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39
.github/workflows/publish-to-pypi.yml
vendored
39
.github/workflows/publish-to-pypi.yml
vendored
@@ -1,33 +1,50 @@
|
||||
name: Publish Python 🐍 distributions 📦 to PyPI
|
||||
on: push
|
||||
on:
|
||||
pull_request:
|
||||
types:
|
||||
- closed
|
||||
|
||||
permissions:
|
||||
contents: write
|
||||
|
||||
jobs:
|
||||
build-n-publish:
|
||||
name: Build and publish Python 🐍 distributions 📦 to PyPI
|
||||
runs-on: ubuntu-latest
|
||||
|
||||
if: github.event.pull_request.merged == true && github.event.pull_request.base.ref == 'main'
|
||||
|
||||
steps:
|
||||
- uses: actions/checkout@v3
|
||||
|
||||
- name: Set up Python
|
||||
uses: actions/setup-python@v4
|
||||
with:
|
||||
python-version: "3.10"
|
||||
|
||||
- name: Install dependencies
|
||||
run: pip install toml
|
||||
|
||||
- name: Increment version
|
||||
run: python increment_version.py
|
||||
|
||||
- name: Commit version increment
|
||||
run: |
|
||||
git config --global user.name 'github-actions'
|
||||
git config --global user.email 'github-actions@github.com'
|
||||
git add pyproject.toml
|
||||
git commit -m 'Increment version'
|
||||
|
||||
- name: Push changes
|
||||
run: git push
|
||||
|
||||
- name: Install poetry
|
||||
run: >-
|
||||
python3 -m
|
||||
pip install
|
||||
poetry
|
||||
--user
|
||||
run: pip install poetry --user
|
||||
|
||||
- name: Build distribution 📦
|
||||
run: >-
|
||||
python3 -m
|
||||
poetry
|
||||
build
|
||||
run: poetry build
|
||||
|
||||
- name: Publish distribution 📦 to PyPI
|
||||
if: startsWith(github.ref, 'refs/tags')
|
||||
uses: pypa/gh-action-pypi-publish@release/v1
|
||||
with:
|
||||
password: ${{ secrets.PYPI_API_TOKEN }}
|
||||
123
README.md
123
README.md
@@ -2,14 +2,11 @@
|
||||
|
||||
**JobSpy** is a simple, yet comprehensive, job scraping library.
|
||||
|
||||
*Looking to build a data-focused software product?* **[Book a call](https://bunsly.com/)** *to
|
||||
work with us.*
|
||||
|
||||
## Features
|
||||
|
||||
- Scrapes job postings from **LinkedIn**, **Indeed**, **Glassdoor**, **Google**, & **ZipRecruiter** simultaneously
|
||||
- Aggregates the job postings in a Pandas DataFrame
|
||||
- Proxies support
|
||||
- Aggregates the job postings in a dataframe
|
||||
- Proxies support to bypass blocking
|
||||
|
||||

|
||||
|
||||
@@ -30,14 +27,14 @@ from jobspy import scrape_jobs
|
||||
jobs = scrape_jobs(
|
||||
site_name=["indeed", "linkedin", "zip_recruiter", "glassdoor", "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, # (only Linkedin/Indeed is hour specific, others round up to days old)
|
||||
country_indeed='USA', # only needed for indeed / glassdoor
|
||||
hours_old=72,
|
||||
country_indeed='USA',
|
||||
|
||||
# linkedin_fetch_description=True # get more info such as full description, direct job url for linkedin (slower)
|
||||
# linkedin_fetch_description=True # gets more info such as description, direct job url (slower)
|
||||
# proxies=["208.195.175.46:65095", "208.195.175.45:65095", "localhost"],
|
||||
|
||||
)
|
||||
print(f"Found {len(jobs)} jobs")
|
||||
print(jobs.head())
|
||||
@@ -65,6 +62,9 @@ Optional
|
||||
| (default is all)
|
||||
│
|
||||
├── search_term (str)
|
||||
|
|
||||
├── google_search_term (str)
|
||||
| search term for google jobs. This is the only param for filtering google jobs.
