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...

31 Commits

Author SHA1 Message Date
Cullen Watson
02caf1b38d fix(zr): date posted (#98) 2024-02-03 07:20:53 -06:00
Cullen Watson
8e2ab277da fix(ziprecruiter): pagination (#97)
* fix(ziprecruiter): pagination

* chore: version
2024-02-02 20:48:28 -06:00
Cullen Watson
ce3bd84ee5 fix: indeed parse description bug (#96)
* fix(indeed): full descr

* chore: version
2024-02-02 18:21:55 -06:00
Cullen Watson
1ccf2290fe docs: readme 2024-02-02 17:59:24 -06:00
Cullen Watson
ec2eefc58a docs: readme 2024-02-02 17:58:15 -06:00
Cullen Watson
13c7694474 Easy apply (#95)
* enh(glassdoor): easy apply filter

* enh(ziprecruiter): easy apply

* enh(indeed): use mobile headers

* chore: version
2024-02-02 17:47:15 -06:00
Cullen Watson
bbe46fe3f4 enh(glassdoor): easy apply filter (#92) 2024-02-01 19:42:24 -06:00
Cullen Watson
b97c73ffd6 fix: clean description (#88) 2024-01-28 21:50:41 -06:00
Cullen Watson
5b3627b244 enh: full description param (#85) 2024-01-22 20:22:32 -06:00
Cullen Watson
2ec3b04777 fix(ziprecruiter): init cookies (#82) 2024-01-12 12:28:35 -06:00
Harish Vadaparty
89a5264391 add long scrape example (#81) 2024-01-12 12:24:00 -06:00
Cullen Watson
a7ad616567 fix: linkedin no results (#80) 2024-01-10 14:01:10 -06:00
cullenwatson
53bc33a43a chore: version 2024-01-09 19:33:56 -06:00
Cullen Watson
22870438c7 linkedin fix delays (#79) 2024-01-09 19:32:51 -06:00
Cullen Watson
aeb93b99f5 Update pyproject.toml 2024-01-03 12:04:50 -06:00
Cullen Watson
a5916edcdd fix(glassdoor): add retry adapter (#77) 2024-01-03 12:04:32 -06:00
Augusto Gunsch
33d442bf1e Add czech to Indeed (#72) 2023-12-02 02:42:54 -06:00
Zachary Hampton
6587e464fa Update README.md 2023-11-30 11:49:31 -07:00
Vincent Yan
eed7fca300 Get full indeed description (#70) 2023-11-27 15:00:36 -06:00
Faraz Khan
dfb8c18c51 include location with 3 parts (#69) 2023-11-10 16:59:42 -06:00
Faraz Khan
81f70ff8a5 added salary data for linkedin (#68) 2023-11-09 14:57:15 -06:00
Cullen Watson
cc9e7866b7 fix linkedin bug & add linkedin company url (#67) 2023-11-08 15:51:07 -06:00
Zachary Hampton
a2c8fe046e Update README.md 2023-11-06 22:13:19 -07:00
Cullen Watson
2b7fea40a5 [fix] glassdoor duplicates 2023-10-30 20:29:55 -05:00
Cullen Watson
d37f86e1b9 [fix] glassdoor location 2023-10-30 20:19:56 -05:00
Cullen Watson
0302ab14f5 glassdoor keywords 2023-10-30 20:07:31 -05:00
Cullen Watson
3f2b582445 add glassdoor (#66) 2023-10-30 19:57:36 -05:00
Cullen Watson
93223b6a38 bug fix 2023-10-30 13:57:23 -05:00
Cullen Watson
e3fc222eb5 readd proxy support for zip (#64) 2023-10-29 08:54:56 -05:00
Cullen
b303b3f841 chore: version 2023-10-28 16:58:32 -05:00
Cullen
1a0c75f323 chore: version 2023-10-28 16:54:04 -05:00
17 changed files with 843 additions and 422 deletions

View File

