import pandas as pd from datetime import datetime from uszipcode import SearchEngine from datasets import load_dataset # dataset path to NYC_collisions_data JSON dataset_path = "xx103/NYC_Motor_Vehicle_Collisions_and_Weather_Dataset" data_files1 = ["df1_NYC_collisions_data.json"] dataset1 = load_dataset(path=dataset_path, data_files=data_files1) # Convert to pandas DataFrame df = dataset1['train'].to_pandas() # convert 'CRASH TIME' to datetime to further extract the hours and minutes df['CRASH TIME'] = pd.to_datetime(df['CRASH TIME'], format='%H:%M') # Update 'CRASH TIME' to 'CRASH TIME PERIOD', 'CONTRIBUTING FACTOR VEHICLES', 'VEHICLE TYPES', 'STREET NAME' and 'STREET TYPE' for index, row in df.iterrows(): hour = row['CRASH TIME'].hour period_start = (hour // 3) * 3 period_end = period_start + 2 df.at[index, 'CRASH TIME PERIOD'] = f"{period_start:02d}:00-{period_end:02d}:59" factors = [row[f'CONTRIBUTING FACTOR VEHICLE {i}'] for i in range(1, 6) if row.get(f'CONTRIBUTING FACTOR VEHICLE {i}')] df.at[index, 'CONTRIBUTING FACTOR VEHICLES'] = ', '.join(factors) vehicle_types = [row[f'VEHICLE TYPE CODE {i}'] for i in range(1, 6) if row.get(f'VEHICLE TYPE CODE {i}')] df.at[index, 'VEHICLE TYPES'] = ', '.join(vehicle_types) street_names = [] street_types = [] # Check and append 'ON STREET NAME' if pd.notna(row['ON STREET NAME']) and row['ON STREET NAME'] != '': street_names.append(row['ON STREET NAME']) street_types.append('ON STREET') # Check and append 'CROSS STREET NAME' if pd.notna(row['CROSS STREET NAME']) and row['CROSS STREET NAME'] != '': street_names.append(row['CROSS STREET NAME']) street_types.append('CROSS STREET') # Check and append 'OFF STREET NAME' if pd.notna(row['OFF STREET NAME']) and row['OFF STREET NAME'] != '': street_names.append(row['OFF STREET NAME']) street_types.append('OFF STREET') # Join the names and types with a comma df.at[index, 'STREET NAME'] = ', '.join(street_names) df.at[index, 'STREET TYPE'] = ', '.join(street_types) # Convert specific columns to numeric numeric_columns = ['NUMBER OF PERSONS INJURED', 'NUMBER OF PEDESTRIANS INJURED', 'NUMBER OF CYCLIST INJURED', 'NUMBER OF MOTORIST INJURED', 'NUMBER OF PERSONS KILLED', 'NUMBER OF PEDESTRIANS KILLED', 'NUMBER OF CYCLIST KILLED', 'NUMBER OF MOTORIST KILLED'] for column in numeric_columns: df[column] = pd.to_numeric(df[column], errors='coerce').fillna(0).astype(int) # add new columns 'NUMBER OF INJURIES' and 'NUMBER OF DEATHS' df['NUMBER OF INJURIES'] = df['NUMBER OF PERSONS INJURED'] + df['NUMBER OF PEDESTRIANS INJURED'] + df['NUMBER OF CYCLIST INJURED'] + df['NUMBER OF MOTORIST INJURED'] df['NUMBER OF DEATHS'] = df['NUMBER OF PERSONS KILLED'] + df['NUMBER OF PEDESTRIANS KILLED'] + df['NUMBER OF CYCLIST KILLED'] + df['NUMBER OF MOTORIST KILLED'] # Keeping only the necessary columns columns_to_keep = [ 'CRASH DATE', 'BOROUGH', 'ZIP CODE', 'LATITUDE', 'LONGITUDE', 'COLLISION_ID', 'CRASH TIME PERIOD', 'CONTRIBUTING FACTOR VEHICLES', 'VEHICLE TYPES', 'NUMBER OF INJURIES', 'NUMBER OF