Flipkart Product Data Scraper & Analyzer

Published: Apr 25, 2023
Technologies: PythonWeb ScrapingBeautifulSoupPandasData CleaningE-commerce Analytics
Quick Access: Source Code

Web scraping pipeline built with BeautifulSoup and Pandas to extract product listings from Flipkart search results, collecting names, prices, ratings, and review counts, with automated data cleaning and CSV export for e-commerce market analysis.

The Challenge

E-commerce market research and competitive analysis require systematic collection of product data across categories, but Flipkart lacks a public API for bulk data extraction. Manual copying of product information (names, prices, ratings, review counts, URLs) from multiple search result pages is time-consuming and error-prone. Analysts need automated tools to gather structured product datasets for price comparison, trend analysis, and identifying highly-rated products. Traditional copy-paste methods don't scale when analyzing hundreds of products across different categories.

Solution & Approach

Web Scraping with BeautifulSoup: Built automated scraper using requests library to fetch Flipkart search result pages and BeautifulSoup4 for HTML parsing. Implemented CSS class selector navigation to extract specific DOM elements containing product information. Added error handling with try-except blocks for robust link extraction when product URLs are malformed or missing
Dynamic Search Query Input: Designed interactive script accepting user input for product search terms, enabling flexible scraping across different product categories without code modifications. Scraper navigates multiple pages (3 hardcoded pages per search) by constructing URL query parameters with page numbers
Multi-Field Data Extraction: Extracted 5 key product attributes per listing - Product Name (from div._4rR01T), Price (from div._30jeq3), Rating (from div._3LWZlK), Total Ratings count (from span._2_R_DZ), and Product Link (href from anchor tags). Parsed rating counts by splitting on whitespace to separate numeric values from text
Pandas Data Cleaning Pipeline: Implemented comprehensive cleaning - removed rows with missing Product Names using dropna(), eliminated duplicates across all columns with drop_duplicates(), filtered malformed header rows where 'Product Name' equals literal string 'Product Name', stripped commas from rating counts using str.replace(), and converted Total Ratings to integer dtype for numerical analysis
Rating-Based Filtering & Analysis: Created derived 'Highly Ratings' boolean column flagging products with >1000 total ratings. Sorted products by Total Ratings in descending order to identify market leaders. Filtered dataset to highly-rated products subset for focused analysis of popular items
CSV Export & HTML Display: Saved cleaned data to CSV files (flipkart_details2.csv, flipkart_link.csv) for external analysis in Excel/BI tools. Generated interactive HTML tables with clickable product links using IPython.display.HTML and pandas to_html() for Jupyter notebook visualization of top 5 highly-rated products

Tech Stack & Tools

Python 3.xBeautifulSoup4 (bs4)Requests libraryPandas for data manipulationJupyter NotebookIPython.display for HTML rendering

Impact & Results

BeautifulSoup4 + CSS

Scraping Method

5 attributes/product

Data Fields

3 per search

Pages Scraped

CSV + HTML

Output Format

>1000 ratings

Filtering Threshold

Automated pipeline

Data Cleaning

Project Visuals

Web scraping pipeline architecture — End-to-end pipeline from HTTP requests to CSV export with data cleaning

BeautifulSoup DOM parsing workflow — CSS selector-based extraction of product attributes from Flipkart HTML

Scraped product data output — Sample extracted product listings with ratings, prices, and filtered results

Data cleaning and analysis metrics — Data quality metrics showing cleaning pipeline effectiveness and rating distribution

View Source

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