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        "description": "# DataJazz\nTools to jazz up your data.\n\nDataJazz is toolkit for manipulating and optimizing your data for analysis, machine learning, extraction, transformation, and loading (ETL). \n\n## Contributing\nDataJazz is an open-source project founded and maintained to better serve the data science and machine learning community. Please feel free to submit pull requests to contribute to the project. By participating, you are expected to adhere to DataJazz's [code of conduct](CODE_OF_CONDUCT.md).\n\n## Installation\n```\npip install datajazz\n```\n\n## Example usage\n\n### Create a dataframe with different datatypes\n```\nimport pandas as pd\nimport numpy as np\n\nrng = pd.date_range('2015-02-24', periods=5, freq='20H')\ndf = pd.DataFrame({ 'Start_Time': rng, 'Values': np.random.randn(len(rng)), 'Categories': ['A']*len(rng) }) \ndf.head()\n```\n<img src=\"images/df_1.png\" height=\"150\">\n\n### Create time-of-time features\n```\nimport datajazz as dj\ndf = dj.timeoftime(df)\n```\n<img src=\"images/df_tot.png\" height=\"150\">\n\n### One-hot encode your categorical columns\n```\nimport datajazz as dj\ndf = dj.onehot_categories(df)\n```\n<img src=\"images/df_onehot.png\" height=\"150\">\n\n### Remove redundant rows and columns\n```\nimport datajazz as dj\ndf = dj.remove_redundancies(df)\n```\n<img src=\"images/df_redundancies.png\" height=\"150\">\n<img src=\"images/df_1.png\" height=\"150\">\n\n### Many more use cases to come! Submit a pull request to add a new use case.\n\n",
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