DEATHS', 'STREET NAME', 'STREET TYPE' ] df = df[columns_to_keep] # Create a SearchEngine object search = SearchEngine() # Convert 'LATITUDE' and 'LONGITUDE' to floats df['LATITUDE'] = pd.to_numeric(df['LATITUDE'], errors='coerce') df['LONGITUDE'] = pd.to_numeric(df['LONGITUDE'], errors='coerce') # fill in the missing 'ZIP CODE' if it has valid 'LATITUDE' and 'LONGITUDE' for index, row in df.iterrows(): # Check if 'ZIP CODE' is an empty string and 'LATITUDE' and 'LONGITUDE' are valid if row['ZIP CODE'] == '' and not (pd.isna(row['LATITUDE']) or row['LATITUDE'] == 0) and not (pd.isna(row['LONGITUDE']) or row['LONGITUDE'] == 0): result = search.by_coordinates(lat=row['LATITUDE'], lng=row['LONGITUDE'], returns=1) if result: # Set the 'ZIP CODE' to the found zip code df.at[index, 'ZIP CODE'] = result[0].zipcode # dataset path to NYC_borough_data JSON dataset_path = "xx103/NYC_Motor_Vehicle_Collisions_and_Weather_Dataset" data_files2 = ["df2_NYC_borough_data.json"] dataset2 = load_dataset(path=dataset_path, data_files=data_files2) # Convert to pandas DataFrame df2 = dataset2['train'].to_pandas() # Convert the 'Borough' column to uppercase df2['Borough'] = df2['Borough'].str.upper() # Create a mapping dictionary from ZIP Code to Borough zip_to_borough = df2.set_index('ZIP Code')['Borough'].to_dict() # Function to update BOROUGH based on ZIP CODE def update_borough(row): if pd.isna(row['BOROUGH']) or row['BOROUGH'] == '': return zip_to_borough.get(row['ZIP CODE'], row['BOROUGH']) else: return row['BOROUGH'] # Apply the function to each row in df df['BOROUGH'] = df.apply(update_borough, axis=1) # dataset path to NYC_collisions_data JSON dataset_path = "xx103/NYC_Motor_Vehicle_Collisions_and_Weather_Dataset" data_files3 = ["df3_NYC_weather_data.json"] dataset3 = load_dataset(path=dataset_path, data_files=data_files3) # Convert to pandas DataFrame df3 = dataset3['train'].to_pandas() # Keep only the specified columns df3 = df3[['datetime', 'description', 'precip', 'preciptype', 'tempmax', 'tempmin']] # Rename the columns df3.rename(columns={ 'description': 'WEATHER DESCRIPTION', 'precip': 'PRECIPITATION', 'preciptype': 'PRECIPITATION TYPE', 'tempmax': 'TEMPMAX', 'tempmin': 'TEMPMIN' }, inplace=True) # Convert 'CRASH DATE' to datetime and remove the time component df['CRASH DATE'] = pd.to_datetime(df['CRASH DATE']).dt.date # Convert 'datetime' in df3 to datetime and remove the time component df3['datetime'] = pd.to_datetime(df3['datetime']).dt.date # Perform the merge merged_df = pd.merge(left=df, right=df3, how='left', left_on='CRASH DATE', right_on='datetime') # Drop the 'datetime' column from df3 as it's redundant now merged_df.drop(columns=['datetime'], inplace=True) # print the first row of merged_df # print(merged_df.iloc[0]) # Specify the path to the new JSON file # new_file_path = '/Users/suzyxie/Desktop/hugging_face_data/NYC_collisions_weather_merged_data.json' # Save the DataFrame to a JSON file # merged_df.to_json(new_file_path, orient='records', lines=True)