@@ -4,15 +4,12 @@
**Not technical?** Try out the web scraping tool on our site at [usejobspy.com](https://usejobspy.com).
*Looking to build a data-focused software product?* **[Book a call](https://calendly.com/bunsly/15min)** *to
*Looking to build a data-focused software product?* **[Book a call](https://bunsly.com/)** *to
work with us.*
\
Check out another project we wrote: ***[HomeHarvest](https://github.com/Bunsly/HomeHarvest)** a Python package
for real estate scraping*
## Features
- Scrapes job postings from **LinkedIn**, **Indeed** & **ZipRecruiter** simultaneously
- Scrapes job postings from **LinkedIn**, **Indeed**, **Glassdoor**, & **ZipRecruiter** simultaneously
- Aggregates the job postings in a Pandas DataFrame
- Proxy support (HTTP/S, SOCKS)
@@ -35,15 +32,15 @@ _Python version >= [3.10](https://www.python.org/downloads/release/python-3100/)
from jobspy import scrape_jobs
jobs = scrape_jobs(
site_name=["indeed", "linkedin", "zip_recruiter"],
site_name=["indeed", "linkedin", "zip_recruiter", "glassdoor"],
search_term="software engineer",
location="Dallas, TX",
results_wanted=10,
country_indeed='USA' # only needed for indeed
country_indeed='USA' # only needed for indeed / glassdoor
)
print(f"Found {len(jobs)} jobs")
print(jobs.head())
jobs.to_csv("jobs.csv", index=False) # / to_xlsx
jobs.to_csv("jobs.csv", index=False) # to_xlsx
```
### Output
@@ -62,7 +59,7 @@ zip_recruiter Software Developer TEKsystems Phoenix
```plaintext
Required
├── site_type (List[enum]): linkedin, zip_recruiter, indeed
├── site_type (List[enum]): linkedin, zip_recruiter, indeed, glassdoor
└── search_term (str)
Optional
├── location (int)
@@ -70,8 +67,9 @@ Optional
├── job_type (enum): fulltime, parttime, internship, contract
├── proxy (str): in format 'http://user:pass@host:port' or [https, socks]
├── is_remote (bool)
├── full_description (bool): fetches full description for Indeed / LinkedIn (much slower)
├── results_wanted (int): number of job results to retrieve for each site specified in 'site_type'
├── easy_apply (bool): filters for jobs that are hosted on LinkedIn
├── easy_apply (bool): filters for jobs that are hosted on the job board site
├── country_indeed (enum): filters the country on Indeed (see below for correct spelling)
├── offset (num): starts the search from an offset (e.g. 25 will start the search from the 25th result)
```
@@ -82,6 +80,7 @@ Optional
JobPost
├── title (str)
├── company (str)
├── company_url (str)
├── job_url (str)
├── location (object)
│ ├── country (str)
@@ -107,43 +106,46 @@ The following exceptions may be raised when using JobSpy:
* `LinkedInException`
* `IndeedException`
* `ZipRecruiterException`
* `GlassdoorException`
## Supported Countries for Job Searching
### **LinkedIn**
LinkedIn searches globally & uses only the `location` parameter.
LinkedIn searches globally & uses only the `location` parameter. You can only fetch 1000 jobs max from the LinkedIn endpoint we're using
### **ZipRecruiter**
ZipRecruiter searches for jobs in **US/Canada** & uses only the `location` parameter.
### **Indeed**
### **Indeed / Glassdoor**
Indeed supports most countries, but the `country_indeed` parameter is required. Additionally, use the `location`
Indeed & Glassdoor supports most countries, but the `country_indeed` parameter is required. Additionally, use the `location`
parameter to narrow down the location, e.g. city & state if necessary.
You can specify the following countries when searching on Indeed (use the exact name):
You can specify the following countries when searching on Indeed (use the exact name, * indicates support for Glassdoor):
| | | | |
|----------------------|--------------|------------|----------------|
| Argentina | Australia | Austria | Bahrain |
| Belgium | Brazil | Canada | Chile |
| Argentina | Australia* | Austria* | Bahrain |
| Belgium* | Brazil* | Canada* | Chile |
| China | Colombia | Costa Rica | Czech Republic |
| Denmark | Ecuador | Egypt | Finland |
| France | Germany | Greece | Hong Kong |
| Hungary | India | Indonesia | Ireland |
| Israel | Italy | Japan | Kuwait |
| Luxembourg | Malaysia | Mexico | Morocco |
| Netherlands | New Zealand | Nigeria | Norway |
| France* | Germany* | Greece | Hong Kong* |
| Hungary | India* | Indonesia | Ireland* |
| Israel | Italy* | Japan | Kuwait |
| Luxembourg | Malaysia | Mexico* | Morocco |
| Netherlands* | New Zealand* | Nigeria | Norway |
| Oman | Pakistan | Panama | Peru |
| Philippines | Poland | Portugal | Qatar |
| Romania | Saudi Arabia | Singapore | South Africa |
| South Korea | Spain | Sweden | Switzerland |
| Romania | Saudi Arabia | Singapore* | South Africa |
| South Korea | Spain* | Sweden | Switzerland* |
| Taiwan | Thailand | Turkey | Ukraine |
| United Arab Emirates | UK | USA | Uruguay |
| United Arab Emirates | UK* | USA* | Uruguay |
| Venezuela | Vietnam | | |
Glassdoor can only fetch 900 jobs from the endpoint we're using on a given search.
