Retail Sales Data Analysis

A comprehensive data engineering project analyzing retail sales data using Python, Pandas, and data visualization techniques.

Project Overview

• This project demonstrates how to:

• Extract data from retail sales CSV files

• Transform and clean sales data

• Analyze sales performance and trends

• Generate visual reports and Excel exports

Tech Stack

• Python

• pandas - Data analysis

• matplotlib - Data visualization

• xlsxwriter - Excel reports

Process

1️⃣ Extract

  Reads sales data from retail_sales.csv file

2️⃣ Transform

   Cleans and processes the data:

   -  Handles missing values

   -  Converts data types

   -  Validates data quality

3️⃣ Analyze

   Performs sales analysis:

   - Product performance ranking

   - Sales trend analysis

   - Daily sales metrics

4️⃣ Report

   - Generates Excel reports and visual charts

   - Run the Pipeline

bash

Install dependencies

• pip install pandas matplotlib xlsxwriter openpyxl

Run the analysis

• python retail_sale_analyzer.py

Example Output

• Retail Sale Analyzer Starting...

Cleaned data:

• Removed 0 rows with missing values.

Total Sales per Product:

• Product A 495
• Product B 565
• Product C 635

Best Selling Product:

• Product C

Average Daily Sales:

• 56.5

Excel report generated successfully:

• sales_report.xlsx

• Analysis complete!

Analysis Summary:

• Total sales entries: 30

Product Performance:

• Product A: 495

• Product B: 565

• Product C: 635

Best Seller:

• Product C

• Average Daily Sales: 56.5

Create retail_sales.csv with these columns:

• Date (YYYY-MM-DD)

• Product (text)

• Sales (numeric)

Example:

• csv
• Date,Product,Sales
• 2025-01-01,Product A,50
• 2025-01-01,Product B,60
• 2025-01-01,Product C,70
• 2025-01-02,Product A,45
• 2025-01-02,Product B,55
• 2025-01-02,Product C,65
• 2025-01-03,Product A,35
• 2025-01-03,Product B,25
• 2025-01-03,Product C,15
• 2025-01-04,Product A,60
• 2025-01-04,Product B,70
• 2025-01-04,Product C,80
• 2025-01-05,Product A,40
• 2025-01-05,Product B,45
• 2025-01-05,Product C,55
• 2025-01-06,Product A,55
• 2025-01-06,Product B,65
• 2025-01-06,Product C,75
• 2025-01-07,Product A,30
• 2025-01-07,Product B,35
• 2025-01-07,Product C,40
• 2025-01-08,Product A,65
• 2025-01-08,Product B,75
• 2025-01-08,Product C,85
• 2025-01-09,Product A,45
• 2025-01-09,Product B,50
• 2025-01-09,Product C,60
• 2025-01-10,Product A,70
• 2025-01-10,Product B,80
• 2025-01-10,Product C,90

Retail Sales Data Analysis

A comprehensive data engineering project analyzing retail sales data using Python, Pandas, and data visualization techniques.

Chart View

Matthew Lawrence L

Bengaluru, Karnataka

lawrence82773824@gmail.com