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        "author": "DeepAir Dev",
        "author_email": "naman@deepair.io",
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            "License :: OSI Approved :: MIT License",
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        "description": "## Deep Air Encoder\n\nThis package is used for encoding data fields for machine compliant dataframes.\n\n## Package structure\n\n`deepair_encoder`\n\n\u251c\u2500\u2500 encoder.py\n\u251c\u2500\u2500 __init__.py\n\u2514\u2500\u2500 utils\n    \u251c\u2500\u2500 encoder_tools.py\n    \u251c\u2500\u2500 __init__.py\n    \u251c\u2500\u2500 logger.py\n    \u2514\u2500\u2500 splitters.py\n\n1 directory, 6 files\n\n## Dependencies\n\n**Note**: The following python3 packages are necessary for this package to run:\n\n* numpy\n* scipy\n* pandas\n* sklearn\n* tabulate\n* tqdm\n\n## Function Declarations\n\nHere are the signatures for the functions in the package that can be used for deepair-dev.\n\n### encoder.py\n\nBelow are the functions that can be accessed by importing this module as `from deepair_encoder.encoder import <function_name>`.\n\n`encode_username`:\n```\ndef encode_username(df, drop_field=True):\n    '''\n        Username one-hot encoder.\n        inputs:\n            df: Dataframe which have username column (pandas df series)\n            drop_field: a flag if the usename column should be dropped or not after encoding (bool)\n        return:\n            df: Dataframe which have only username indicator [0/1] (pandas df series)\n    '''\n```\n\n`encode_discounts`:\n```\ndef encode_discounts(df, drop_field=True):\n    '''\n        Discounts encoder.\n        inputs:\n            df: dataframe which has discounts column (pandas df series)\n            drop_field: a flag if the discounts column should be dropped or not after encoding (bool)\n        return:\n            df: a dataframe with 3 new columns 'PROMOCODE', 'RES', 'LFG' and discounts droped if drop_field = True\n    '''\n```\n\n`minmax_score`:\n```\ndef minmax_score(df, fields, keyval=['requestid', 'direction_onward'], drop_field=True, verbose=True):\n    '''\n        scorer function to normalize from 0-1 inversely to the value.\n        inputs:\n            df: Dataframe which contains fields (pandas df series)\n            fields: Dataframe columns on which the score is calculated (list)\n            keyval: Group the data by this key values (list)\n            drop_field: Indicator to replace fields by new scored fields (bool)\n            verbose: Progress bar indicator (bool)\n        return:\n            field: Dataframe with a score for totalprice relative to whole requestid-direction, by index, by faregroup  (pandas df series)\n    '''\n```\n\n`minmax_normalize`:\n```\ndef minmax_normalize(df, fields, keyval=['requestid', 'direction_onward'], drop_field=True, verbose=True):\n    '''\n        normalizer function to normalize from 0-1 proportional to the value.\n        inputs:\n            df: Dataframe which contains fields (pandas df series)\n            fields: Dataframe columns on which the normalication is calculated (list)\n            keyval: Group the data by this key values (list)\n            drop_field: Indicator to replace fields by normalized new fields (bool)\n            verbose: Progress bar indicator (bool)\n        return:\n            field: Dataframe with a score for totalprice relative to whole requestid-direction, by index, by faregroup  (pandas df series)\n    '''\n```\n\n`encode_totalprice`:\n```\ndef encode_totalprice(df):\n    '''\n        total price encoder.\n        inputs:\n            df: Dataframe which have totalprice and totaltaxes column (pandas df series)\n        return:\n            df: Dataframe with a score for totalprice relative to whole requestid-direction, by index, by faregroup  (pandas df series)\n    '''\n```\n\n`encode_bookingid`:\n```\ndef encode_bookingid(df):\n    '''\n        Bookingid flag one-hot encoder.\n        inputs:\n            df: Dataframe which have Bookingid column (pandas df series)\n        return:\n            df: Dataframe which have only Bookingid indicator [0/1] (pandas df series)\n    '''\n```\n\n`encode_faregroup`:\n```\ndef encode_faregroup(raw_file, df, verbose=False):\n    '''\n        Faregroup encoder.\n        inputs:\n            raw_file: raw_file location where the faregroup_definition is available\n            df: Dataframe which have faregroup column (pandas df series)\n            verbose: Optional, if more detailed log is needed\n        return:\n            field: Dataframe with extra fields with faregroup attributes [-1/0/1] (pandas df series)\n            Note: -1 = not available, 0 = available for a fee, 1 = available for free (at no charge)\n    '''\n```\n\n`encode_datetime`:\n```\ndef encode_datetime(df, field, verbose=False):\n    '''\n        Datetime encoder.\n        inputs:\n            df: Dataframe which have 'field' column (pandas df series)\n            field: the field that has to be encoded\n        return:\n            df: Dataframe with 6 ecoded values capturing TOD, DOW and WOY\n    '''\n```\n\n`encode_advanced_purchase`:\n```\ndef encode_advanced_purchase(df, dptr_field='departuredate', sales_field='utctimestamp'):\n    '''\n        advanced purchase encoder.\n        inputs:\n            df: Dataframe which have dptr_field and sales_field column (pandas df series)\n        return:\n            df: Dataframe advanced purchase column\n    '''\n```\n\n`encode_los`:\n```\ndef encode_los(df, verbose=False):\n    '''\n        los and trip_type encoder.