## Frequently Asked Questions
---
@@ -157,16 +159,11 @@ persist, [submit an issue](https://github.com/Bunsly/JobSpy/issues).
**Q: Received a response code 429?**
**A:** This indicates that you have been blocked by the job board site for sending too many requests. All of the job board sites are aggressive with blocking. We recommend:
- Waiting a few seconds between requests.
- Waiting some time between scrapes (site-dependent).
- Trying a VPN or proxy to change your IP address.
---
**Q: Experiencing a "Segmentation fault: 11" on macOS Catalina?**
**A:** This is due to `tls_client` dependency not supporting your architecture. Solutions and workarounds include:
- Upgrade to a newer version of MacOS
- Reach out to the maintainers of [tls_client](https://github.com/bogdanfinn/tls-client) for fixes

View File

@@ -2,12 +2,11 @@ from jobspy import scrape_jobs
import pandas as pd
jobs: pd.DataFrame = scrape_jobs(
site_name=["indeed", "linkedin", "zip_recruiter"],
site_name=["indeed", "linkedin", "zip_recruiter", "glassdoor"],
search_term="software engineer",
location="Dallas, TX",
results_wanted=50, # be wary the higher it is, the more likey you'll get blocked (rotating proxy should work tho)
results_wanted=25, # be wary the higher it is, the more likey you'll get blocked (rotating proxy can help tho)
country_indeed="USA",
offset=25 # start jobs from an offset (use if search failed and want to continue)
# proxy="http://jobspy:5a4vpWtj8EeJ2hoYzk@ca.smartproxy.com:20001",
)

View File

@@ -0,0 +1,77 @@
from jobspy import scrape_jobs
import pandas as pd
import os
import time
# creates csv a new filename if the jobs.csv already exists.
csv_filename = "jobs.csv"
counter = 1
while os.path.exists(csv_filename):
csv_filename = f"jobs_{counter}.csv"
counter += 1
# results wanted and offset
results_wanted = 1000
offset = 0
all_jobs = []
# max retries
max_retries = 3
# nuumber of results at each iteration
results_in_each_iteration = 30
while len(all_jobs) < results_wanted:
retry_count = 0
while retry_count < max_retries:
print("Doing from", offset, "to", offset + results_in_each_iteration, "jobs")
try:
jobs = scrape_jobs(
site_name=["indeed"],
search_term="software engineer",
# New York, NY
# Dallas, TX
# Los Angeles, CA
location="Los Angeles, CA",
results_wanted=min(results_in_each_iteration, results_wanted - len(all_jobs)),
country_indeed="USA",
offset=offset,
# proxy="http://jobspy:5a4vpWtj8EeJ2hoYzk@ca.smartproxy.com:20001",
)
# Add the scraped jobs to the list
all_jobs.extend(jobs.to_dict('records'))
# Increment the offset for the next page of results
offset += results_in_each_iteration
# Add a delay to avoid rate limiting (you can adjust the delay time as needed)
print(f"Scraped {len(all_jobs)} jobs")
print("Sleeping secs", 100 * (retry_count + 1))
time.sleep(100 * (retry_count + 1)) # Sleep for 2 seconds between requests
break # Break out of the retry loop if successful
except Exception as e:
print(f"Error: {e}")
retry_count += 1
print("Sleeping secs before retry", 100 * (retry_count + 1))
time.sleep(100 * (retry_count + 1))
if retry_count >= max_retries:
print("Max retries reached. Exiting.")
