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"description": "# TakeMessageCleaner\n\nTakeMessageCleaner is a tool for pre processing messages. \nIt can be used to convert messages to lower case, correct spelling, remove elements like punctuation, emoji, whatapp's emoji, accentuation, number, cpf, url, e-mail, money, code, time, date and small talks.\nAlso, it can pre process data from a dataframe, series, list or csv file.\n\n#### MessageCleaner.from_dataframe: creates a constructor from a dataframe\n\n\n- config_file_path: str
\nconfig_file_path is the path of the json file with the configuration\n\n- dataframe: pd.core.frame.DataFrame
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\n\n#### MessageCleaner.from_series: creates a constructor from a series\n\n\n- config_file_path: str
\nconfig_file_path is the path of the json file with the pre processing\n\n- series: pd.core.frame.Series
\nseries is the pandas series that needs to be processed.\n\n#### MessageCleaner.from_list: creates a constructor from a list\n\n\n- config_file_path: str
\nconfig_file_path is the path of the json file with the configuration\n\n- lst: list
\nlst is the list of string that need to be processed.\n
\n\n#### MessageCleaner.from_file: creates a constructor from a csv file\nfile_path : str, content_column : str = 'Content', encoding: str = 'utf-8', sep: str = ';'\n\n\n- config_file_path: str
\nconfig_file_path is the path of the json file with the configuration\n\n- file_path : strt
\nfile_path is the path of the csv file that needs to be processed.\n\n- content_column: str
\ncontent_column is the column name of the dataframe that has the information to be processed. If the file separator is not set, the value 'Content' will be used.\n\n- sep: str
\nsep is the csv file separator. If the file separator is not set, the value ';' will be used.\n\n- encoding: str
\nencoding is the encoding of the csv file. If the file encoding is not set, the value 'utf-8' will be used.\n
\n\n#### MessageCleaner.pre_process: pre-process messages using a json file with the configuration.\nThe pre processing step is able to convert sentences to lower case, correct spelling and remove elements like punctuation, emoji, whatapp emoji, accentuation, number, cpf, url, e-mail, money, code, time, date and small talks.\nOptionally, you can activate use_placeholder to insert a placeholder where the element was removed. For example: \"I want 2 apples\" would be converted in \"I want NUMBER apples\".\n\n## config.json\n```\n{\n\t\"use_placeholder\": true,\n\t\"verbose\": true, \n\t\"processing\": {\n\t\t\"lower\": true,\n\t\t\"punctuation\": true,\n\t\t\"emoji\": true,\n\t\t\"wa_emoji\": true,\n\t\t\"accentuation\": true,\n\t\t\"number\": true,\n\t\t\"cpf\": true,\n\t\t\"url\": true,\n\t\t\"email\": true,\n\t\t\"money\": true,\n\t\t\"code\": true,\n\t\t\"time\": true,\n\t\t\"date\": true,\n\t\t\"spelling\": true\n\t},\n\t\"output\": {\n\t\t\"file_name\": \"output_file.csv\",\n\t\t\"file_encoding\" : \"utf-8\",\n\t\t\"file_sep\": \";\",\n\t\t\"remove_duplicates\": true,\n\t\t\"remove_empty\": true,\n\t\t\"sort_by_length\": true\n\t}\n}\n```\n\n## Installation\n\nUse the package manager [pip](https://pip.pypa.io/en/stable/) to install TakeMessageCleaner\n\n```bash\npip install TakeMessageCleaner\n```\n\n## Usage\n\n```python\nimport MessageCleaner as mc\n\ncleaner = mc.MessageCleaner.from_file(config_file_path = 'C:/Documents/config.json', file_path = 'C:/Users/mydata.csv', sep = ';', encoding = 'latin-1')\nresult = cleaner.clean()\nprint(result)\n```\n\n## Author\nKarina Tiemi Kato\n\n## License\n[MIT](https://choosealicense.com/licenses/mit/)\n\n",
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