{
    "info": {
        "author": "Adam Kariv",
        "author_email": "adam.kariv@gmail.com",
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        "classifiers": [
            "Development Status :: 4 - Beta",
            "Intended Audience :: Developers",
            "License :: OSI Approved :: MIT License",
            "Operating System :: OS Independent",
            "Programming Language :: Python :: 3.6",
            "Topic :: Software Development :: Libraries :: Python Modules"
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        "description": "# ![logo](logo-s.png) DataFlows\n\n[![Travis](https://img.shields.io/travis/datahq/dataflows/master.svg)](https://travis-ci.org/datahq/dataflows)\n[![Coveralls](http://img.shields.io/coveralls/datahq/dataflows.svg?branch=master)](https://coveralls.io/r/datahq/dataflows?branch=master)\n![PyPI - Python Version](https://img.shields.io/pypi/pyversions/dataflows.svg)\n[![Gitter chat](https://badges.gitter.im/dataflows-chat/Lobby.png)](https://gitter.im/dataflows-chat/Lobby)\n\nDataFlows is a simple and intuitive way of building data processing flows.\n\n- It's built for small-to-medium-data processing - data that fits on your hard drive, but is too big to load in Excel or as-is into Python, and not big enough to require spinning up a Hadoop cluster...\n- It's built upon the foundation of the Frictionless Data project - which means that all data produced by these flows is easily reusable by others.\n- It's a pattern not a heavy-weight framework: if you already have a bunch of download and extract scripts this will be a natural fit\n\nRead more in the [Features section below](#features).\n\n## QuickStart \n\nInstall `dataflows` via `pip install.`\n\n(If you are using minimal UNIX OS, run first `sudo apt install build-essential`)\n\nThen use the command-line interface to bootstrap a basic processing script for any remote data file:\n\n```bash\n\n# Install from PyPi\n$ pip install dataflows\n\n# Inspect a remote CSV file\n$ dataflows init https://raw.githubusercontent.com/datahq/dataflows/master/data/academy.csv\nWriting processing code into academy_csv.py\nRunning academy_csv.py\nacademy:\n#     Year           Ceremony  Award                                 Winner  Name                            Film\n      (string)      (integer)  (string)                            (string)  (string)                        (string)\n----  ----------  -----------  --------------------------------  ----------  ------------------------------  -------------------\n1     1927/1928             1  Actor                                         Richard Barthelmess             The Noose\n2     1927/1928             1  Actor                                      1  Emil Jannings                   The Last Command\n3     1927/1928             1  Actress                                       Louise Dresser                  A Ship Comes In\n4     1927/1928             1  Actress                                    1  Janet Gaynor                    7th Heaven\n5     1927/1928             1  Actress                                       Gloria Swanson                  Sadie Thompson\n6     1927/1928             1  Art Direction                                 Rochus Gliese                   Sunrise\n7     1927/1928             1  Art Direction                              1  William Cameron Menzies         The Dove; Tempest\n...\n\n# dataflows create a local package of the data and a reusable processing script which you can tinker with\n$ tree\n.\n\u251c\u2500\u2500 academy_csv\n\u2502\u00a0\u00a0 \u251c\u2500\u2500 academy.csv\n\u2502\u00a0\u00a0 \u2514\u2500\u2500 datapackage.json\n\u2514\u2500\u2500 academy_csv.py\n\n1 directory, 3 files\n\n# Resulting 'Data Package' is super easy to use in Python\n[adam] ~/code/budgetkey-apps/budgetkey-app-main-page/tmp (master=) $ python\nPython 3.6.1 (default, Mar 27 2017, 00:25:54)\n[GCC 4.2.1 Compatible Apple LLVM 8.0.0 (clang-800.0.42.1)] on darwin\nType \"help\", \"copyright\", \"credits\" or \"license\" for more information.\n>>> from datapackage import Package\n>>> pkg = Package('academy_csv/datapackage.json')\n>>> it = pkg.resources[0].iter(keyed=True)\n>>> next(it)\n{'Year': '1927/1928', 'Ceremony': 1, 'Award': 'Actor', 'Winner': None, 'Name': 'Richard Barthelmess', 'Film': 'The Noose'}\n>>> next(it)\n{'Year': '1927/1928', 'Ceremony': 1, 'Award': 'Actor', 'Winner': '1', 'Name': 'Emil Jannings', 'Film': 'The Last Command'}\n\n# You now run `academy_csv.py` to repeat the process\n# And obviously modify it to add data modification steps\n```\n\n## Features\n\n* Trivial to get started and easy to scale up\n* Set up and run from command line in seconds ...\n    * `dataflows init` => `flow.py`\n    * `python flow.py`\n* Validate input (and esp source) quickly (non-zero length, right structure, etc.)\n* Supports caching data from source and even between steps\n    * so that we can run and test quickly (retrieving is slow)\n* Immediate test is run: and look at output ...\n    * Log, debug, rerun\n* Degrades to simple python\n* Conventions over configuration\n* Log exceptions and / or terminate\n* The input to each stage is a Data Package or Data Resource (not a previous task)\n\t* Data package based and compatible\n* Processors can be a function (or a class) processing row-by-row, resource-by-resource or a full package\n* A pre-existing decent contrib library of Readers (Collectors) and Processors and Writers\n\n## Learn more\n\nDive into the [Tutorial](TUTORIAL.md) to get a deeper glimpse into everything that `dataflows` can do.\nAlso review this list of [Built-in Processors](PROCESSORS.md), which also includes an API reference for each one of them.\n\n",
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