break
# DataFrame from the collected job data
jobs_df = pd.DataFrame(all_jobs)
# Formatting
pd.set_option("display.max_columns", None)
pd.set_option("display.max_rows", None)
pd.set_option("display.width", None)
pd.set_option("display.max_colwidth", 50)
print(jobs_df)
jobs_df.to_csv(csv_filename, index=False)
print(f"Outputted to {csv_filename}")

18
poetry.lock generated
View File

@@ -1053,16 +1053,6 @@ files = [
{file = "MarkupSafe-2.1.3-cp311-cp311-musllinux_1_1_x86_64.whl", hash = "sha256:5bbe06f8eeafd38e5d0a4894ffec89378b6c6a625ff57e3028921f8ff59318ac"},
{file = "MarkupSafe-2.1.3-cp311-cp311-win32.whl", hash = "sha256:dd15ff04ffd7e05ffcb7fe79f1b98041b8ea30ae9234aed2a9168b5797c3effb"},
{file = "MarkupSafe-2.1.3-cp311-cp311-win_amd64.whl", hash = "sha256:134da1eca9ec0ae528110ccc9e48041e0828d79f24121a1a146161103c76e686"},
{file = "MarkupSafe-2.1.3-cp312-cp312-macosx_10_9_universal2.whl", hash = "sha256:f698de3fd0c4e6972b92290a45bd9b1536bffe8c6759c62471efaa8acb4c37bc"},
{file = "MarkupSafe-2.1.3-cp312-cp312-macosx_10_9_x86_64.whl", hash = "sha256:aa57bd9cf8ae831a362185ee444e15a93ecb2e344c8e52e4d721ea3ab6ef1823"},
{file = "MarkupSafe-2.1.3-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:ffcc3f7c66b5f5b7931a5aa68fc9cecc51e685ef90282f4a82f0f5e9b704ad11"},
{file = "MarkupSafe-2.1.3-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:47d4f1c5f80fc62fdd7777d0d40a2e9dda0a05883ab11374334f6c4de38adffd"},
{file = "MarkupSafe-2.1.3-cp312-cp312-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:1f67c7038d560d92149c060157d623c542173016c4babc0c1913cca0564b9939"},
{file = "MarkupSafe-2.1.3-cp312-cp312-musllinux_1_1_aarch64.whl", hash = "sha256:9aad3c1755095ce347e26488214ef77e0485a3c34a50c5a5e2471dff60b9dd9c"},
{file = "MarkupSafe-2.1.3-cp312-cp312-musllinux_1_1_i686.whl", hash = "sha256:14ff806850827afd6b07a5f32bd917fb7f45b046ba40c57abdb636674a8b559c"},
{file = "MarkupSafe-2.1.3-cp312-cp312-musllinux_1_1_x86_64.whl", hash = "sha256:8f9293864fe09b8149f0cc42ce56e3f0e54de883a9de90cd427f191c346eb2e1"},
{file = "MarkupSafe-2.1.3-cp312-cp312-win32.whl", hash = "sha256:715d3562f79d540f251b99ebd6d8baa547118974341db04f5ad06d5ea3eb8007"},
{file = "MarkupSafe-2.1.3-cp312-cp312-win_amd64.whl", hash = "sha256:1b8dd8c3fd14349433c79fa8abeb573a55fc0fdd769133baac1f5e07abf54aeb"},
{file = "MarkupSafe-2.1.3-cp37-cp37m-macosx_10_9_x86_64.whl", hash = "sha256:8e254ae696c88d98da6555f5ace2279cf7cd5b3f52be2b5cf97feafe883b58d2"},
{file = "MarkupSafe-2.1.3-cp37-cp37m-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:cb0932dc158471523c9637e807d9bfb93e06a95cbf010f1a38b98623b929ef2b"},
{file = "MarkupSafe-2.1.3-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:9402b03f1a1b4dc4c19845e5c749e3ab82d5078d16a2a4c2cd2df62d57bb0707"},
@@ -2270,13 +2260,13 @@ test = ["flake8", "isort", "pytest"]
[[package]]
name = "tls-client"
version = "0.2.1"
version = "1.0"
description = "Advanced Python HTTP Client."
