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To collect job postings with Python, first confirm that the source permits your intended access. Prefer an official API or partner integration where one is available. For an allowed, server-rendered page, fetch its HTML with Requests and parse it with Beautiful Soup or lxml; use Scrapy for a larger crawl, or Playwright or Selenium only when the site permits browser automation. The example below shows the pattern for a page you are authorized to collect from—it is not a universal Indeed or LinkedIn scraper.
Choose a permitted source and access method
Before writing a scraper, check the source’s terms, applicable API rules, and robot-exclusion directives. Permission to view a page in a browser is not, by itself, permission to collect, store, or reuse its contents. Keep the collection within the permitted scope, and stop if the site blocks requests or changes its access rules.
If a source offers an API or partner program that covers your use case, investigate that first. Indeed’s developer documentation covers APIs for jobs, candidates, employers, and search integrations; its Job Sync API is a GraphQL API for ATS partners to create, update, expire, and check the status of job postings. Those descriptions do not mean every developer can freely query all Indeed listings. Indeed’s Developer Agreement restricts uses including copying, redistribution, permanent database creation, algorithmic query generation, and attempts to bypass access limits.
LinkedIn documents an approval and vetting process for Job Posting API integrations in its Job Posting API terms. Its crawling terms say automated crawling and indexing without express permission is prohibited; permitted crawling must follow authorized paths and robot-exclusion restrictions. LinkedIn’s prohibited software guidance also says third-party software, crawlers, bots, browser plug-ins, and scripts that scrape or automate activity are not permitted on its services. Do not treat browser automation as a workaround.
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Match the tool to the page and scale
| Approach | Use it when | Trade-off |
|---|---|---|
| Official API or partner integration | The provider documents an API that covers your purpose and grants access. | Access, fields, and usage depend on the provider’s terms and approval process. |
| Requests plus Beautiful Soup or lxml | The page is server-rendered and HTML collection is permitted. | Simple and light, but selectors can break when markup changes. |
| Scrapy | You have permission to visit many pages and need a crawl queue, retries, and item pipelines. | More setup than a one-page script; it does not grant access that the source disallows. |
| Playwright or Selenium | The permitted page requires JavaScript to render the listing data. | Uses a browser and more resources; automation must still be allowed by the site. |
Beautiful Soup, Scrapy, Selenium, and Requests are among the Python scraping tools discussed in Web Scraping with Python. Tool choice does not change the permission question: use only the access method and pages the source allows.
Build a small, polite Requests scraper
For a permitted server-rendered listing page, start with one request and inspect the HTML before expanding to pagination. The example below expects each posting to use an article[data-job] element and the listed data-* attributes. Those selectors are an example contract, not selectors for Indeed, LinkedIn, or any other named job board; adapt them to the permitted source’s actual markup. Install the dependencies with python -m pip install requests beautifulsoup4.
import csv
import time
from datetime import datetime, timezone
from urllib.parse import urljoin, urldefrag
import requests
from bs4 import BeautifulSoup
from requests.adapters import HTTPAdapter
from urllib3.util.retry import Retry
START_URL = "https://example.com/jobs"
OUTPUT_CSV = "jobs.csv"
MAX_PAGES = 5 # Keep within the source's permitted scope.
