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MLOps / LLMOps \u0026 Production","Feature Engineering"],"sub_categories":["Miscellaneous Tools","Data Pre-processing \u0026 Loading","Python","Useful Python Tools for Data Analysis"],"readme":"\u003cp align=\"center\"\u003e\n\u003cimg width=50% src=\"https://www.featuretools.com/wp-content/uploads/2017/12/FeatureLabs-Logo-Tangerine-800.png\" alt=\"Featuretools\" /\u003e\n\u003c/p\u003e\n\u003cp align=\"center\"\u003e\n\u003ci\u003e\"One of the holy grails of machine learning is to automate more and more of the feature engineering process.\"\u003c/i\u003e ― Pedro Domingos, \u003ca href=\"https://bit.ly/things_to_know_ml\"\u003eA Few Useful Things to Know about Machine Learning\u003c/a\u003e\n\u003c/p\u003e\n\n\u003cp align=\"center\"\u003e\n    \u003ca href=\"https://github.com/alteryx/featuretools/actions/workflows/tests_with_latest_deps.yaml\" alt=\"Tests\" target=\"_blank\"\u003e\n        \u003cimg src=\"https://github.com/alteryx/featuretools/actions/workflows/tests_with_latest_deps.yaml/badge.svg?branch=main\" alt=\"Tests\" /\u003e\n    \u003c/a\u003e\n    \u003ca href=\"https://codecov.io/gh/alteryx/featuretools\"\u003e\n        \u003cimg src=\"https://codecov.io/gh/alteryx/featuretools/branch/main/graph/badge.svg\"/\u003e\n    \u003c/a\u003e\n    \u003ca href='https://featuretools.alteryx.com/en/stable/?badge=stable'\u003e\n        \u003cimg src='https://readthedocs.com/projects/feature-labs-inc-featuretools/badge/?version=stable' alt='Documentation Status' /\u003e\n    \u003c/a\u003e\n    \u003ca href=\"https://badge.fury.io/py/featuretools\" target=\"_blank\"\u003e\n        \u003cimg src=\"https://badge.fury.io/py/featuretools.svg?maxAge=2592000\" alt=\"PyPI Version\" /\u003e\n    \u003c/a\u003e\n    \u003ca href=\"https://anaconda.org/conda-forge/featuretools\" target=\"_blank\"\u003e\n        \u003cimg src=\"https://anaconda.org/conda-forge/featuretools/badges/version.svg\" alt=\"Anaconda Version\" /\u003e\n    \u003c/a\u003e\n    \u003ca href=\"https://stackoverflow.com/questions/tagged/featuretools\" target=\"_blank\"\u003e\n        \u003cimg src=\"http://img.shields.io/badge/questions-on_stackoverflow-blue.svg\" alt=\"StackOverflow\" /\u003e\n    \u003c/a\u003e\n    \u003ca href=\"https://pepy.tech/project/featuretools\" target=\"_blank\"\u003e\n        \u003cimg src=\"https://static.pepy.tech/badge/featuretools/month\" alt=\"PyPI Downloads\" /\u003e\n    \u003c/a\u003e\n\u003c/p\u003e\n\u003chr\u003e\n\n[Featuretools](https://www.featuretools.com) is a python library for automated feature engineering. See the [documentation](https://docs.featuretools.com) for more information.\n\n## Installation\nInstall with pip\n\n```\npython -m pip install featuretools\n```\n\nor from the Conda-forge channel on [conda](https://anaconda.org/conda-forge/featuretools):\n\n```\nconda install -c conda-forge featuretools\n```\n\n### Add-ons\n\nYou can install add-ons individually or all at once by running:\n\n```\npython -m pip install \"featuretools[complete]\"\n```\n\n**Premium Primitives** - Use Premium Primitives from the premium-primitives repo\n\n```\npython -m pip install \"featuretools[premium]\"\n```\n\n**NLP Primitives** - Use Natural Language Primitives from the nlp-primitives repo\n\n```\npython -m pip install \"featuretools[nlp]\"\n```\n\n**Dask Support** - Use Dask to run DFS with njobs \u003e 1\n\n```\npython -m pip install \"featuretools[dask]\"\n```\n\n## Example\nBelow is an example of using Deep Feature Synthesis (DFS) to perform automated feature engineering. In this example, we apply DFS to a multi-table dataset consisting of timestamped customer transactions.\n\n```python\n\u003e\u003e import featuretools as ft\n\u003e\u003e es = ft.demo.load_mock_customer(return_entityset=True)\n\u003e\u003e es.plot()\n```\n\n\u003cimg src=\"https://github.com/alteryx/featuretools/blob/main/docs/source/_static/images/entity_set.png?raw=true\" width=\"350\"\u003e\n\nFeaturetools can automatically create a single table of features for any \"target dataframe\"\n```python\n\u003e\u003e feature_matrix, features_defs = ft.dfs(entityset=es, target_dataframe_name=\"customers\")\n\u003e\u003e feature_matrix.head(5)\n```\n\n```\n            zip_code  COUNT(transactions)  COUNT(sessions)  SUM(transactions.amount) MODE(sessions.device)  MIN(transactions.amount)  MAX(transactions.amount)  YEAR(join_date)  SKEW(transactions.amount)  DAY(join_date)                   ...                     SUM(sessions.MIN(transactions.amount))  MAX(sessions.SKEW(transactions.amount))  MAX(sessions.MIN(transactions.amount))  SUM(sessions.MEAN(transactions.amount))  STD(sessions.SUM(transactions.amount))  STD(sessions.MEAN(transactions.amount))  SKEW(sessions.MEAN(transactions.amount))  STD(sessions.MAX(transactions.amount))  NUM_UNIQUE(sessions.DAY(session_start))  MIN(sessions.SKEW(transactions.amount))\ncustomer_id                                                                                                                                                                                                                                  ...\n1              60091                  131               10                  10236.77               desktop                      5.60                    149.95             2008                   0.070041               1                   ...                                                     169.77                                 0.610052                                   41.95                               791.976505                              175.939423                                 9.299023                                 -0.377150                                5.857976                                        1                                -0.395358\n2              02139                  122                8                   9118.81                mobile                      5.81                    149.15             2008                   0.028647              20                   ...                                                     