optional = false
python-versions = "*"
files = [
{file = "tls_client-0.2.1-py3-none-any.whl", hash = "sha256:124a710952b979d5e20b4e2b7879b7958d6e48a259d0f5b83101055eb173f0bd"},
{file = "tls_client-0.2.1.tar.gz", hash = "sha256:473fb4c671d9d4ca6b818548ab6e955640dd589767bfce520830c5618c2f2e2b"},
{file = "tls_client-1.0-py3-none-any.whl", hash = "sha256:f1183f5e18cb31914bd62d11b350a33ea0293ea80fb91d69a3072821dece3e66"},
{file = "tls_client-1.0.tar.gz", hash = "sha256:7f6de48ad4a0ef69b72682c76ce604155971e07b4bfb2148a36276194ae3e7a0"},
]
[[package]]
@@ -2445,4 +2435,4 @@ files = [
[metadata]
lock-version = "2.0"
python-versions = "^3.10"
content-hash = "f966f3979873eec2c3b13460067f5aa414c69aa8ab5cd3239c1cfa564fcb5deb"
content-hash = "404a77d78066cbb2ef71015562baf44aa11d12aac29a191c1ccc7758bfda598a"

View File

@@ -1,7 +1,7 @@
[tool.poetry]
name = "python-jobspy"
version = "1.1.17"
description = "Job scraper for LinkedIn, Indeed & ZipRecruiter"
version = "1.1.41"
description = "Job scraper for LinkedIn, Indeed, Glassdoor & ZipRecruiter"
authors = ["Zachary Hampton <zachary@bunsly.com>", "Cullen Watson <cullen@bunsly.com>"]
homepage = "https://github.com/Bunsly/JobSpy"
readme = "README.md"
@@ -13,7 +13,7 @@ packages = [
[tool.poetry.dependencies]
python = "^3.10"
requests = "^2.31.0"
tls-client = "^0.2.1"
tls-client = "*"
beautifulsoup4 = "^4.12.2"
pandas = "^2.1.0"
NUMPY = "1.24.2"

View File

@@ -6,18 +6,21 @@ from typing import Tuple, Optional
from .jobs import JobType, Location
from .scrapers.indeed import IndeedScraper
from .scrapers.ziprecruiter import ZipRecruiterScraper
from .scrapers.glassdoor import GlassdoorScraper
from .scrapers.linkedin import LinkedInScraper
from .scrapers import ScraperInput, Site, JobResponse, Country
from .scrapers.exceptions import (
LinkedInException,
IndeedException,
ZipRecruiterException,
GlassdoorException,
)
SCRAPER_MAPPING = {
Site.LINKEDIN: LinkedInScraper,
Site.INDEED: IndeedScraper,
Site.ZIP_RECRUITER: ZipRecruiterScraper,
Site.GLASSDOOR: GlassdoorScraper,
}
@@ -37,6 +40,7 @@ def scrape_jobs(
country_indeed: str = "usa",
hyperlinks: bool = False,
proxy: Optional[str] = None,
full_description: Optional[bool] = False,
offset: Optional[int] = 0,
) -> pd.DataFrame:
"""
@@ -71,6 +75,7 @@ def scrape_jobs(
is_remote=is_remote,
job_type=job_type,
easy_apply=easy_apply,
full_description=full_description,
results_wanted=results_wanted,
offset=offset,
)
@@ -90,6 +95,8 @@ def scrape_jobs(
raise IndeedException(str(e))
if site == Site.ZIP_RECRUITER:
raise ZipRecruiterException(str(e))
if site == Site.GLASSDOOR:
raise GlassdoorException(str(e))
else:
raise e
return site.value, scraped_data
@@ -127,7 +134,10 @@ def scrape_jobs(
job_data["emails"] = (
", ".join(job_data["emails"]) if job_data["emails"] else None
)
job_data["location"] = Location(**job_data["location"]).display_location()
if job_data["location"]:
job_data["location"] = Location(
**job_data["location"]
).display_location()
compensation_obj = job_data.get("compensation")
if compensation_obj and isinstance(compensation_obj, dict):
@@ -155,6 +165,7 @@ def scrape_jobs(
"site",
"title",
"company",
"company_url",
"location",
"job_type",
"date_posted",

View File

@@ -1,8 +1,7 @@
from typing import Union, Optional
from typing import Optional
from datetime import date
from enum import Enum
from pydantic import BaseModel, validator
from pydantic import BaseModel
class JobType(Enum):
@@ -56,40 +55,46 @@ class JobType(Enum):
class Country(Enum):
ARGENTINA = ("argentina", "ar")
AUSTRALIA = ("australia", "au")
AUSTRIA = ("austria", "at")
"""
Gets the subdomain for Indeed and Glassdoor.
