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        "author": "Jos Polfliet, Simon Brugman",
        "author_email": "pandasprofiling@gmail.com",
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            "Development Status :: 5 - Production/Stable",
            "Environment :: Console",
            "Framework :: IPython",
            "Intended Audience :: Developers",
            "Intended Audience :: Financial and Insurance Industry",
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            "License :: OSI Approved :: MIT License",
            "Operating System :: OS Independent",
            "Programming Language :: Python :: 3",
            "Programming Language :: Python :: 3.5",
            "Programming Language :: Python :: 3.6",
            "Programming Language :: Python :: 3.7",
            "Topic :: Scientific/Engineering",
            "Topic :: Software Development :: Build Tools"
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        "description": "# Pandas Profiling\n[![Build Status](https://travis-ci.com/pandas-profiling/pandas-profiling.svg?branch=master)](https://travis-ci.com/pandas-profiling/pandas-profiling)\n[![Code Coverage](https://codecov.io/gh/pandas-profiling/pandas-profiling/branch/master/graph/badge.svg?token=gMptB4YUnF)](https://codecov.io/gh/pandas-profiling/pandas-profiling)\n[![Release Version](https://img.shields.io/github/release/pandas-profiling/pandas-profiling.svg)](https://github.com/pandas-profiling/pandas-profiling/releases)\n[![Code style: black](https://img.shields.io/badge/code%20style-black-000000.svg)](https://github.com/python/black)\n\nGenerates profile reports from a pandas `DataFrame`. \nThe pandas `df.describe()` function is great but a little basic for serious exploratory data analysis. \n`pandas_profiling` extends the pandas DataFrame with `df.profile_report()` for quick data analysis.\n\nFor each column the following statistics - if relevant for the column type - are presented in an interactive HTML report:\n\n* **Essentials**: type, unique values, missing values\n* **Quantile statistics** like minimum value, Q1, median, Q3, maximum, range, interquartile range\n* **Descriptive statistics** like mean, mode, standard deviation, sum, median absolute deviation, coefficient of variation, kurtosis, skewness\n* **Most frequent values**\n* **Histogram**\n* **Correlations** highlighting of highly correlated variables, Spearman, Pearson and Kendall matrices\n* **Missing values** matrix, count, heatmap and dendrogram of missing values\n\n## Examples\n\nThe following examples can give you an impression of what the package can do:\n\n* [Census Income](http://pandas-profiling.github.io/pandas-profiling/examples/census/census_report.html) (US Adult Census data relating income)\n* [NASA Meteorites](http://pandas-profiling.github.io/pandas-profiling/examples/meteorites/meteorites_report.html) (comprehensive set of meteorite landings)\n* [Titanic](http://pandas-profiling.github.io/pandas-profiling/examples/titanic/titanic_report.html) (the \"Wonderwall\" of datasets)\n* [NZA](http://pandas-profiling.github.io/pandas-profiling/examples/nza/nza_report.html) (open data from the Dutch Healthcare Authority)\n* [Stata Auto](http://pandas-profiling.github.io/pandas-profiling/examples/stata_auto/stata_auto_report.html) (1978 Automobile data)\n* [Website Inaccessibility](http://pandas-profiling.github.io/pandas-profiling/examples/website_inaccessibility/website_inaccessibility_report.html) (demonstrates the URL type)\n\n## Installation\n\n### Using pip\n\n[![PyPi Downloads](https://pepy.tech/badge/pandas-profiling)](https://pepy.tech/project/pandas-profiling)\n[![PyPi Monthly Downloads](https://pepy.tech/badge/pandas-profiling/month)](https://pepy.tech/project/pandas-profiling/month)\n[![PyPi Version](https://badge.fury.io/py/pandas-profiling.svg)](https://pypi.org/project/pandas-profiling/)\n\nYou can install using the pip package manager by running\n\n    pip install pandas-profiling\n    \nAlternatively, you could install directly from Github:\n\n    pip install https://github.com/pandas-profiling/pandas-profiling/archive/master.zip\n\n    \n### Using conda\n\n[![Conda Downloads](https://img.shields.io/conda/dn/conda-forge/pandas-profiling.svg)](https://anaconda.org/conda-forge/pandas-profiling)\n[![Conda Version](https://img.shields.io/conda/vn/conda-forge/pandas-profiling.svg)](https://anaconda.org/conda-forge/pandas-profiling) \n \nYou can install using the conda package manager by running\n\n    conda install -c conda-forge pandas-profiling\n\n### From source\n\nDownload the source code by cloning the repository or by pressing ['Download ZIP'](https://github.com/pandas-profiling/pandas-profiling/archive/master.zip) on this page. \nInstall by navigating to the proper directory and running\n\n    python setup.py install\n    \n## Usage\n\nThe profile report is written in HTML5 and CSS3, which means pandas-profiling requires a modern browser. \n\n## Documentation\n\nThe documentation for `pandas_profiling` can be found [here](https://pandas-profiling.github.io/pandas-profiling/docs/).\nThe documentation is generated using [`pdoc3`](https://github.com/pdoc3/pdoc). \nIf you are contributing to this project, you can rebuild the documentation using:\n```\nmake docs\n```\nor on Windows:\n```\nmake.bat docs\n```\n\n### Jupyter Notebook\n\nWe recommend generating reports interactively by using the Jupyter notebook. \n\nStart by loading in your pandas DataFrame, e.g. by using\n```python\nimport numpy as np\nimport pandas as pd\nimport pandas_profiling\n\ndf = pd.DataFrame(\n    np.random.rand(100, 5),\n    columns=['a', 'b', 'c', 'd', 'e']\n)\n```\nTo display the report in a Jupyter notebook, run:\n```python\ndf.profile_report(style={'full_width':True})\n```\nTo retrieve the list of variables which are rejected due to high correlation:\n```python\nprofile = df.profile_report()\nrejected_variables = profile.get_rejected_variables(threshold=0.9)\n```\nIf you want to generate a HTML report file, save the `ProfileReport` to an object and use the `to_file()` function:\n```python\nprofile = df.profile_report(title='Pandas Profiling Report')\nprofile.to_file(output_file=\"output.html\")\n```\n### Command line usage\n\nFor standard formatted CSV files that can be read immediately by pandas, you can use the `pandas_profiling` executable. Run\n\n\tpandas_profiling -h\n\nfor information about options and arguments.\n\n### Advanced usage\n\nA set of options is available in order to adapt the report generated.\n\n* `title` (`str`): Title for the report ('Pandas Profiling Report' by default).\n* `pool_size` (`int`): Number of workers in thread pool. When set to zero, it is set to the number of CPUs available (0 by default).\n* `minify_html` (`boolean`): Whether to minify the output HTML.\n\nMore settings can be found in the [default configuration file](https://github.com/pandas-profiling/pandas-profiling/blob/master/pandas_profiling/config_default.yaml).\n\n__Example__\n```python\nprofile = df.profile_report(title='Pandas Profiling Report', plot={'histogram': {'bins': 8}})\nprofile.to_file(output_file=\"output.html\")\n```\n\n## How to contribute\n\nThe package is actively maintained and developed as open-source software. \nIf `pandas-profiling` was helpful or interesting to you, you might want to get involved. \nThere are several ways of contributing and helping our thousands of users.\nIf you would like to be a industry partner or sponsor, please [drop us a line](mailto:pandasprofiling@gmail.com).\n\nRead more on getting involved in the [Contribution Guide](https://github.com/pandas-profiling/pandas-profiling/blob/master/CONTRIBUTING.md).\n\n\n## Editor integration\n### PyCharm integration \n1. Install `pandas-profiling` via the instructions above\n2. Locate your `pandas-profiling` executable.\n\n\t  On macOS / Linux / BSD:\n\t\n\t```console\n\t$ which pandas_profiling\n\t(example) /usr/local/bin/pandas_profiling\n\t```\n\t\n\t  On Windows:\n\t\n\t```console\n\t$ where pandas_profiling\n\t(example) C:\\ProgramData\\Anaconda3\\Scripts\\pandas_profiling.exe\n\t```\n\n2. In Pycharm, go to _Settings_ (or _Preferences_ on macOS) > _Tools_ > _External tools_\n3. Click the _+_ icon to add a new external tool\n4. 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