114.85                                 0.492531                                   42.96                               596.243506                              230.333502                                10.925037                                  0.962350                                7.420480                                        1                                -0.470007\n3              02139                   78                5                   5758.24               desktop                      6.78                    147.73             2008                   0.070814              10                   ...                                                      64.98                                 0.645728                                   21.77                               369.770121                              471.048551                                 9.819148                                 -0.244976                               12.537259                                        1                                -0.630425\n4              60091                  111                8                   8205.28               desktop                      5.73                    149.56             2008                   0.087986              30                   ...                                                      83.53                                 0.516262                                   17.27                               584.673126                              322.883448                                13.065436                                 -0.548969                               12.738488                                        1                                -0.497169\n5              02139                   58                4                   4571.37                tablet                      5.91                    148.17             2008                   0.085883              19                   ...                                                      73.09                                 0.830112                                   27.46                               313.448942                              198.522508                                 8.950528                                  0.098885                                5.599228                                        1                                -0.396571\n\n[5 rows x 69 columns]\n```\nWe now have a feature vector for each customer that can be used for machine learning. See the [documentation on Deep Feature Synthesis](https://featuretools.alteryx.com/en/stable/getting_started/afe.html) for more examples.\n\nFeaturetools contains many different types of built-in primitives for creating features. If the primitive you need is not included, Featuretools also allows you to [define your own custom primitives](https://featuretools.alteryx.com/en/stable/getting_started/primitives.html#defining-custom-primitives).\n\n## Demos\n**Predict Next Purchase**\n\n[Repository](https://github.com/alteryx/open_source_demos/blob/main/predict-next-purchase/) | [Notebook](https://github.com/alteryx/open_source_demos/blob/main/predict-next-purchase/Tutorial.ipynb)\n\nIn this demonstration, we use a multi-table dataset of 3 million online grocery orders from Instacart to predict what a customer will buy next. We show how to generate features with automated feature engineering and build an accurate machine learning pipeline using Featuretools, which can be reused for multiple prediction problems. For more advanced users, we show how to scale that pipeline to a large dataset using Dask.\n\nFor more examples of how to use Featuretools, check out our [demos](https://www.featuretools.com/demos) page.\n\n## Testing \u0026 Development\n\nThe Featuretools community welcomes pull requests. Instructions for testing and development are available [here.](https://featuretools.alteryx.com/en/stable/install.html#development)\n\n## Support\nThe Featuretools community is happy to provide support to users of Featuretools. Project support can be found in four places depending on the type of question:\n\n1. For usage questions, use [Stack Overflow](https://stackoverflow.com/questions/tagged/featuretools) with the `featuretools` tag.\n2. For bugs, issues, or feature requests start a [Github issue](https://github.com/alteryx/featuretools/issues).\n3. For discussion regarding development on the core library, use [Slack](https://join.slack.com/t/alteryx-oss/shared_invite/zt-182tyvuxv-NzIn6eiCEf8TBziuKp0bNA).\n4. For everything else, the core developers can be reached by email at open_source_support@alteryx.com\n\n## Citing Featuretools\n\nIf you use Featuretools, please consider citing the following paper:\n\nJames Max Kanter, Kalyan Veeramachaneni. [Deep feature synthesis: Towards automating data science endeavors.](https://dai.lids.mit.edu/wp-content/uploads/2017/10/DSAA_DSM_2015.pdf) *IEEE DSAA 2015*.\n\nBibTeX entry:\n\n```bibtex\n@inproceedings{kanter2015deep,\n  author    = {James Max Kanter and Kalyan Veeramachaneni},\n  title     = {Deep feature synthesis: Towards automating data science endeavors},\n  booktitle = {2015 {IEEE} International Conference on Data Science and Advanced Analytics, DSAA 2015, Paris, France, October 19-21, 2015},\n  pages     = {1--10},\n  year      = {2015},\n  organization={IEEE}\n}\n```\n\n## Built at Alteryx\n\n**Featuretools** is an open source project maintained by [Alteryx](https://www.alteryx.com). To see the other open source projects we’re working on visit [Alteryx Open Source](https://www.alteryx.com/open-source). If building impactful data science pipelines is important to you or your business, please get in touch.\n\n\u003cp align=\"center\"\u003e\n  \u003ca href=\"https://www.alteryx.com/open-source\"\u003e\n    \u003cimg src=\"https://alteryx-oss-web-images.s3.amazonaws.com/OpenSource_Logo-01.png\" alt=\"Alteryx Open Source\" width=\"800\"/\u003e\n  \u003c/a\u003e\n\u003c/p\u003e\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Falteryx%2Ffeaturetools","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Falteryx%2Ffeaturetools","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Falteryx%2Ffeaturetools/lists"}