whylogs


Namewhylogs JSON
Version 1.3.28 PyPI version JSON
download
home_pagehttps://docs.whylabs.ai
SummaryProfile and monitor your ML data pipeline end-to-end
upload_time2024-03-26 23:52:04
maintainerNone
docs_urlNone
authorWhyLabs.ai
requires_python<4,>=3.7.1
licenseApache-2.0
keywords
VCS
bugtrack_url
requirements No requirements were recorded.
Travis-CI No Travis.
coveralls test coverage No coveralls.
            <img src="https://static.scarf.sh/a.png?x-pxid=bc3c57b0-9a65-49fe-b8ea-f711c4d35b82" /><p align="center">
<img src="https://i.imgur.com/nv33goV.png" width="35%"/>
</br>

<h1 align="center">The open standard for data logging

 </h1>
  <h3 align="center">
   <a href="https://whylogs.readthedocs.io/"><b>Documentation</b></a> &bull;
   <a href="https://bit.ly/whylogsslack"><b>Slack Community</b></a> &bull;
   <a href="https://github.com/whylabs/whylogs#python-quickstart"><b>Python Quickstart</b></a> &bull;
   <a href="https://whylogs.readthedocs.io/en/latest/examples/integrations/writers/Writing_to_WhyLabs.html"><b>WhyLabs Quickstart</b></a>
 </h3>

<p align="center">
<a href="https://github.com/whylabs/whylogs-python/blob/mainline/LICENSE" target="_blank">
    <img src="http://img.shields.io/:license-Apache%202-blue.svg" alt="License">
</a>
<a href="https://badge.fury.io/py/whylogs" target="_blank">
    <img src="https://badge.fury.io/py/whylogs.svg" alt="PyPi Version">
</a>
<a href="https://github.com/python/black" target="_blank">
    <img src="https://img.shields.io/badge/code%20style-black-000000.svg" alt="Code style: black">
</a>
<a href="https://pepy.tech/project/whylogs" target="_blank">
    <img src="https://pepy.tech/badge/whylogs" alt="PyPi Downloads">
</a>
<a href="bit.ly/whylogs" target="_blank">
    <img src="https://github.com/whylabs/whylogs-python/workflows/whylogs%20CI/badge.svg" alt="CI">
</a>
<a href="https://codeclimate.com/github/whylabs/whylogs-python/maintainability" target="_blank">
    <img src="https://api.codeclimate.com/v1/badges/442f6ca3dca1e583a488/maintainability" alt="Maintainability">
</a>
</p>

## What is whylogs

whylogs is an open source library for logging any kind of data. With whylogs, users are able to generate summaries of their datasets (called _whylogs profiles_) which they can use to:

1. Track changes in their dataset
2. Create _data constraints_ to know whether their data looks the way it should
3. Quickly visualize key summary statistics about their datasets

These three functionalities enable a variety of use cases for data scientists, machine learning engineers, and data engineers:

- Detect data drift in model input features
- Detect training-serving skew, concept drift, and model performance degradation
- Validate data quality in model inputs or in a data pipeline
- Perform exploratory data analysis of massive datasets
- Track data distributions & data quality for ML experiments
- Enable data auditing and governance across the organization
- Standardize data documentation practices across the organization
- And more

## Quickstart

Install whylogs using the pip package manager in a terminal by running:

```
pip install whylogs
```

Then you can log data in python as simply as this:

```python
import whylogs as why
import pandas as pd

df = pd.read_csv("path/to/file.csv")
results = why.log(df)
```

And voilĂ , you now have a whylogs profile. To learn more about what a whylogs profile is and what you can do with it, check out our [docs](https://whylogs.readthedocs.io/en/latest/) and our [examples](https://github.com/whylabs/whylogs/tree/mainline/python/examples).


            

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    "description": "<img src=\"https://static.scarf.sh/a.png?x-pxid=bc3c57b0-9a65-49fe-b8ea-f711c4d35b82\" /><p align=\"center\">\n<img src=\"https://i.imgur.com/nv33goV.png\" width=\"35%\"/>\n</br>\n\n<h1 align=\"center\">The open standard for data logging\n\n </h1>\n  <h3 align=\"center\">\n   <a href=\"https://whylogs.readthedocs.io/\"><b>Documentation</b></a> &bull;\n   <a href=\"https://bit.ly/whylogsslack\"><b>Slack Community</b></a> &bull;\n   <a href=\"https://github.com/whylabs/whylogs#python-quickstart\"><b>Python Quickstart</b></a> &bull;\n   <a href=\"https://whylogs.readthedocs.io/en/latest/examples/integrations/writers/Writing_to_WhyLabs.html\"><b>WhyLabs Quickstart</b></a>\n </h3>\n\n<p align=\"center\">\n<a href=\"https://github.com/whylabs/whylogs-python/blob/mainline/LICENSE\" target=\"_blank\">\n    <img src=\"http://img.shields.io/:license-Apache%202-blue.svg\" alt=\"License\">\n</a>\n<a href=\"https://badge.fury.io/py/whylogs\" target=\"_blank\">\n    <img src=\"https://badge.fury.io/py/whylogs.svg\" alt=\"PyPi Version\">\n</a>\n<a href=\"https://github.com/python/black\" target=\"_blank\">\n    <img src=\"https://img.shields.io/badge/code%20style-black-000000.svg\" alt=\"Code style: black\">\n</a>\n<a href=\"https://pepy.tech/project/whylogs\" target=\"_blank\">\n    <img src=\"https://pepy.tech/badge/whylogs\" alt=\"PyPi Downloads\">\n</a>\n<a href=\"bit.ly/whylogs\" target=\"_blank\">\n    <img src=\"https://github.com/whylabs/whylogs-python/workflows/whylogs%20CI/badge.svg\" alt=\"CI\">\n</a>\n<a href=\"https://codeclimate.com/github/whylabs/whylogs-python/maintainability\" target=\"_blank\">\n    <img src=\"https://api.codeclimate.com/v1/badges/442f6ca3dca1e583a488/maintainability\" alt=\"Maintainability\">\n</a>\n</p>\n\n## What is whylogs\n\nwhylogs is an open source library for logging any kind of data. With whylogs, users are able to generate summaries of their datasets (called _whylogs profiles_) which they can use to:\n\n1. Track changes in their dataset\n2. Create _data constraints_ to know whether their data looks the way it should\n3. Quickly visualize key summary statistics about their datasets\n\nThese three functionalities enable a variety of use cases for data scientists, machine learning engineers, and data engineers:\n\n- Detect data drift in model input features\n- Detect training-serving skew, concept drift, and model performance degradation\n- Validate data quality in model inputs or in a data pipeline\n- Perform exploratory data analysis of massive datasets\n- Track data distributions & data quality for ML experiments\n- Enable data auditing and governance across the organization\n- Standardize data documentation practices across the organization\n- And more\n\n## Quickstart\n\nInstall whylogs using the pip package manager in a terminal by running:\n\n```\npip install whylogs\n```\n\nThen you can log data in python as simply as this:\n\n```python\nimport whylogs as why\nimport pandas as pd\n\ndf = pd.read_csv(\"path/to/file.csv\")\nresults = why.log(df)\n```\n\nAnd voil\u00e0, you now have a whylogs profile. To learn more about what a whylogs profile is and what you can do with it, check out our [docs](https://whylogs.readthedocs.io/en/latest/) and our [examples](https://github.com/whylabs/whylogs/tree/mainline/python/examples).\n\n",
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