|
||||
│
|
||||
├── location (str)
|
||||
│
|
||||
@@ -84,7 +84,7 @@ Optional
|
||||
| number of job results to retrieve for each site specified in 'site_name'
|
||||
│
|
||||
├── easy_apply (bool):
|
||||
| filters for jobs that are hosted on the job board site
|
||||
| filters for jobs that are hosted on the job board site (LinkedIn easy apply filter no longer works)
|
||||
│
|
||||
├── description_format (str):
|
||||
| markdown, html (Format type of the job descriptions. Default is markdown.)
|
||||
@@ -129,51 +129,11 @@ Optional
|
||||
| - easy_apply
|
||||
```
|
||||
|
||||
|
||||
### JobPost Schema
|
||||
|
||||
```plaintext
|
||||
JobPost
|
||||
├── title
|
||||
├── company
|
||||
├── company_url
|
||||
├── job_url
|
||||
├── location
|
||||
│ ├── country
|
||||
│ ├── city
|
||||
│ ├── state
|
||||
├── description
|
||||
├── job_type: fulltime, parttime, internship, contract
|
||||
├── job_function
|
||||
│ ├── interval: yearly, monthly, weekly, daily, hourly
|
||||
│ ├── min_amount
|
||||
│ ├── max_amount
|
||||
│ ├── currency
|
||||
│ └── salary_source: direct_data, description (parsed from posting)
|
||||
├── date_posted
|
||||
├── emails
|
||||
└── is_remote
|
||||
|
||||
Linkedin specific
|
||||
└── job_level
|
||||
|
||||
Linkedin & Indeed specific
|
||||
└── company_industry
|
||||
|
||||
Indeed specific
|
||||
├── company_country
|
||||
├── company_addresses
|
||||
├── company_employees_label
|
||||
├── company_revenue_label
|
||||
├── company_description
|
||||
└── company_logo
|
||||
```
|
||||
|
||||
## Supported Countries for Job Searching
|
||||
|
||||
### **LinkedIn / Google**
|
||||
### **LinkedIn**
|
||||
|
||||
LinkedIn & Google searches globally & uses only the `location` parameter.
|
||||
LinkedIn searches globally & uses only the `location` parameter.
|
||||
|
||||
### **ZipRecruiter**
|
||||
|
||||
@@ -215,7 +175,23 @@ You can specify the following countries when searching on Indeed (use the exact
|
||||
|
||||
---
|
||||
**Q: Why is Indeed giving unrelated roles?**
|
||||
**A:** Indeed is searching each one of your terms e.g. software intern, it searches software OR intern. Try search_term='"software intern"' in quotes for stricter searching
|
||||
**A:** Indeed searches the description too.
|
||||
|
||||
- use - to remove words
|
||||
- "" for exact match
|
||||
|
||||
Example of a good Indeed query
|
||||
|
||||
```py
|
||||
search_term='"engineering intern" software summer (java OR python OR c++) 2025 -tax -marketing'
|
||||
```
|
||||
|
||||
This searches the description/title and must include software, summer, 2025, one of the languages, engineering intern exactly, no tax, no marketing.
|
||||
|
||||
---
|
||||
|
||||
**Q: No results when using "google"?**
|
||||
**A:** You have to use super specific syntax. Search for google jobs on your browser and then whatever pops up in the google jobs search box after applying some filters is what you need to copy & paste into the google_search_term.
|
||||
|
||||
---
|
||||
|
||||
@@ -227,8 +203,41 @@ You can specify the following countries when searching on Indeed (use the exact
|
||||
|
||||
---
|
||||
|
||||
**Q: Encountering issues with your queries?**
|
||||
**A:** Try reducing the number of `results_wanted` and/or broadening the filters. If problems
|
||||
persist, [submit an issue](https://github.com/Bunsly/JobSpy/issues).
