Upload data_processing_final.py

data_processing_final.py

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+from botocore import UNSIGNED
+from botocore.client import Config
+import pandas as pd
+import boto3
+from datetime import datetime
+from uszipcode import SearchEngine
+import numpy as np
+
+
+# Initialize the S3 client
+s3 = boto3.client('s3', config=Config(signature_version=UNSIGNED))
+
+# Download 3 json files in S3 bucket, specified bucket name, file keys, and file names 
+bucket_name = 'sta663project1'
+file_keys = ['NYC_collisions_data.json', 'NYC_borough_data.json', 'NYC_weather_data.json']
+local_file_names = ['NYC_collisions_data.json', 'NYC_borough_data.json', 'NYC_weather_data.json']
+
+for file_key, local_file_name in zip(file_keys, local_file_names):
+    s3.download_file(bucket_name, file_key, local_file_name)
+
+# Load each file into a DataFrame, df is NYC collisions data, df2 is NYC borough data, df3 is NYC weather data
+df = pd.read_json(local_file_names[0])
+df2 = pd.read_json(local_file_names[1])
+df3 = pd.read_json(local_file_names[2])
+
+# convert 'CRASH TIME' to datetime to further extract the hours and minutes
+df['CRASH TIME'] = pd.to_datetime(df['CRASH TIME'], format='%H:%M')
+
+# In NYC collisions data, 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 number of injured, and number of killed 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 that are needed to merge with df2 and df3
+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' in NYC collisions data 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
+
+# Convert the 'Borough' column in NYC borough data 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()
+
+# Write a function update_borough 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 NYC collisions data
+df['BOROUGH'] = df.apply(update_borough, axis=1)
+
+# Keep only the specified columns in NYC weather data
+df3 = df3[['datetime', 'description', 'precip', 'preciptype', 'tempmax', 'tempmin']]
+
+# Rename the columns in NYC weather data
+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 NYC weather data to datetime and remove the time component
+df3['datetime'] = pd.to_datetime(df3['datetime']).dt.date
+
+# Merge NYC collisions data and NYC weather data on 'CRASH DATE' and 'datetime' respectively, using left join.
+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)
+
+
+# Convert 'CRASH DATE' column to string to avoid messy date columns and conversion issues in Hugging Face
+merged_df['CRASH DATE'] = merged_df['CRASH DATE'].astype(str)
+
+# Replace empty values with NaN
+merged_df = merged_df.replace('', np.nan)
+
+# Print the first row of merged_df
+print(merged_df.iloc[0])