The second item in the tuple is the subdomain for Indeed
The third item in the tuple is the subdomain (and tld if there's a ':' separator) for Glassdoor
"""
ARGENTINA = ("argentina", "ar", "com.ar")
AUSTRALIA = ("australia", "au", "com.au")
AUSTRIA = ("austria", "at", "at")
BAHRAIN = ("bahrain", "bh")
BELGIUM = ("belgium", "be")
BRAZIL = ("brazil", "br")
CANADA = ("canada", "ca")
BELGIUM = ("belgium", "be", "fr:be")
BRAZIL = ("brazil", "br", "com.br")
CANADA = ("canada", "ca", "ca")
CHILE = ("chile", "cl")
CHINA = ("china", "cn")
COLOMBIA = ("colombia", "co")
COSTARICA = ("costa rica", "cr")
CZECHREPUBLIC = ("czech republic", "cz")
CZECHREPUBLIC = ("czech republic,czechia", "cz")
DENMARK = ("denmark", "dk")
ECUADOR = ("ecuador", "ec")
EGYPT = ("egypt", "eg")
FINLAND = ("finland", "fi")
FRANCE = ("france", "fr")
GERMANY = ("germany", "de")
FRANCE = ("france", "fr", "fr")
GERMANY = ("germany", "de", "de")
GREECE = ("greece", "gr")
HONGKONG = ("hong kong", "hk")
HONGKONG = ("hong kong", "hk", "com.hk")
HUNGARY = ("hungary", "hu")
INDIA = ("india", "in")
INDIA = ("india", "in", "co.in")
INDONESIA = ("indonesia", "id")
IRELAND = ("ireland", "ie")
IRELAND = ("ireland", "ie", "ie")
ISRAEL = ("israel", "il")
ITALY = ("italy", "it")
ITALY = ("italy", "it", "it")
JAPAN = ("japan", "jp")
KUWAIT = ("kuwait", "kw")
LUXEMBOURG = ("luxembourg", "lu")
MALAYSIA = ("malaysia", "malaysia")
MEXICO = ("mexico", "mx")
MEXICO = ("mexico", "mx", "com.mx")
MOROCCO = ("morocco", "ma")
NETHERLANDS = ("netherlands", "nl")
NEWZEALAND = ("new zealand", "nz")
NETHERLANDS = ("netherlands", "nl", "nl")
NEWZEALAND = ("new zealand", "nz", "co.nz")
NIGERIA = ("nigeria", "ng")
NORWAY = ("norway", "no")
OMAN = ("oman", "om")
@@ -102,19 +107,19 @@ class Country(Enum):
QATAR = ("qatar", "qa")
ROMANIA = ("romania", "ro")
SAUDIARABIA = ("saudi arabia", "sa")
SINGAPORE = ("singapore", "sg")
SINGAPORE = ("singapore", "sg", "sg")
SOUTHAFRICA = ("south africa", "za")
SOUTHKOREA = ("south korea", "kr")
SPAIN = ("spain", "es")
SPAIN = ("spain", "es", "es")
SWEDEN = ("sweden", "se")
SWITZERLAND = ("switzerland", "ch")
SWITZERLAND = ("switzerland", "ch", "de:ch")
TAIWAN = ("taiwan", "tw")
THAILAND = ("thailand", "th")
TURKEY = ("turkey", "tr")
UKRAINE = ("ukraine", "ua")
UNITEDARABEMIRATES = ("united arab emirates", "ae")
UK = ("uk", "uk")
USA = ("usa", "www")
UK = ("uk,united kingdom", "uk", "co.uk")
USA = ("usa,us,united states", "www", "com")
URUGUAY = ("uruguay", "uy")
VENEZUELA = ("venezuela", "ve")
VIETNAM = ("vietnam", "vn")
@@ -122,34 +127,43 @@ class Country(Enum):
# internal for ziprecruiter
US_CANADA = ("usa/ca", "www")
# internal for linkeind
# internal for linkedin
WORLDWIDE = ("worldwide", "www")
def __new__(cls, country, domain):
obj = object.__new__(cls)
obj._value_ = country
obj.domain = domain
return obj
@property
def indeed_domain_value(self):
return self.value[1]
@property
def domain_value(self):
return self.domain
def glassdoor_domain_value(self):
if len(self.value) == 3:
subdomain, _, domain = self.value[2].partition(":")
if subdomain and domain:
return f"{subdomain}.glassdoor.{domain}"
else:
return f"www.glassdoor.{self.value[2]}"
else:
raise Exception(f"Glassdoor is not available for {self.name}")
def get_url(self):
return f"https://{self.glassdoor_domain_value}/"
@classmethod
def from_string(cls, country_str: str):
"""Convert a string to the corresponding Country enum."""