|
||||
### JobPost Schema
|
||||
|
||||
---
|
||||
```plaintext
|
||||
JobPost
|
||||
├── title
|
||||
├── company
|
||||
├── company_url
|
||||
├── job_url
|
||||
├── location
|
||||
│ ├── country
|
||||
│ ├── city
|
||||
│ ├── state
|
||||
├── description
|
||||
├── job_type: fulltime, parttime, internship, contract
|
||||
├── job_function
|
||||
│ ├── interval: yearly, monthly, weekly, daily, hourly
|
||||
│ ├── min_amount
|
||||
│ ├── max_amount
|
||||
│ ├── currency
|
||||
│ └── salary_source: direct_data, description (parsed from posting)
|
||||
├── date_posted
|
||||
├── emails
|
||||
└── is_remote
|
||||
|
||||
Linkedin specific
|
||||
└── job_level
|
||||
|
||||
Linkedin & Indeed specific
|
||||
└── company_industry
|
||||
|
||||
Indeed specific
|
||||
├── company_country
|
||||
├── company_addresses
|
||||
├── company_employees_label
|
||||
├── company_revenue_label
|
||||
├── company_description
|
||||
└── company_logo
|
||||
```
|
||||
|
||||
21
increment_version.py
Normal file
21
increment_version.py
Normal file
@@ -0,0 +1,21 @@
|
||||
import toml
|
||||
|
||||
def increment_version(version):
|
||||
major, minor, patch = map(int, version.split('.'))
|
||||
patch += 1
|
||||
return f"{major}.{minor}.{patch}"
|
||||
|
||||
# Load pyproject.toml
|
||||
with open('pyproject.toml', 'r') as file:
|
||||
pyproject = toml.load(file)
|
||||
|
||||
# Increment the version
|
||||
current_version = pyproject['tool']['poetry']['version']
|
||||
new_version = increment_version(current_version)
|
||||
pyproject['tool']['poetry']['version'] = new_version
|
||||
|
||||
# Save the updated pyproject.toml
|
||||
with open('pyproject.toml', 'w') as file:
|
||||
toml.dump(pyproject, file)
|
||||
|
||||
print(f"Version updated from {current_version} to {new_version}")
|
||||
@@ -1,2 +0,0 @@
|
||||
[virtualenvs]
|
||||
in-project = true
|
||||
@@ -1,15 +1,21 @@
|
||||
[build-system]
|
||||
requires = [ "poetry-core",]
|
||||
build-backend = "poetry.core.masonry.api"
|
||||
|
||||
[tool.poetry]
|
||||
name = "python-jobspy"
|
||||
version = "1.1.74"
|
||||
version = "1.1.76"
|
||||
description = "Job scraper for LinkedIn, Indeed, Glassdoor & ZipRecruiter"
|
||||
authors = ["Zachary Hampton <zachary@bunsly.com>", "Cullen Watson <cullen@bunsly.com>"]
|
||||
authors = [ "Zachary Hampton <zachary@bunsly.com>", "Cullen Watson <cullen@bunsly.com>",]
|
||||
homepage = "https://github.com/Bunsly/JobSpy"
|
||||
readme = "README.md"
|
||||
keywords = ['jobs-scraper', 'linkedin', 'indeed', 'glassdoor', 'ziprecruiter']
|
||||
keywords = [ "jobs-scraper", "linkedin", "indeed", "glassdoor", "ziprecruiter",]
|
||||
[[tool.poetry.packages]]
|
||||
include = "jobspy"
|
||||
from = "src"
|
||||
|
||||
packages = [
|
||||
{ include = "jobspy", from = "src" }
|
||||
]
|
||||
[tool.black]
|
||||
line-length = 88
|
||||
|
||||
[tool.poetry.dependencies]
|
||||
python = "^3.10"
|
||||
@@ -22,16 +28,8 @@ tls-client = "^1.0.1"
|
||||
markdownify = "^0.13.1"
|
||||
regex = "^2024.4.28"
|
||||
|
||||
|
||||
[tool.poetry.group.dev.dependencies]
|
||||
pytest = "^7.4.1"
|
||||
jupyter = "^1.0.0"
|
||||
black = "*"
|
||||
pre-commit = "*"
|
||||
|
||||
[build-system]
|
||||
requires = ["poetry-core"]
|
||||
build-backend = "poetry.core.masonry.api"
|
||||
|
||||
