numbeer1ii.py

import pandas as pd

# Step 1: Load Data
data = pd.read_csv("trips_by_distance.csv")



# Step 2: Data Pre-processing
# Handle Missing Values
# Remove Duplicates
# Validate Data (if necessary)

# Step 3: Data Categorization
# Identify movements away from home
# For example, filter rows where "Population Not Staying at Home" is nonzero
away_from_home_data = data[data['Population Not Staying at Home'] > 0]

# Step 4: Data Aggregation
# Calculate the total distance traveled by individuals when away from home
# We'll sum up the products of the number of trips in each distance range by their respective midpoint
total_distance_away_from_home = (
    (away_from_home_data['Number of Trips <1'] * 0.5) +
    (away_from_home_data['Number of Trips 1-3'] * 2) +
    (away_from_home_data['Number of Trips 3-5'] * 4) +
    (away_from_home_data['Number of Trips 5-10'] * 7.5) +
    (away_from_home_data['Number of Trips 10-25'] * 17.5) +
    (away_from_home_data['Number of Trips 25-50'] * 37.5) +
    (away_from_home_data['Number of Trips 50-100'] * 75) +
    (away_from_home_data['Number of Trips 100-250'] * 175) +
    (away_from_home_data['Number of Trips 250-500'] * 375) +
    (away_from_home_data['Number of Trips >=500'] * 750)
)

# Calculate the total number of trips when away from home
total_trips_away_from_home = away_from_home_data['Number of Trips'].sum()

# Calculate the average distance traveled per trip when away from home
average_distance_per_trip_away_from_home = total_distance_away_from_home.sum() / total_trips_away_from_home

# Display the Result
print("Average distance traveled per trip when away from home:", average_distance_per_trip_away_from_home)