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        "author": "Jake Kara",
        "author_email": "jake@jakekara.com",
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        "description": "# CT Name Cleaner\n\nResolve village and coloquial Connecticut town names, as well as common\nmisspellings of Connecticut town names to their official town names.\n\nThis is based on an R package of the same name by my colleague Andrew Ba Tran.\n\nThis installs a command line script, ctclean,  as well as a library\nparticularly meant for use within Jupyter notebooks.\n\nby Jake Kara, jake@jakekara.com\n\n### Latest version\n\n0.10.1\n     \n### Installation\n\n    pip install ctnamecleaner\n\n### Command line util\n\nUsage:\n\n\t$ ctclean New\\ Preston\n\tWASHINGTON\n\t$ ctclean \"New Preston\"\n\tWASHINGTON\n\nWhen nothing is found, return None:\n\n\t$ ctclean NotGonnaFindItsVille\n\tNone\n\nSet a custom value to return on error with the --error or -e flag:\n\n    $ ctclean NotGonnaFindItsVille --error \"Ruh Roh\"\n    Ruh Roh\n\n### Use with Pandas dataframes\n\nSee HELP.txt in this directory and the Notebook in the demo/ folder in this\nrepo for an example of translating an entire column with the clean,\nclean_col and the clean_dataframe() method. clean_dataframe uses pandas'\nDataFrame.join() method, so it's faster than using the cean() method\nand applying it with a lambda function yourself. \n\n### Extending with other data\n\nNot in CT? Want to map other things? Just make a spreadsheet and put it\nanywhere, online or locally, that Pandas .read_csv() can open, and then use\nthe constructor to customize the lookup class.\n\n     >>> l = lookup.Lookup(csv_url=\"http://path/to/your/sheet\",\n\t\t\t   raw_name_col=\"something\",\n\t\t\t   clean_name_col=\"something_else\")\n\n### Contents of HELP.txt\n\nBelow this point is auto documentation from the lookup class generated from\nhelp.py:\n\nHelp on module ctlookup.lookup in ctlookup:\n\nNAME\n    ctlookup.lookup - Main module for CT Name Cleaner\n\nFILE\n    /Applications/MAMP/htdocs/tdev/pyctnamecleaner/package/ctlookup/lookup.py\n\nCLASSES\n    Lookup\n    \n    class Lookup\n     |  Lookup class for CT place names, or any other DF for that matter\n     |  \n     |  Methods defined here:\n     |  \n     |  __init__(self, raw_name_col='name', clean_name_col='real.town.name', csv_url=None, use_inet_csv=False)\n     |      Constructor for Lookup \n     |      \n     |      No need to use parameters unless you are specifying a different\n     |      source URL.\n     |      \n     |      Parameters\n     |      -----------\n     |      raw_name_col : string, optional\n     |          The name of the column with input names, like \"New Preston\"\n     |      \n     |          Only use if you're using a different source spreadsheet.\n     |      \n     |      clean_name_col : string, optional\n     |          The name of the column with out names, like \"Washington\"\n     |      \n     |          Only use if you're using a different source spreadsheet.\n     |      \n     |      csv_url : string, optional\n     |          A valid local file or remote url to use as an alternative\n     |          source spreadsheet.\n     |      \n     |      use_inet_csv : boolean, optional\n     |          Force a reload of the spreadsheet from the web to reflect any\n     |          new additions since it was bundled with this python package.\n     |      \n     |          Defaults to False. The list doesn't change too much anymore.\n     |  \n     |  clean(self, raw_name, error=None)\n     |      Get a clean place name (e.g. input \"New Preston\" and get\n     |      \"Washington\")\n     |      \n     |      Parameters\n     |      ----------\n     |      raw_name : string\n     |          The input name of the place, such as a village or a\n     |          common misspelling of a town name\n     |      \n     |      error : obj, optional\n     |          The default to return if no match is found\n     |      \n     |          Defaults to None\n     |      \n     |      Returns\n     |      -------\n     |      String or the value of None (or anything specified with the error\n     |      parameter) if no match is found\n     |  \n     |  clean_col(self, series, error=None)\n     |      Clean a Pandas Series of place names\n     |      \n     |      Parameters\n     |      ----------\n     |      series : Pandas Series\n     |          A series containing place names that need to be cleaned\n     |      \n     |      error : obj, optional\n     |          Value to use if no match is found for a given place.\n     |      \n     |          Defaults to None\n     |      \n     |      Notes\n     |      -----\n     |      Meant as a less opinionated version of clean_dataframe\n     |  \n     |  clean_dataframe(self, df, town_col, error=None)\n     |      Clean an entire column of place names\n     |      \n     |      Parameters\n     |      ----------\n     |      \n     |      df : Pandas DataFrame\n     |          Dataframe containing to clean\n     |      \n     |      town_col : valid column label\n     |          Label of column containing town names to clean\n     |      \n     |      error : obj, optional\n     |          Default value to use when no match is found.\n     |      \n     |          Defaults to None\n     |      \n     |      Notes\n     |      -----\n     |      I plan to deprecate this but leave it in place for\n     |      backward-compatibility. Use clean_col instead.",
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