Web Scraping with ChatGPT Code Interpreter is Mind-Blowing!

Introduction to Web Scraping with ChatGPT

In this video, the presenter demonstrates how to perform web scraping using the ChatGPT code interpreter. The method shown is straightforward and does not require any plugins or additional tools. The presenter starts by saving a webpage as an HTML file and then uploads it to the ChatGPT code interpreter. The goal is to extract specific elements from the HTML file, such as product names and prices, and export them into a CSV file.

Getting Started with Web Scraping

• Save the desired webpage as an HTML file by pressing Ctrl+S (or Command+S on Mac).

• Upload the saved HTML file to the ChatGPT code interpreter.

• Specify which elements to extract from the HTML file, such as product names and prices.

• Handle missing data in case some products do not have certain information.

Extracting Data Using ChatGPT

The presenter demonstrates how ChatGPT can extract data from webpages using specified prompts.

Extracting Data from a Webpage

• Provide prompts to instruct ChatGPT on what data to extract from the webpage.

• Verify that ChatGPT correctly extracts the desired information, such as product names and prices.

• Download the generated CSV file containing all scraped data for verification purposes.

Scraping Multiple Pages

The presenter shows how to scrape data from multiple pages of a website using ChatGPT.

Scraping Data from Additional Pages

• Save each page of interest as separate HTML files.

• Upload each HTML file individually to extract data using prompts specific to each page.

• Repeat this process for all desired pages of the website.

This summary provides an overview of key points covered in the transcript. For a more detailed understanding, please refer to the full transcript.

Scraping Data from Amazon

In this section, the speaker demonstrates how to scrape data from Amazon using a code interpreter. They show how to extract product information from multiple pages and export it into a single CSV file.

Extracting Data from Multiple Pages

• The speaker concatenates the data from multiple pages into one dataframe.

• They click on "Download Products Combined" to obtain the CSV file.

• The resulting file contains more rows, indicating successful scraping of data from both the first and second pages.

Scraping Data Continuously

• The speaker mentions that this approach can be used for scraping data from as many pages as desired.

• It is possible to continue the process with third, fourth, and fifth pages or any number of additional pages.

Scraping Data from Glassdoor

In this section, the speaker demonstrates an alternative approach to web scraping by extracting job data from Glassdoor using HTML files.

Saving HTML File for Web Scraping

• The speaker navigates to Glassdoor website and searches for job listings related to "data scientists."

• They save the webpage as an HTML file named "Glassdoor jobsearch.html."

Extracting Job Data Using IDs

• The speaker uses ChardDBT code interpreter and uploads the saved HTML file.

• They specify elements with specific IDs that they want to extract: company name, job title, location, and salary.

• By inspecting elements on the webpage, they identify corresponding IDs for each piece of information.

Handling Missing Data

• If certain elements do not have specific IDs or cannot be extracted using traditional methods, alternative approaches like analyzing specific elements can be used.

Exporting Data into CSV File

• ChardDBT is instructed to put extracted data into a table format and export it into a CSV file.

• In case of missing data, it is marked as "new data" in the CSV file.

Verification and Conclusion

In this section, the speaker verifies the extracted job data from Glassdoor by comparing it with the actual website.

Verifying Extracted Data

• The speaker downloads the CSV file and opens it to preview the extracted job data.

• They cross-reference the data with Glassdoor website to ensure accuracy.

• Examples of verified data include company names, job titles, and salaries.

Timestamps are provided for each section to help locate specific parts of the video.