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        "author": "Kenneth Reitz",
        "author_email": "me@kennethreitz.org",
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        "classifiers": [
            "License :: OSI Approved :: MIT License",
            "Programming Language :: Python",
            "Programming Language :: Python :: 3.6",
            "Programming Language :: Python :: Implementation :: CPython",
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        "description": "\nTwitter Scraper\n===============\n\nTwitter's API is annoying to work with, and has lots of limitations \u2014\nluckily their frontend (JavaScript) has it's own API, which I reverse\u2013engineered.\nNo API rate limits. No restrictions. Extremely fast.\n\nYou can use this library to get the text of any user's Tweets trivially.\n\nVery useful for making markov chains.\n\nUsage\n=====\n\n.. code-block:: pycon\n\n    >>> from twitter_scraper import get_tweets\n\n    >>> for tweet in get_tweets('kennethreitz', pages=1):\n    >>>     print(tweet['text'])\n    P.S. your API is a user interface\n    s3monkey just hit 100 github stars! Thanks, y\u2019all!\n    I\u2019m not sure what this /dev/fd/5 business is, but it\u2019s driving me up the wall.\n    \u2026\n\nIt appears you can ask for up to 25 pages of tweets reliably (~486 tweets).\n\nMarkov Example\n==============\n\nFirst, install markovify:\n\n.. code-block:: shell\n\n    $ pipenv install markovify\n\n.. code-block:: pycon\n\n    >>> import markovify\n\n    >>> tweets = '\\n'.join([t['text'] for t in get_tweets('kennethreitz', pages=25)])\n    >>> text_model = markovify.Text(tweets)\n\n    >>> print(text_model.make_short_sentence(140))\n    Wtf you can\u2019t use APFS on a prototype for \u201cdjango-heroku\u201d, which does a lot out of me.\n\nInstallation\n============\n\n.. code-block:: shell\n\n    $ pipenv install twitter-scraper\n\nOnly Python 3.6+ is supported\n\n\nLICENSE\n=======\n\nMIT\n\n\n",
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        "license": "MIT",
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        "name": "twitter-scraper",
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        "summary": "Scrape the Twitter Frontend API without authentication.",
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