\n        inputs:\n            df: Dataframe which have departuredate, requestid and direction column (pandas df series)\n        return:\n            df: Dataframe los and trip_type encodes column\n    '''\n```\n\n`encode_airports`:\n```\ndef encode_airports(df, fields, reference_file_path, verbose=True):\n    '''\n        encode the airports.\n        inputs:\n            df                      : Dataframe which have airports (pandas df series)\n            fields                  : list of fields that you want to run this\n                                        function on (list)\n            reference_file_path     : path to the codes file (string)\n            verbose                 : Indicator for progress bar (bool)\n        return:\n            df: Dataframe encoded with airports values\n    '''\n```\n\n`encode_city`:\n```\ndef encode_city(df, fields, reference_file_path, verbose=True):\n    '''\n        encode the airports.\n        inputs:\n            df                      : Dataframe which have city (pandas df series)\n            fields                  : list of fields that you want to run this\n                                        function on (list)\n            reference_file_path     : path to the codes file (string)\n            verbose                 : Indicator for progress bar (bool)\n        return:\n            df: Dataframe encoded with airports values\n    '''\n```\n\n### utils\n\nThis subpackage contains tools a level lower than `encoder` module i.e. those modules that are used by encoder.\n\n#### encoder_tools\n\nBelow are the functions that can be accessed by importing this module as `from deepair_encoder.utils.encoder_tools import <function_name>`.\n\n`one_hot_encoder`:\n```\ndef one_hot_encoder(df, fields, fields_drop=True, verbose=True, classes=None):\n    '''\n        Converts the fields into one hot encoding.\n        inputs:\n            df: Dataframe containing all those fields (pandas df)\n            fields: Dataframe columns that you want to convert to one hot (string)\n            verbose: Indicator for progress bar (bool)\n            classes: Dictionary for the desired columns (dict)\n        return:\n            df: updated dataframe (pandas df)\n    '''\n```\n\n`integer_encoder`:\n```\ndef integer_encoder(df, fields, verbose=True):\n    '''\n        Converts the fields into integer encoding.\n        inputs:\n            df: Dataframe containing all those fields (pandas df)\n            fields: Dataframe columns that you want to convert to one hot (string)\n            verbose: Indicator for progress bar (bool)\n        returns:\n            df: updated dataframe (pandas df)\n    '''\n```\n\n`get_wom`:\n```\ndef get_wom(field):\n    '''\n        Function for week of the month.\n        inputs:\n            field: Dataframe column which have timestamp (pandas df series)\n        return:\n            field: Dataframe column which have wom (pandas df series)\n    '''\n```\n\n`get_dow`:\n```\ndef get_dow(field):\n    '''\n        Function for day of the week.\n        inputs:\n            field: Dataframe column which have timestamp (pandas df series)\n        return:\n            field: Dataframe column which have dow (pandas df series)\n    '''\n```\n\n`get_month`:\n```\ndef get_month(field):\n    '''\n        Function for month.\n        inputs:\n            field: Dataframe column which have timestamp (pandas df series)\n        return:\n            field: Dataframe column which have only month (pandas df series)\n    '''\n```\n\n`get_year`:\n```\ndef get_year(field):\n    '''\n        Function for year.\n        inputs:\n            field: Dataframe column which have timestamp (pandas df series)\n        return:\n            field: Dataframe column which have only year (pandas df series)\n    '''\n```\n\n`obj2num`:\n```\ndef obj2num(df, fields, verbose=True):\n    '''\n        Converts the fields into numeric from obj data type.\n        inputs:\n            df: Dataframe containing all those fields (pandas df)\n            fields: Dataframe columns that you want to convert to numeric (string)\n            verbose: Indicator for progress bar (bool)\n        returns:\n            df: updated dataframe (pandas df)\n    '''\n```\n\n#### logger\n\nBelow are the functions that can be accessed by importing this module as `from deepair_encoder.utils.logger import <function_name>`.\n\n`unique_stats`:\n```\ndef unique_stats(df):\n```\n\n`_log_with_timestamp`:\n```\ndef _log_with_timestamp(message):\n    '''\n        prints message on console\n        input :\n            message     : msg to print (string)\n    '''\n```\n\n#### splitters\n\nBelow are the functions that can be accessed by importing this module as `from deepair_encoder.utils.splitters import <function_name>`.\n\n`type_splitter`:\n```\ndef type_splitter(data, keys=['passengertypes'], newheadings=[['ADT', 'CHD', 'INF']]):\n    '''\n        Split the column (need to make it more generic).\n        inputs:\n            data: dataframe which contain this column (pd dataframe)\n            keys: list of column fields to split(list)\n            newheadings: list of list for header columns in keys (list)\n        returns:\n            data: updated dataframe (pandas df)\n    '''\n```\n\n`split_datewise`:\n```\ndef split_datewise(df, directory='', postfix='', mode='w'):\n    '''\n        Split a Dataframe according to dates in UTC-Timestamp:\n\n        input :-\n            DF          : dataframe which contain this column (pd dataframe)\n            directory   : path to the target directory (string)\n            postfix     : any postfix you want to add (string)\n            mode        : writing mode ('w':writing, 'a': append)\n    '''\n```\n\n\n\n",
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