Task 1 (a).py

#!/usr/bin/env python
# coding: utf-8

# In[1]:


import pandas as pd
import numpy as np
import dask.dataframe as dd
df=dd.read_csv('Cleaned_Trips_by_Distance.csv')
df['Week'].nunique().compute()
df.groupby(by = 'Week')['Population Staying at Home'].mean().compute()


# In[2]:


import matplotlib.pyplot as plt
grouped_data = df.groupby('Week')['Population Staying at Home'].mean().compute()
plt.figure(figsize=(10, 6))
grouped_data.plot(kind='bar')
plt.title('BarChart of People Staying at Home vs Week')
plt.xlabel('Week')
plt.ylabel('Average Population Staying at Home')
plt.show()



# In[5]:


df_full=pd.read_csv("Trips_Full Data.csv")
df_full=df_full.drop(columns=['Trips'])


# In[6]:


import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt

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


trip_columns = ['Trips <1 Mile', 'Trips 1-3 Miles', 'Trips 3-5 Miles', 
                'Trips 5-10 Miles', 'Trips 10-25 Miles', 'Trips 25-50 Miles', 
                'Trips 50-100 Miles', 'Trips 100-250 Miles', 'Trips 250-500 Miles', 
                'Trips 500+ Miles']

average_values = df_full[trip_columns].mean()
print(average_values)

import seaborn as sns
plt.figure(figsize=(12, 8))
sns.barplot(x=average_values.index, y=average_values.values, palette='viridis')
plt.xlabel('Distance Range', fontsize=12)
plt.ylabel('Average Number of Trips', fontsize=12)
plt.title('Average Number of Trips for Each Distance Range', fontsize=14)
plt.xticks(rotation=45, ha='right') 
plt.tight_layout()  
plt.show()


# In[ ]: