1adata.py

import csv
import numpy as np
import pandas as pd
from dask import dataframe as dd
import dask.dataframe as dd
# Histogram visualisation does not work without this.
import matplotlib
matplotlib.use('Agg')  # Use the Agg backend
import matplotlib.pyplot as plt


# Load the CSV file using Dask
ddf = dd.read_csv('Trips_Full_Data.csv')
ddd = dd.read_csv('Trips_by_Distance.csv', usecols=['Week', 'Population Staying at Home','Number of Trips'], dtype={'Population Staying at Home': 'float64', 'Number of Trips': 'float64'})


# How many people are staying at home - find the average of the columns in TFD that are staying at home


# Calculate the average people staying at home using Dask
average = ddf['Population Staying at Home'].mean().compute() # Overall average
# Convert the average to an int otherwise it'll output in standard form
average_int = average.astype('int64')
print("Average number of people staying at home:", average_int)
# Data cleaning
# Fill null values
ddd['Population Staying at Home'] = ddd['Population Staying at Home'].fillna(0)
# Convert floats to ints so that the mean() works
ddd['Population Staying at Home'] = ddd['Population Staying at Home'].round().astype('int64')


# Group by 'Week' and calculate the average of 'Population Staying at Home' for each week
average_per_week = ddd.groupby('Week')['Population Staying at Home'].mean()
avperweek = average_per_week.compute()
# Convert the avperweek to integer otherwise it gives it to you in standard form
avperweek_int = avperweek.astype('int64')
print("Average number of people staying at home per week", avperweek_int)

fig = plt.figure(figsize=(10, 6))
plt.bar(range(len(avperweek_int)), avperweek_int, width=0.4, color='orange')
plt.xlabel("Week")
plt.xticks(range(len(avperweek_int)), rotation=45)  # Display all week numbers
plt.ylabel("Average number of people staying at home")
plt.title("Average number of people staying at home per week")
plt.rcParams.update({
    'text.color': "black",
    'axes.labelcolor': "black",
    'xtick.color': "black",
    'ytick.color': "black",
    'font.size': 10
})  # Change text color and size for better readability
plt.tight_layout()
plt.savefig('bar_plot1.png')

# Documentation references: https://docs.dask.org/en/stable/generated/dask.dataframe.DataFrame.astype.html

# How far are people traveling when they dont stay at home, find the average of how far people have travelled when they're not staying at home
df_data = dd.read_csv('Trips_Full_Data.csv', dtype = {'Trips 1-25 Miles': 'float64',
                                                          'Trips 1-3 Miles': 'float64',
                                                          'Trips 10-25 Miles': 'float64',
                                                          'Trips 100-250 Miles': 'float64',
                                                          'Trips 25-50 Miles': 'float64',
                                                          'Trips 250-500 Miles': 'float64',
                                                          'Trips 3-5 Miles': 'float64',
                                                          'Trips 5-10 Miles': 'float64',
                                                          'Trips 50-100 Miles': 'float64',
                                                          'Trips <1 Mile': 'float64',
                                                          'Trips >=500 Miles': 'float64',
                                                          'Population Not Staying at Home': 'float64',
                                                          'Population Staying at Home': 'float64',
                                                          'Week': 'float64'
                                                      })


Trips = [ 
    'Trips 1-25 Miles',
    'Trips 1-3 Miles',
    'Trips 10-25 Miles',
    'Trips 100-250 Miles',
    'Trips 100+ Miles',
    'Trips 25-100 Miles',
    'Trips 25-50 Miles',
    'Trips 250-500 Miles',
    'Trips 3-5 Miles',
    'Trips 5-10 Miles',
    'Trips 50-100 Miles',
    'Trips 500+ Miles'
]
# # Group and sum
# df_merge = df_data[Trips].sum().compute()
# print(df_merge)

# # Unique values
# ddf['Week of Date'].nunique().compute()
# how_far = ddf.groupby(by= "Week of Date")["Trips"].mean().compute()
# print("Dfmerge", df_merge)
# print("How far", how_far)
# # Convert the mean distance to int64
# how_far_int = how_far.astype('int64')

# #Print the mean distance
# print("How far are people travelling when they don't stay home on average:", how_far_int)

# #Barplot
# fig = plt.figure(figsize=(10, 6))
# plt.bar(range(len(how_far_int)), how_far_int, width=0.4,  color='orange')
# plt.xlabel("Week")
# plt.xticks(range(len(how_far_int)), rotation=45)  
# plt.ylabel("Total Trip Distance")
# plt.title("How far are people travelling when they don't stay home")
# plt.rcParams.update({
#     'text.color': "black",
#     'axes.labelcolor': "black",
#     'xtick.color': "black",
#     'ytick.color': "black",
#     'font.size': 10
# })  # Change text color and size for better readability
# plt.tight_layout()
# plt.savefig('bar_plot2data.png')  # Save the plot as an image file