Prerequisites and Objectives

This exercise requires the installation of the numpy, pandas, and matplotlib libraries. The primary goals are to perform operations on CSV files, conduct data analysis using pandas, and create visualizations with matplotlib.

Generating Simulated Sales Data

The following script creates a CSV file named data.csv containing simulated daily sales data for a retail store throughout the year 2022.

import csv
import random
from datetime import date, timedelta

output_file = 'data.csv'
base_sales = 500

with open(output_file, 'w', newline='') as file:
    writer = csv.writer(file)
    writer.writerow(['Date', 'Revenue'])  # Write header
    
    current_date = date(2022, 1, 1)
    for day in range(365):
        daily_revenue = base_sales + (day * 5) + random.randint(0, 99)
        writer.writerow([current_date.isoformat(), daily_revenue])
        current_date += timedelta(days=1)

Data Loading and Cleaning

Load the generated data into a pandas DataFrame and remove any rows with missing values.

import pandas as pd

sales_data = pd.read_csv('data.csv')
sales_data.dropna(inplace=True)

Creating a Daily Revenue Line Chart

Generate a line plot showing daily revenue trends and save it as daily_sales.jpg.

import matplotlib.pyplot as plt

plt.figure(figsize=(12, 6))
plt.plot(sales_data['Date'], sales_data['Revenue'], color='steelblue')
plt.title('Daily Store Revenue for 2022')
plt.xlabel('Date')
plt.ylabel('Revenue')
plt.xticks(rotation=45)
plt.tight_layout()
plt.savefig('daily_sales.jpg', dpi=100)
plt.show()

Craeting a Monthly Revenue Bar Chart

Aggregate the data by month, create a bar chart, and save it as monthly_sales.jpg.

sales_data['Date'] = pd.to_datetime(sales_data['Date'])
sales_data['Month'] = sales_data['Date'].dt.month
monthly_totals = sales_data.groupby('Month')['Revenue'].sum()

plt.figure(figsize=(10, 6))
plt.bar(monthly_totals.index, monthly_totals.values, color='seagreen')
plt.title('Total Revenue by Month')
plt.xlabel('Month')
plt.ylabel('Total Revenue')
plt.xticks(range(1, 13))
plt.grid(axis='y', linestyle='--', alpha=0.7)
plt.savefig('monthly_sales.jpg')
plt.show()

Identifying the Month with the Largest Revenue Increase

Calculate the month-over-month revenue change, find the largest increase, and write the corresponding month to a text file.

# Calculate revenue for each month
monthly_revenue = sales_data.groupby('Month')['Revenue'].sum().reset_index()

# Calculate the difference from the previous month
monthly_revenue['Previous_Month_Revenue'] = monthly_revenue['Revenue'].shift(1)
monthly_revenue['Increase'] = monthly_revenue['Revenue'] - monthly_revenue['Previous_Month_Revenue']

# Find the month with the maximum increase
max_increase_row = monthly_revenue.loc[monthly_revenue['Increase'].idxmax()]
target_month = int(max_increase_row['Month'])

# Write result to file
with open('peak_growth_month.txt', 'w') as f:
    f.write(f'{target_month}')

Creating a Quarterly Revenue Pie Chart

Group the data by fiscal quarter, create a pie chart showing the distribution, and save it as quarterly_distribution.jpg.

# Assign quarter based on month
sales_data['Quarter'] = sales_data['Date'].dt.quarter
quarterly_totals = sales_data.groupby('Quarter')['Revenue'].sum()

quarter_labels = ['Q1', 'Q2', 'Q3', 'Q4']
colors = ['gold', 'lightcoral', 'lightskyblue', 'lightgreen']

explode = (0.05, 0, 0, 0)  # Slightly separate the first slice

plt.figure(figsize=(8, 8))
plt.pie(quarterly_totals.values, labels=quarter_labels, colors=colors,
        autopct='%1.1f%%', startangle=140, explode=explode)
plt.title('2022 Revenue Distribution by Quarter')
plt.savefig('quarterly_distribution.jpg')
plt.show()