session = requests.Session()
session.headers.update({
"User-Agent": "JobListingResearch/1.0 (contact: you@example.com)"
})
retry = Retry(
total=3,
backoff_factor=1,
status_forcelist=(429, 500, 502, 503, 504),
allowed_methods=frozenset(["GET"]),
respect_retry_after_header=True,
)
session.mount("https://", HTTPAdapter(max_retries=retry))
session.mount("http://", HTTPAdapter(max_retries=retry))
def text_or_empty(node):
return node.get_text(" ", strip=True) if node else ""
def absolute_url(href, page_url):
if not href:
return ""
return urldefrag(urljoin(page_url, href))[0]
def parse_page(html, page_url, retrieved_at):
soup = BeautifulSoup(html, "html.parser")
jobs = []
for card in soup.select("article[data-job]"):
link = card.select_one("a[data-job-url]")
jobs.append({
"title": text_or_empty(card.select_one("[data-job-title]")),
"employer": text_or_empty(card.select_one("[data-employer]")),
"location": text_or_empty(card.select_one("[data-location]")),
"description": text_or_empty(card.select_one("[data-description]")),
"employment_type": text_or_empty(card.select_one("[data-employment-type]")),
"salary": text_or_empty(card.select_one("[data-salary]")),
"posting_url": absolute_url(link.get("href") if link else "", page_url),
"source_url": page_url,
"retrieved_at_utc": retrieved_at,
})
next_link = soup.select_one("a[rel='next']")
next_url = absolute_url(next_link.get("href") if next_link else "", page_url)
return jobs, next_url
records = []
seen_urls = set()
page_url = START_URL
for page_number in range(MAX_PAGES):
if not page_url:
break
response = session.get(page_url, timeout=(10, 30))
response.raise_for_status()
retrieved_at = datetime.now(timezone.utc).isoformat()
page_jobs, next_url = parse_page(response.text, response.url, retrieved_at)
for job in page_jobs:
key = job["posting_url"] or (job["title"], job["employer"], job["location"])
if key not in seen_urls:
seen_urls.add(key)
records.append(job)
page_url = next_url
if page_url:
time.sleep(2) # Set a conservative rate consistent with the source's rules.
fields = [
"title", "employer", "location", "description", "employment_type",
"salary", "posting_url", "source_url", "retrieved_at_utc",
]
with open(OUTPUT_CSV, "w", newline="", encoding="utf-8-sig") as csvfile:
writer = csv.DictWriter(csvfile, fieldnames=fields)
writer.writeheader()
writer.writerows(records)
print(f"Saved {len(records)} postings to {OUTPUT_CSV}")Change START_URL to a page you may collect and update the selectors after inspecting its HTML. A successful HTTP response with zero records usually means the page uses different markup, the content is JavaScript-rendered, or the page no longer contains listings; do not respond by guessing selectors or increasing crawl volume blindly. The script follows only a visible rel="next" link and caps the number of pages. Adjust both behavior and request interval to the source’s permitted scope.
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What the records preserve
The CSV separates posting data from collection metadata. Keep a canonical posting URL or stable source ID when available so later runs can deduplicate records. The script uses a URL as its preferred key and falls back to title, employer, and location only when no URL is present; that fallback is less reliable because two distinct jobs can share those fields.
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Salary is collected only when the page shows it. Store the displayed value faithfully, including currency and pay period if present; do not infer compensation from a title, location, or similar listing. Likewise, an empty field means the source did not provide a value in the parsed markup, not that the value is zero. The UTC retrieval timestamp records when your script saw the page; it is not the employer’s publication or update time.
When the page needs JavaScript
If the permitted page’s listing cards do not appear in the HTML returned by Requests, first check for a documented API or a server-rendered version that the site authorizes. If browser automation is expressly allowed, Playwright can render the page before parsing its HTML. Install it with python -m pip install playwright and python -m playwright install chromium.
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from playwright.sync_api import sync_playwright
from bs4 import BeautifulSoup
url = "https://example.com/jobs" # Use only a permitted page.
with sync_playwright() as p:
browser = p.chromium.launch(headless=True)
page = browser.new_page()
page.goto(url, wait_until="domcontentloaded", timeout=30000)
page.locator("article[data-job]").first.wait_for(timeout=10000)
html = page.content()
browser.close()
jobs, next_url = parse_page(html, url, "")
print(f"Rendered {len(jobs)} job cards; next page: {next_url or 'none'}")This uses the same example selectors as the Requests script and calls its parse_page function, so keep that function in the same Python file or import it from your scraper module. A selector wait is preferable to an arbitrary long sleep, but it is not a way around access controls. Do not automate a source whose rules prohibit it, defeat bot checks or CAPTCHAs, or continue after a block.
