-2&tabs=cli).\r\n- `from_delta_lake` supports pulling latest version by default\r\n\r\n### Bugs Fixed\r\n- Fix `support_multi_line` issue for `MLTable.from_delimited_files`\r\n\r\n## 1.4.1 (2023-06-19)\r\n### Bugs Fixed\r\n- Relaxing cryptography library dependency to allow versions greater than 41.*.*\r\n\r\n## 1.4.0 (2023-05-31)\r\n### Features Added\r\n- Updating runtime dependencies\r\n- Improved error handling and argument validation\r\n\r\n## 1.3.0 (2023-04-07)\r\n### Features Added\r\n- bugfix (user error mapping, mltable save/load roundtrip)\r\n\r\n## 1.2.0 (2023-02-22)\r\n\r\n### Features Added\r\n- bugfix (mltable save/load, validation schema)\r\n\r\n## 1.1.0 (2023-01-26)\r\n\r\n### Features Added\r\n- bugfix (fix schema, flake8 errors)\r\n- improve logging and exception message\r\n\r\n## 1.0.0 (2022-12-05)\r\n\r\n### Features Added\r\n- factory apis(from_delta_lake)\r\n- Authoring apis(convert_column_types, save, skip etc)\r\n\r\n## 0.1.0b4 (2022-10-05)\r\n\r\n### Features Added\r\n- Factory apis(from_paths, from_delimited_files, from_parquet_files, from_json_lines_files).\r\n- Authoring apis(keep_columns, drop_columns, take_random_sample, take etc).\r\n- Support mltable load from data asset uri\r\n\r\n## 0.1.0b3 (2022-06-30)\r\n\r\n## 0.1.0b2 (2022-05-23)\r\n\r\n## 0.1.0b1 (2022-05-17)\r\n\r\n### Features Added\r\n- Initial public preview release to load into pandas dataframe\r\n",
"bugtrack_url": null,
"license": "Proprietary https://aka.ms/azureml-preview-sdk-license",
"summary": "Contains MLTable loading and authoring apis for the mltable package.",
"version": "1.6.1",
"project_urls": {
"Homepage": "https://docs.microsoft.com/python/api/overview/azure/ml/?view=azure-ml-py"
},
"split_keywords": [],
"urls": [
{
"comment_text": "",
"digests": {
"blake2b_256": "eafd5b8e59c886a9ae05a726621597484e828e9727ba37d6adf87d87f5073b78",
"md5": "c8e58ae1584bed6999aa5b2d6c9896c2",
"sha256": "7b8cb29c116b85f3f8ade180123d1847dc803c0a10ef353ec64382ba12dfb9db"
},
"downloads": -1,
"filename": "mltable-1.6.1-py3-none-any.whl",
"has_sig": false,
"md5_digest": "c8e58ae1584bed6999aa5b2d6c9896c2",
"packagetype": "bdist_wheel",
"python_version": "py3",
"requires_python": ">=3.6,< 4.0",
"size": 189364,
"upload_time": "2024-01-24T22:46:58",
"upload_time_iso_8601": "2024-01-24T22:46:58.276400Z",
"url": "https://files.pythonhosted.org/packages/ea/fd/5b8e59c886a9ae05a726621597484e828e9727ba37d6adf87d87f5073b78/mltable-1.6.1-py3-none-any.whl",
"yanked": false,
"yanked_reason": null
}
],
"upload_time": "2024-01-24 22:46:58",
"github": false,
"gitlab": false,
"bitbucket": false,
"codeberg": false,
"lcname": "mltable"
}
JSON