[tool.black]
|
||||
line-length = 88
|
||||
|
||||
@@ -24,6 +24,7 @@ from .scrapers.exceptions import (
|
||||
def scrape_jobs(
|
||||
site_name: str | list[str] | Site | list[Site] | None = None,
|
||||
search_term: str | None = None,
|
||||
google_search_term: str | None = None,
|
||||
location: str | None = None,
|
||||
distance: int | None = 50,
|
||||
is_remote: bool = False,
|
||||
@@ -86,6 +87,7 @@ def scrape_jobs(
|
||||
site_type=get_site_type(),
|
||||
country=country_enum,
|
||||
search_term=search_term,
|
||||
google_search_term=google_search_term,
|
||||
location=location,
|
||||
distance=distance,
|
||||
is_remote=is_remote,
|
||||
@@ -216,8 +218,8 @@ def scrape_jobs(
|
||||
"title",
|
||||
"company",
|
||||
"location",
|
||||
"job_type",
|
||||
"date_posted",
|
||||
"job_type",
|
||||
"salary_source",
|
||||
"interval",
|
||||
"min_amount",
|
||||
@@ -248,6 +250,8 @@ def scrape_jobs(
|
||||
jobs_df = jobs_df[desired_order]
|
||||
|
||||
# Step 4: Sort the DataFrame as required
|
||||
return jobs_df.sort_values(by=["site", "date_posted"], ascending=[True, False])
|
||||
return jobs_df.sort_values(
|
||||
by=["site", "date_posted"], ascending=[True, False]
|
||||
).reset_index(drop=True)
|
||||
else:
|
||||
return pd.DataFrame()
|
||||
|
||||
@@ -28,6 +28,7 @@ class SalarySource(Enum):
|
||||
class ScraperInput(BaseModel):
|
||||
site_type: list[Site]
|
||||
search_term: str | None = None
|
||||
google_search_term: str | None = None
|
||||
|
||||
location: str | None = None
|
||||
country: Country | None = Country.USA
|
||||
|
||||
@@ -59,13 +59,14 @@ class GoogleJobsScraper(Scraper):
|
||||
self.session = create_session(
|
||||
proxies=self.proxies, ca_cert=self.ca_cert, is_tls=False, has_retry=True
|
||||
)
|
||||
forward_cursor = self._get_initial_cursor()
|
||||
forward_cursor, job_list = self._get_initial_cursor_and_jobs()
|
||||
if forward_cursor is None:
|
||||
logger.error("initial cursor not found")
|
||||
return JobResponse(jobs=[])
|
||||
logger.warning(
|
||||
"initial cursor not found, try changing your query or there was at most 10 results"
|
||||
)
|
||||
return JobResponse(jobs=job_list)
|
||||
|
||||
page = 1
|
||||
job_list: list[JobPost] = []
|
||||
|
||||
while (
|
||||
len(self.seen_urls) < scraper_input.results_wanted + scraper_input.offset
|
||||
@@ -74,7 +75,11 @@ class GoogleJobsScraper(Scraper):
|
||||
logger.info(
|
||||
f"search page: {page} / {math.ceil(scraper_input.results_wanted / self.jobs_per_page)}"
|
||||
)
|
||||
try:
|
||||
jobs, forward_cursor = self._get_jobs_next_page(forward_cursor)
|
||||
except Exception as e:
|
||||
logger.error(f"failed to get jobs on page: {page}, {e}")
|
||||
break
|
||||
if not jobs:
|
||||
logger.info(f"found no jobs on page: {page}")
|
||||
break
|
||||
@@ -87,8 +92,8 @@ class GoogleJobsScraper(Scraper):
|
||||
]
|
||||
)
|
||||
|
||||
def _get_initial_cursor(self):
|
||||
"""Gets initial cursor to paginate through job listings"""
|
||||
def _get_initial_cursor_and_jobs(self) -> Tuple[str, list[JobPost]]:
|
||||
"""Gets initial cursor and jobs to paginate through job listings"""
|
||||
query = f"{self.scraper_input.search_term} jobs"
|
||||
|
||||
def get_time_range(hours_old):
|
||||
@@ -121,13 +126,22 @@ class GoogleJobsScraper(Scraper):
|
||||
