Datasets:

xx103
/

NYC_Motor_Vehicle_Collisions_and_Weather_Dataset

@@ -1,154 +0,0 @@
-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)