1apt1.py

import pandas as pd
import matplotlib.pyplot as plt

# Reading the Trips by Distance data
df = pd.read_csv('Trips_by_Distance.csv')

# Since the 'Week' column exists in Trips_by_Distance.csv, proceed with original plan
unique_weeks = df['Week'].nunique()
avg_population_staying_home = df.groupby('Week')['Population Staying at Home'].mean()


df_full = pd.read_csv('Trips_Full Data.csv')  

trip_distance_columns = [col for col in df_full.columns if 'Trips' in col and 'Miles' in col]
avg_trips_by_distance = df_full.groupby('Week of Date')[trip_distance_columns].mean()

# Plotting the barplot for People staying at home vs Week
plt.figure(figsize=(10, 5))
plt.bar(avg_population_staying_home.index, avg_population_staying_home.values, label='Population Staying at Home')
plt.xlabel('Week')
plt.ylabel('Average Population Staying at Home')
plt.title('Average Population Staying at Home by Week')
plt.legend()
plt.xticks(rotation=90)
plt.tight_layout()

# Adjust the path as needed for your environment
plt.savefig('population_staying_home_vs_week.png')