Delete dataset_prep.py

dataset_prep.py

-from pandas import read_csv, DataFrame, concat
-import numpy as np
-
-
-def getDataset():
-    ticket_data = getRawDataset()
-    impacts = ticket_data['Impact'].tolist()
-    urgencies = ticket_data['Urgency'].tolist()
-    texts = ticket_data['Description'].tolist()
-
-    dict_corpus = {'Descriptions': [], 'Impacts': [], 'Urgencies': []}
-
-    for index in range(len(impacts)):
-        if not (impacts[index] is np.nan
-                or urgencies[index] is np.nan
-                or texts[index] is np.nan):
-            dict_corpus['Descriptions'].append(texts[index])
-            dict_corpus['Impacts'].append(impacts[index])
-            dict_corpus['Urgencies'].append(urgencies[index])
-
-    data_frame_corpus = DataFrame(dict_corpus)
-    return data_frame_corpus
-
-
-def getRawDataset():
-    ticket_data_low_prio = read_csv('project_utilities/Datasets/ITSupport_Tickets.csv')
-    ticket_data_high_prio = read_csv('custom_models/ITSupport_Tickets_High_Prio.csv')
-    ticket_data_whole = concat([ticket_data_low_prio, ticket_data_high_prio])
-    return ticket_data_whole
-
-
-def convertToPriorities(dataset: DataFrame or dict) -> DataFrame:
-    prio_to_num = {'Low': 0, 'Medium': 1, 'High': 2}
-    num_to_pnum = ['P5', 'P4', 'P3', 'P2', 'P1']
-
-    pnums = []
-    for priorities in zip(dataset['Impacts'], dataset['Urgencies']):
-        numbered_priority = sum([prio_to_num[priorities[0]], prio_to_num[priorities[1]]])
-        pnums.append(num_to_pnum[numbered_priority])
-
-    dataset['Priorities'] = pnums
-    return dataset
-
-
-if __name__ == '__main__':
-    hi = getDataset()
-    print(convertToPriorities(hi))