Quickstart

Scrape job postings from multiple job boards in under a minute.

1. Install JobSpy

   If you haven’t installed JobSpy yet, install it with pip:

   pip install -U python-jobspy
   

   Python 3.10 or higher is required. See Installation for full details.

2. Import and call scrape_jobs()

   Import scrape_jobs from the jobspy package and run your first search:

   import csv
   from jobspy import scrape_jobs
   
   jobs = scrape_jobs(
       site_name=["indeed", "linkedin", "zip_recruiter", "google"],
       search_term="software engineer",
       google_search_term="software engineer jobs near San Francisco, CA since yesterday",
       location="San Francisco, CA",
       results_wanted=20,
       hours_old=72,
       country_indeed="USA",
   )
   

   scrape_jobs() runs all specified boards concurrently and returns a single Pandas DataFrame.

   Start with indeed — it is currently the best-performing scraper with no rate limiting. LinkedIn is the most restrictive and typically rate limits around the 10th page with a single IP address.

3. View results

   Print a summary and preview the first few rows:

   print(f"Found {len(jobs)} jobs")
   print(jobs.head())
   

   The DataFrame contains the following key columns:

   Column	Description
   id	Platform-specific job identifier
   site	Job board the posting came from (e.g. indeed, linkedin)
   job_url	URL to the job posting
   job_url_direct	Direct employer application URL, when available
   title	Job title
   company	Company name
   location	City, state, and/or country as a display string
   date_posted	Date the job was posted
   job_type	Employment type: fulltime, parttime, internship, contract
   salary_source	direct_data or description (where salary was found)
   interval	Salary pay period: yearly, monthly, weekly, daily, hourly
   min_amount	Minimum salary amount
   max_amount	Maximum salary amount
   currency	Currency code (e.g. USD)
   is_remote	Whether the job is remote
   description	Full job description (Markdown by default)

4. Export results

   Save your results to CSV or Excel for further analysis.

   export to CSV

   import csv
   
   jobs.to_csv(
       "jobs.csv",
       quoting=csv.QUOTE_NONNUMERIC,
       escapechar="\\",
       index=False,
   )
   

   Use quoting=csv.QUOTE_NONNUMERIC and escapechar="\\" when exporting to CSV. Job descriptions often contain commas, quotes, and newlines that will break a plain CSV export.

   Your job results are now saved and ready for filtering, analysis, or loading into a database.

Complete example

Here is the full working script from start to finish:

import csv
from jobspy import scrape_jobs

jobs = scrape_jobs(
    site_name=["indeed", "linkedin", "zip_recruiter", "google"],
    search_term="software engineer",
    google_search_term="software engineer jobs near San Francisco, CA since yesterday",
    location="San Francisco, CA",
    results_wanted=20,
    hours_old=72,
    country_indeed="USA",
)

print(f"Found {len(jobs)} jobs")
print(jobs.head())

# Export to CSV
jobs.to_csv("jobs.csv", quoting=csv.QUOTE_NONNUMERIC, escapechar="\\", index=False)

# Export to Excel
jobs.to_excel("jobs.xlsx", index=False)

Next steps

scrape_jobs() reference

Full parameter reference for scrape_jobs(), including filtering, proxy, and LinkedIn options

Job board guides

Per-board usage notes, rate limit behavior, and country support