Scrapy Jobs

I need a straightforward Python script that reliably pulls text content from websites I will specify once the project starts. The focus is strictly on text—no images or media files—so the routine should locate, extract, and save headings, paragraphs, or other copy in a clean, structured format such as CSV or JSON. Because the target sites may vary, the code must be written with easy-to-tweak selectors and robust error handling. Please use widely supported libraries (requests, BeautifulSoup, or Scrapy if you prefer) and include polite scraping practices such as user-agent rotation, rate limiting, and graceful retries on common HTTP errors. Deliverables: • A well-commented .py file ready to run from the command line • A brief README explaining any required packages...

I need a reliable script that can harvest contact details from a set of company websites I will provide. The information I’m after is any publicly listed email address, phone number and physical address that appears on those sites. Please build the scraper in a way that I can rerun it easily—Python with BeautifulSoup, Scrapy or Selenium is perfect as long as the code is well-commented and the output is clean. I prefer a single CSV (or JSON if you feel it suits the data better) with one row per company and distinct columns for each field you capture. Accuracy and completeness matter more than sheer speed, so let me know how long you’ll need once you see the list of domains. If you have questions about structure or formatting, ask before you dive in.

I have roughly ten thousand ISBN-13 codes and I need a production-ready Python pipeline that can take those codes, pull the corresponding book details from , , and a small set of external APIs, then push the cleaned results straight into a Google Sheets workbook. The pipeline must • survive Amazon’s throttling, bot checks, and page format changes without manual babysitting, • finish a full run on 10 k titles in a single session without crashing or silently skipping rows, and • give me fields that are already matched and normalised so downstream staff can link them to our catalogue instantly. Architecture is up to you: Scrapy, Playwright, headless Chrome, rotating residential proxies, Selenium, or a custom HTTP solution—whichever mix keeps the request foot...

I have roughly ten thousand ISBN-13 codes and I need a production-ready Python pipeline that can take those codes, pull the corresponding book details from , , and a small set of external APIs, then push the cleaned results straight into a Google Sheets workbook. The pipeline must • survive Amazon’s throttling, bot checks, and page format changes without manual babysitting, • finish a full run on 10 k titles in a single session without crashing or silently skipping rows, and • give me fields that are already matched and normalised so downstream staff can link them to our catalogue instantly. Architecture is up to you: Scrapy, Playwright, headless Chrome, rotating residential proxies, Selenium, or a custom HTTP solution—whichever mix keeps the request foot...

I’m compiling a comprehensive list of Australian-based IT companies and service providers. For each company I need two things captured accurately: up-to-date contact details (website, email, phone, physical address where available) and a clear summary of the services they offer. Your task is to source this information via web scraping, clean it, and deliver it in a structured spreadsheet or CSV . The data must be gathered only from publicly available, reputable sources; if you already know reliable sites or industry listings, feel free to leverage them, and you can also suggest new sources if they improve coverage. I’ll provide any seed lists I have once we start, but please assume you’ll need to expand well beyond those. Acceptance criteria • Minimum record ...