if self.scraper_input.is_remote:
|
||||
query += " remote"
|
||||
|
||||
if self.scraper_input.google_search_term:
|
||||
query = self.scraper_input.google_search_term
|
||||
|
||||
params = {"q": query, "udm": "8"}
|
||||
response = self.session.get(self.url, headers=headers_initial, params=params)
|
||||
|
||||
pattern_fc = r'<div jsname="Yust4d"[^>]+data-async-fc="([^"]+)"'
|
||||
match_fc = re.search(pattern_fc, response.text)
|
||||
data_async_fc = match_fc.group(1) if match_fc else None
|
||||
return data_async_fc
|
||||
jobs_raw = self._find_job_info_initial_page(response.text)
|
||||
jobs = []
|
||||
for job_raw in jobs_raw:
|
||||
job_post = self._parse_job(job_raw)
|
||||
if job_post:
|
||||
jobs.append(job_post)
|
||||
return data_async_fc, jobs
|
||||
|
||||
def _get_jobs_next_page(self, forward_cursor: str) -> Tuple[list[JobPost], str]:
|
||||
params = {"fc": [forward_cursor], "fcv": ["3"], "async": [async_param]}
|
||||
@@ -147,19 +161,22 @@ class GoogleJobsScraper(Scraper):
|
||||
match_fc = re.search(pattern_fc, job_data)
|
||||
data_async_fc = match_fc.group(1) if match_fc else None
|
||||
jobs_on_page = []
|
||||
|
||||
for array in parsed:
|
||||
|
||||
_, job_data = array
|
||||
if not job_data.startswith("[[["):
|
||||
continue
|
||||
job_d = json.loads(job_data)
|
||||
|
||||
job_info = self._find_job_info(job_d)
|
||||
job_post = self._parse_job(job_info)
|
||||
if job_post:
|
||||
jobs_on_page.append(job_post)
|
||||
return jobs_on_page, data_async_fc
|
||||
|
||||
def _parse_job(self, job_info: list):
|
||||
job_url = job_info[3][0][0] if job_info[3] and job_info[3][0] else None
|
||||
if job_url in self.seen_urls:
|
||||
continue
|
||||
return
|
||||
self.seen_urls.add(job_url)
|
||||
|
||||
title = job_info[0]
|
||||
@@ -185,16 +202,13 @@ class GoogleJobsScraper(Scraper):
|
||||
city=city, state=state, country=country[0] if country else None
|
||||
),
|
||||
job_url=job_url,
|
||||
job_url_direct=job_url,
|
||||
date_posted=date_posted,
|
||||
is_remote="remote" in description.lower()
|
||||
or "wfh" in description.lower(),
|
||||
is_remote="remote" in description.lower() or "wfh" in description.lower(),
|
||||
description=description,
|
||||
emails=extract_emails_from_text(description),
|
||||
job_type=extract_job_type(description),
|
||||
)
|
||||
jobs_on_page.append(job_post)
|
||||
return jobs_on_page, data_async_fc
|
||||
return job_post
|
||||
|
||||
@staticmethod
|
||||
def _find_job_info(jobs_data: list | dict) -> list | None:
|
||||
@@ -213,3 +227,24 @@ class GoogleJobsScraper(Scraper):
|
||||
if result:
|
||||
return result
|
||||
return None
|
||||
|
||||
@staticmethod
|
||||
def _find_job_info_initial_page(html_text: str):
|
||||
pattern = (
|
||||
f'520084652":('
|
||||
+ r"\[.*?\]\s*])\s*}\s*]\s*]\s*]\s*]\s*]"
|
||||
)
|
||||
results = []
|
||||
matches = re.finditer(pattern, html_text)
|
||||
|
||||
import json
|
||||
|
||||
for match in matches:
|
||||
try:
|
||||
parsed_data = json.loads(match.group(1))
|
||||
results.append(parsed_data)
|
||||
|
||||
except json.JSONDecodeError as e:
|
||||
logger.error(f"Failed to parse match: {str(e)}")
|
||||
results.append({"raw_match": match.group(0), "error": str(e)})
|
||||
return results
|
||||
|
||||
Reference in New Issue
Block a user