country_str = country_str.strip().lower()
for country in cls:
if country.value == country_str:
country_names = country.value[0].split(',')
if country_str in country_names:
return country
valid_countries = [country.value for country in cls]
raise ValueError(
f"Invalid country string: '{country_str}'. Valid countries (only include this param for Indeed) are: {', '.join(valid_countries)}"
f"Invalid country string: '{country_str}'. Valid countries are: {', '.join([country[0] for country in valid_countries])}"
)
class Location(BaseModel):
country: Country = None
country: Country | None = None
city: Optional[str] = None
state: Optional[str] = None
@@ -160,10 +174,13 @@ class Location(BaseModel):
if self.state:
location_parts.append(self.state)
if self.country and self.country not in (Country.US_CANADA, Country.WORLDWIDE):
if self.country.value in ("usa", "uk"):
location_parts.append(self.country.value.upper())
country_name = self.country.value[0]
if "," in country_name:
country_name = country_name.split(",")[0]
if country_name in ("usa", "uk"):
location_parts.append(country_name.upper())
else:
location_parts.append(self.country.value.title())
location_parts.append(country_name.title())
return ", ".join(location_parts)
@@ -174,6 +191,10 @@ class CompensationInterval(Enum):
DAILY = "daily"
HOURLY = "hourly"
@classmethod
def get_interval(cls, pay_period):
return cls[pay_period].value if pay_period in cls.__members__ else None
class Compensation(BaseModel):
interval: Optional[CompensationInterval] = None
@@ -189,6 +210,8 @@ class JobPost(BaseModel):
location: Optional[Location]
description: str | None = None
company_url: str | None = None
job_type: list[JobType] | None = None
compensation: Compensation | None = None
date_posted: date | None = None

View File

@@ -6,6 +6,7 @@ class Site(Enum):
LINKEDIN = "linkedin"
INDEED = "indeed"
ZIP_RECRUITER = "zip_recruiter"
GLASSDOOR = "glassdoor"
class ScraperInput(BaseModel):
@@ -18,6 +19,7 @@ class ScraperInput(BaseModel):
is_remote: bool = False
job_type: Optional[JobType] = None
easy_apply: bool = None # linkedin
full_description: bool = False
offset: int = 0
results_wanted: int = 15

View File

@@ -19,3 +19,8 @@ class IndeedException(Exception):
class ZipRecruiterException(Exception):
def __init__(self, message=None):
super().__init__(message or "An error occurred with ZipRecruiter")
class GlassdoorException(Exception):
def __init__(self, message=None):
super().__init__(message or "An error occurred with Glassdoor")

View File

@@ -0,0 +1,335 @@
"""
jobspy.scrapers.glassdoor
~~~~~~~~~~~~~~~~~~~
This module contains routines to scrape Glassdoor.
"""
import json
import requests
from bs4 import BeautifulSoup
from typing import Optional
from datetime import datetime, timedelta
from concurrent.futures import ThreadPoolExecutor, as_completed
from ..utils import count_urgent_words, extract_emails_from_text
from .. import Scraper, ScraperInput, Site
from ..exceptions import GlassdoorException
from ..utils import create_session, modify_and_get_description
from ...jobs import (
JobPost,
Compensation,
CompensationInterval,
Location,
JobResponse,
JobType,
)
class GlassdoorScraper(Scraper):
def __init__(self, proxy: Optional[str] = None):
"""
Initializes GlassdoorScraper with the Glassdoor job search url
"""
site = Site(Site.GLASSDOOR)
super().__init__(site, proxy=proxy)
self.url = None
self.country = None
self.jobs_per_page = 30
self.seen_urls = set()
def fetch_jobs_page(
self,
scraper_input: ScraperInput,
location_id: int,
location_type: str,
page_num: int,
cursor: str | None,
) -> (list[JobPost], str | None):
"""
Scrapes a page of Glassdoor for jobs with scraper_input criteria
"""
try:
payload = self.add_payload(
scraper_input, location_id, location_type, page_num, cursor
)
session = create_session(self.proxy, is_tls=False, has_retry=True)
response = session.post(
f"{self.url}/graph", headers=self.headers(), timeout=10, data=payload
)
if response.status_code != 200:
raise GlassdoorException(
f"bad response status code: {response.status_code}"
)
res_json = response.json()[0]
if "errors" in res_json:
raise ValueError("Error encountered in API response")
except Exception as e:
raise GlassdoorException(str(e))
jobs_data = res_json["data"]["jobListings"]["jobListings"]
jobs = []
with ThreadPoolExecutor(max_workers=self.jobs_per_page) as executor:
future_to_job_data = {executor.submit(self.process_job, job): job for job in jobs_data}
for future in as_completed(future_to_job_data):
job_data = future_to_job_data[future]
try:
job_post = future.result()
if job_post:
jobs.append(job_post)
except Exception as exc:
raise GlassdoorException(f'Glassdoor generated an exception: {exc}')
return jobs, self.get_cursor_for_page(
res_json["data"]["jobListings"]["paginationCursors"], page_num + 1
)
def process_job(self, job_data):
"""Processes a single job and fetches its description."""
