{
    "info": {
        "author": "Martin Thoma",
        "author_email": "info@martin-thoma.de",
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        "classifiers": [
            "Development Status :: 3 - Alpha",
            "Environment :: Console",
            "Intended Audience :: Developers",
            "Intended Audience :: Information Technology",
            "Intended Audience :: Science/Research",
            "License :: OSI Approved :: MIT License",
            "Natural Language :: English",
            "Programming Language :: Python :: 3.6",
            "Topic :: Scientific/Engineering :: Information Analysis",
            "Topic :: Software Development",
            "Topic :: Utilities"
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        "description": "[![Python Support](https://img.shields.io/pypi/pyversions/edapy.svg)](https://pypi.org/project/edapy/)\n[![Build Status](https://travis-ci.org/MartinThoma/edapy.svg?branch=master)](https://travis-ci.org/MartinThoma/edapy)\n[![Coverage Status](https://coveralls.io/repos/github/MartinThoma/edapy/badge.svg?branch=master)](https://coveralls.io/github/MartinThoma/edapy?branch=master)\n\nedapy is a first resource to analyze a new dataset.\n\n## Installation\n\n```\n$ pip install git+https://github.com/MartinThoma/edapy.git\n```\n\nFor the pdf part, you also need `pdftotext`:\n\n```\n$ sudo apt-get install poppler-utils\n```\n\n\n## Usage\n\n```\n$ edapy --help\nUsage: edapy [OPTIONS] COMMAND [ARGS]...\n\n  edapy is a tool for exploratory data analysis with Python.\n\n  You can use it to get a first idea what a CSV is about or to get an\n  overview over a directory of PDF files.\n\nOptions:\n  --version  Show the version and exit.\n  --help     Show this message and exit.\n\nCommands:\n  csv     Analyze CSV files.\n  images  Analyze image files.\n  pdf     Analyze PDF files.\n```\n\nThe workflow is as follows:\n\n* `edapy pdf find --path . --output results.csv` creates a `results.csv`\n  for you. This `results.csv` contains meta data about all PDF files in the\n  `path` directory.\n* `edapy csv predict --csv_path my-new.csv --types types.yaml` will start /\n  resume a process in which the user is lead through a series of questions. In\n  those questions, the user has to decide which delimiter, quotechar is used\n  and which types the columns have.\n* `edapy` generates a `types.yaml` file which can be used to load the CSV in\n  other applications with `df = edapy.load_csv(csv_path, yaml_path)`.\n\n\n## Example types.yaml\n\nFor the [Titanic Dataset](https://www.kaggle.com/c/titanic/data), the resulting\n`types.yaml` looks as follows:\n\n```\ncolumns:\n- dtype: other\n  name: Name\n- dtype: int\n  name: Parch\n- dtype: float\n  name: Age\n- dtype: other\n  name: Ticket\n- dtype: float\n  name: Fare\n- dtype: int\n  name: PassengerId\n- dtype: other\n  name: Cabin\n- dtype: other\n  name: Embarked\n- dtype: int\n  name: Pclass\n- dtype: int\n  name: Survived\n- dtype: other\n  name: Sex\n- dtype: int\n  name: SibSp\ncsv_meta:\n  delimiter: ','\n```\n\nA sample run then would look like this:\n\n```\n$ edapy csv predict --types types_titanik.yaml --csv_path train.csv\nNumber of datapoints: 891\n2018-04-16 21:51:56,279 WARNING Column 'Survived' has only 2 different values ([0, 1]). You might want to make it a 'category'\n2018-04-16 21:51:56,280 WARNING Column 'Pclass' has only 3 different values ([3, 1, 2]). You might want to make it a 'category'\n2018-04-16 21:51:56,281 WARNING Column 'Sex' has only 2 different values (['male', 'female']). You might want to make it a 'category'\n2018-04-16 21:51:56,282 WARNING Column 'SibSp' has only 7 different values ([0, 1, 2, 4, 3, 8, 5]). You might want to make it a 'category'\n2018-04-16 21:51:56,283 WARNING Column 'Parch' has only 7 different values ([0, 1, 2, 5, 3, 4, 6]). You might want to make it a 'category'\n2018-04-16 21:51:56,285 WARNING Column 'Embarked' has only 3 different values (['S', 'C', 'Q']). You might want to make it a 'category'\n\n## Integer Columns\nColumn name: Non-nan  mean   std   min   25%   50%   75%   max\nPassengerId:     891  446.00  257.35     1   224   446   668   891\nSurvived   :     891  0.38  0.49     0     0     0     1     1\nPclass     :     891  2.31  0.84     1     2     3     3     3\nSibSp      :     891  0.52  1.10     0     0     0     1     8\nParch      :     891  0.38  0.81     0     0     0     0     6\n\n## Float Columns\nColumn name: Non-nan   mean    std    min    25%    50%    75%    max\nAge        :     714  29.70  14.53   0.42  20.12  28.00  38.00  80.00\nFare       :     891  32.20  49.69   0.00   7.91  14.45  31.00  512.33\n\n## Other Columns\nColumn name: Non-nan   unique   top (count)\nName       :     891      891   Goldschmidt, Mr. George B (1)\nSex        :     891        2   male (577)\nTicket     :     891      681   347082 (7)\nCabin      :     204      148   C23 C25 C27 (4)\nEmbarked   :     889        4   S (644)\n```\n\n\n",
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