Or skip the browser setup
ScreenshotNeo is a website screenshot API, not a job-data extraction API: a screenshot does not give you structured job fields or permission to collect postings. It can help capture an authorized listing page for visual review. Its API accepts a URL and returns an image or PDF; its clean-shot steps accept cookie or consent banners like a visitor and remove more than 60 known consent platforms, newsletter popups, and chat widgets, with each step able to be turned off. Bot checks or CAPTCHAs, blank pages, timeouts, failed loads, and cache hits cost nothing, and responses identify the page verdict and billing status in headers. An MCP server provides take_screenshot, get_page_info, and capture_pdf tools for AI agents and MCP clients.
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import requests
r = requests.get(
"https://api.screenshotneo.com/v1/shot",
params={"access_key": "YOUR_API_KEY", "url": "https://example.com/jobs"},
timeout=90,
)
r.raise_for_status()
open("job-listing.webp", "wb").write(r.content)See the ScreenshotNeo API documentation for request options and response details. Plans include 1,000 screenshots per month free with no card; paid plans start at $5 for 3,000. Sign up for 1,000 free screenshots a month with no card.
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Make collection useful without overstating the data
Normalize carefully
Preserve the original title, employer, location, and salary text alongside any normalized fields you add. Normalize whitespace and standardize formats only when the source supports the transformation. Locations may be ambiguous, and salary displays can use different currencies, ranges, and time units; keep the raw value so a later analyst can audit normalization. Do not turn missing values into estimates.
Choose storage for the job
CSV is convenient for a small export or spreadsheet workflow. For repeated collection, SQLite or a data warehouse makes it easier to track source IDs, retrieval history, and changes without overwriting earlier observations. Keep raw response metadata or an authorized archival representation when appropriate, but follow the source’s retention and redistribution terms. A posting’s presence in your dataset should not be mistaken for evidence that it remains open.
Control freshness and duplicates
Deduplicate with the source’s stable posting ID or canonical URL where available. Listings can be reposted, updated, or removed, so record retrieval time separately from any publication or update time displayed on the page. If you need current openings, revisit only at a frequency allowed by the source and mark records that are no longer present instead of silently treating old rows as active.
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Troubleshoot common failures
- HTTP 403 or a bot check: The site is denying the request. Recheck permission and approved access routes, then stop if access is not authorized. Do not rotate identities, evade a CAPTCHA, or use browser automation to defeat the restriction.
- HTTP 429 or repeated rate limits: Reduce request frequency, honor any
Retry-Afterresponse, and narrow the collection. If the source offers an API or partner route, use its documented process instead. - HTTP 404: The URL may be stale or the listing removed. Check the source’s current navigation or API documentation; do not generate arbitrary URL patterns to probe for records.
- 200 response but no cards: Inspect the returned HTML and confirm the page is the expected listing, then update selectors for actual markup. If data is rendered only in a browser, use an authorized API or an explicitly permitted browser workflow.
- Timeout or connection error: Keep finite connect/read timeouts, retry only transient failures with backoff, and reduce concurrency. Repeated retries against a struggling or blocking site can make the problem worse.
- Duplicate rows: Prefer stable IDs or canonical posting URLs. If only a composite of title, employer, and location is available, treat it as an imperfect deduplication key.
- Broken CSV columns or garbled text: Use a CSV writer rather than concatenating strings, open the file as UTF-8, and preserve multiline descriptions as one quoted field. The sample uses
utf-8-sigfor spreadsheet compatibility.
Keep the scraper maintainable
Run a small permitted sample before expanding the crawl. Log the requested URL, response status, retrieval time, record count, and parsing errors; monitor changes in empty fields and duplicates as well as exceptions. If the page structure changes or the source signals that access is no longer allowed, pause collection and reassess instead of silently emitting incomplete records. Scrapy can help organize queues, retries, and item pipelines for an authorized multi-page project, but it cannot make prohibited collection permissible.
For a practical introduction to common Python scraping techniques and libraries, the linked Web Scraping with Python resource covers Requests, Beautiful Soup, Scrapy, and Selenium.
Frequently Asked Questions
Can I infer a missing salary from the job title or location?
No. Keep the salary field empty or mark it as not provided; an estimate based on title or location is not data reported by the employer.
Does a successful request mean a posting is still open?
No. It only shows that the page was retrievable when you collected it. Check the source again and keep retrieval time distinct from any posting status or date.
Quick Recap
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