Supermarket Price Comparison

This project analyzes product prices across three Spanish supermarkets (Eroski, Dia, and Alcampo) to identify which store offers the lowest prices.

Project Overview

Using web scraping and APIs, we gathered pricing data for various products across the three supermarkets. Our Python code allows you to search for any product, returning a dataframe with that product's prices across stores. We further analyzed a sample of 10 products to identify the supermarket with the most frequent lowest prices and created a heatmap to visualize the price distribution.

Workflow

1. Data Extraction:

   • Used web scraping and APIs to collect product data, including names, prices, and package sizes, from each supermarket’s website.

2. Data Cleaning & Transformation:

   • Utilized replace, strip, and str.contains to standardize product data and remove irrelevant items.
   • Aggregated and sorted product data using concat and sort_values for each store to prepare for analysis.

3. Analysis & Visualization:

   • Calculated average prices per supermarket with groupby and mean.
   • Used value_counts to identify the store with the lowest prices most frequently.
   • Visualized the results through a bar chart and a price heatmap for a clear comparison across products and stores.

Installation

1. Clone the repository:

   git clone https://github.com/your_user/supermarket-price-comparison.git
   cd supermarket-price-comparison

2. Install requirements:

   pip install -r requirements.txt

Usage

Run the main analysis script to retrieve prices and generate visualizations. Adjust the search parameters to analyze the price distribution of any desired product across the supermarkets.

Technologies Used

• Python for data analysis and processing.
• Pandas for data cleaning and transformation.
• Matplotlib & Seaborn for data visualization.
• Web Scraping & APIs for data extraction from the supermarkets.