Update NC_Crime.py

NC_Crime.py

@@ -165,63 +165,6 @@ class NCCrimeDataset(datasets.GeneratorBasedBuilder):
 
         return Durham_new
 
-    def _preprocess_chapel_hill(self, file_path):
-         # Load the dataset
-        Chapel = pd.read_csv(file_path, low_memory=False)
-
-        # Replace specified values with None
-        replace_values = {'<Null>': None, 'NONE': None}
-        Chapel['Weapon_Description'] = Chapel['Weapon_Description'].replace(replace_values)
-
-        # Define the category mapping
-        category_mapping = {
-        'Theft': ['THEFT/LARCENY', 'LARCENY FROM AU', 'LARCENY FROM PE', 'LARCENY OF OTHE', 'LARCENY FROM BU', 'LARCENY OF BIKE', 'LARCENY FROM RE', 'LARCENY OF AUTO'],
-        'Assault': ['ASSAULT/SEXUAL', 'ASSAULT', 'STAB GUNSHOT PE', 'ACTIVE ASSAILAN'],
-        'Burglary': ['BURGLARY', 'BURGLARY ATTEMP', 'STRUCTURE COLLAPSE', 'ROBBERY/CARJACK'],
-        'Drugs': ['DRUGS'],
-        'Traffic Violations': ['TRAFFIC STOP', 'TRAFFIC/TRANSPO', 'TRAFFIC VIOLATI', 'MVC', 'MVC W INJURY', 'MVC W INJURY AB', 'MVC W INJURY DE', 'MVC ENTRAPMENT'],
-        'Disorderly Conduct': ['DISTURBANCE/NUI', 'DOMESTIC DISTUR', 'DISPUTE', 'DISTURBANCE', 'LOST PROPERTY', 'TRESPASSING/UNW', 'REFUSAL TO LEAV', 'SUSPICIOUS COND', 'STRUCTURE FIRE'],
-        'Fraud': ['FRAUD OR DECEPT'],
-        'Sexual Offenses': ['SEXUAL OFFENSE'],
-        'Homicide': ['SUICIDE ATTEMPT', 'ABUSE/ABANDOMEN', 'DECEASED PERSON'],
-        'Weapons Violations': ['WEAPON/FIREARMS'],
-        'Animal-related Offenses': ['ANIMAL BITE', 'ANIMAL', 'ANIMAL CALL'],
-        'Missing Person': ['MISSING PERSON'],
-        'Public Service': ['PUBLIC SERVICE', 'PUBLICE SERVICE'],
-        'Miscellaneous': ['<Null>', 'SUSPICIOUS/WANT', 'MISC OFFICER IN', 'INDECENCY/LEWDN', 'PUBLIC SERVICE', 'TRESPASSING', 'UNKNOWN PROBLEM', 'LOUD NOISE', 'ESCORT', 'ABDUCTION/CUSTO', 'THREATS', 'BURGLAR ALARM', 'DOMESTIC', 'PROPERTY FOUND', 'FIREWORKS', 'MISSING/RUNAWAY', 'MENTAL DISORDER', 'CHECK WELL BEIN', 'PSYCHIATRIC', 'OPEN DOOR', 'ABANDONED AUTO', 'HARASSMENT THRE', 'JUVENILE RELATE', 'ASSIST MOTORIST', 'HAZARDOUS DRIVI', 'MVC', 'GAS LEAK FIRE', 'ASSIST OTHER AG', 'DOMESTIC ASSIST', 'SUSPICIOUS VEHI', 'UNKNOWN LE', 'ALARMS', '911 HANGUP', 'BOMB/CBRN/PRODU', 'STATIONARY PATR', 'LITTERING', 'HOUSE CHECK', 'CARDIAC', 'CLOSE PATROL', 'BOMB FOUND/SUSP', 'INFO FOR ALL UN', 'UNCONCIOUS OR F', 'LIFTING ASSISTA', 'ATTEMPT TO LOCA', 'SICK PERSON', 'HEAT OR COLD EX', 'CONFINED SPACE', 'TRAUMATIC INJUR', 'DROWNING', 'CITY ORDINANCE']
-        }
-         # Function to categorize crime
-        def categorize_crime(crime):
-            for category, crimes in category_mapping.items():
-                if crime in crimes:
-                    return category
-            return 'Miscellaneous'
-
-        # Create a new DataFrame with simplified crime categories
-        Chapel_new = pd.DataFrame({
-            "year": pd.to_datetime(Chapel['Date_of_Occurrence']).dt.year,
-            "city": "Chapel Hill",
-            "crime_major_category": Chapel['Reported_As'].apply(categorize_crime),
-            "crime_detail": Chapel['Offense'].str.title(),
-            "latitude": Chapel['X'].round(5).fillna(0),
-            "longitude": Chapel['Y'].round(5).fillna(0),
-            "occurance_time": pd.to_datetime(Chapel['Date_of_Occurrence'].str.replace(r'\+\d{2}$', '', regex=True)).dt.strftime('%Y/%m/%d %H:%M:%S'),
-            "clear_status": None,
-            "incident_address": Chapel['Street'].str.replace("@", " "),
-            "notes": Chapel['Weapon_Description'].apply(lambda x: f"Weapon: {x}" if pd.notnull(x) else "Weapon: None").str.title()
-        }).fillna("No Data")
-
-        # Correct the latitude and longitude if necessary
-        Chapel_new.loc[(Chapel_new['latitude'].between(-80, -70)) & (Chapel_new['longitude'].between(30, 40)), ['latitude', 'longitude']] = Chapel_new.loc[(Chapel_new['latitude'].between(-80, -70)) & (Chapel_new['longitude'].between(30, 40)), ['longitude', 'latitude']].values
-
-        # Ensure latitude and longitude are in the expected range
-        Chapel_new = Chapel_new.loc[(Chapel_new['latitude'].between(30, 40)) & (Chapel_new['longitude'].between(-80, -70))]
-
-        # Filter for years 2015 and onwards
-        Chapel_new = Chapel_new[Chapel_new['year'] >= 2015]
-
-        return Chapel_new
-
     def _preprocess_cary(self, file_path):
         # Load the dataset
         df = pd.read_csv(file_path, low_memory=False).dropna(subset=['Year'])