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    "info": {
        "author": "Eric Herwin",
        "author_email": "herwineric@gmail.com",
        "bugtrack_url": null,
        "classifiers": [
            "License :: OSI Approved :: MIT License",
            "Programming Language :: Python :: 3",
            "Topic :: Text Editors :: Text Processing"
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        "description": "# TTCPy - Text To Columns Python\n\nThis is the Python version of the original TTC package in R. The purpose of the package is to filter strings of non-frequent words and transform it to columns with the frequency of the common words.\n\nThe package is not big, the three things to remember, for the moment, is:\n\n- - -\n\n    1. The initial process of the class. Give it paramters such as minimum support between a value of 0 and 1. Remember, the lower the `minSup` is, the more time it will take to compute.\n\n```\ninit = TTC(data, minSup)\n```\nThe parameters:\n\n- `data`: Is the list of strings that you want to transform.\n- `minSup`: If the frequency of the word is below this value, then it is filtered out. A value between 0 and 1.\n\n- - -\n\n    2. Later you can call the compute function that will compute it with the given data. The output will be a `numpy` array with the frequencies of the words.\n\n```\ninit.compute()\n```\n- - -\n\n    3. You can also call the words that is the most frequent. These words are in the same order as the output from step 2. The output will be a `numpy` array with the frequent words.\n\n```\ninit.header\n```\n\n\n\n\n\n--- Updates for package TTCPy ---\n\n\n- 19/07/2018 - Version 0.2.0\nIntroduced the `numba` module for some functions. Also took away the dependency of the module `re`.\n\n- 11/07/2018 - Version 0.1.0\nImplemented the TTC version from R to Python (TTCPy). There is not option for filtering out specific words as in the R package.\n\n\n",
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        "home_page": "https://github.com/herwineric/Portfolio",
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        "license": "MIT",
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        "name": "TTCPy",
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        "summary": "Make a list of strings to frequency of words",
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