job_id = job_data["jobview"]["job"]["listingId"]
job_url = f'{self.url}job-listing/j?jl={job_id}'
if job_url in self.seen_urls:
return None
self.seen_urls.add(job_url)
job = job_data["jobview"]
title = job["job"]["jobTitleText"]
company_name = job["header"]["employerNameFromSearch"]
company_id = job_data['jobview']['header']['employer']['id']
location_name = job["header"].get("locationName", "")
location_type = job["header"].get("locationType", "")
age_in_days = job["header"].get("ageInDays")
is_remote, location = False, None
date_posted = (datetime.now() - timedelta(days=age_in_days)).date() if age_in_days else None
if location_type == "S":
is_remote = True
else:
location = self.parse_location(location_name)
compensation = self.parse_compensation(job["header"])
try:
description = self.fetch_job_description(job_id)
except Exception as e :
description = None
job_post = JobPost(
title=title,
company_url=f"{self.url}Overview/W-EI_IE{company_id}.htm" if company_id else None,
company_name=company_name,
date_posted=date_posted,
job_url=job_url,
location=location,
compensation=compensation,
is_remote=is_remote,
description=description,
emails=extract_emails_from_text(description) if description else None,
num_urgent_words=count_urgent_words(description) if description else None,
)
return job_post
def scrape(self, scraper_input: ScraperInput) -> JobResponse:
"""
Scrapes Glassdoor for jobs with scraper_input criteria.
:param scraper_input: Information about job search criteria.
:return: JobResponse containing a list of jobs.
"""
scraper_input.results_wanted = min(900, scraper_input.results_wanted)
self.country = scraper_input.country
self.url = self.country.get_url()
location_id, location_type = self.get_location(
scraper_input.location, scraper_input.is_remote
)
all_jobs: list[JobPost] = []
cursor = None
max_pages = 30
try:
for page in range(
1 + (scraper_input.offset // self.jobs_per_page),
min(
(scraper_input.results_wanted // self.jobs_per_page) + 2,
max_pages + 1,
),
):
try:
jobs, cursor = self.fetch_jobs_page(
scraper_input, location_id, location_type, page, cursor
)
all_jobs.extend(jobs)
if len(all_jobs) >= scraper_input.results_wanted:
all_jobs = all_jobs[: scraper_input.results_wanted]
break
except Exception as e:
raise GlassdoorException(str(e))
except Exception as e:
raise GlassdoorException(str(e))
return JobResponse(jobs=all_jobs)
def fetch_job_description(self, job_id):
"""Fetches the job description for a single job ID."""
url = f"{self.url}/graph"
body = [
{
"operationName": "JobDetailQuery",
"variables": {
"jl": job_id,
"queryString": "q",
"pageTypeEnum": "SERP"
},
"query": """
query JobDetailQuery($jl: Long!, $queryString: String, $pageTypeEnum: PageTypeEnum) {
jobview: jobView(
listingId: $jl
contextHolder: {queryString: $queryString, pageTypeEnum: $pageTypeEnum}
) {
job {
description
__typename
}
__typename
}
}
"""
}
]
response = requests.post(url, json=body, headers=GlassdoorScraper.headers())
if response.status_code != 200:
return None
data = response.json()[0]
desc = data['data']['jobview']['job']['description']
soup = BeautifulSoup(desc, 'html.parser')
return modify_and_get_description(soup)
@staticmethod
def parse_compensation(data: dict) -> Optional[Compensation]:
pay_period = data.get("payPeriod")
adjusted_pay = data.get("payPeriodAdjustedPay")
currency = data.get("payCurrency", "USD")
if not pay_period or not adjusted_pay:
return None
interval = None
if pay_period == "ANNUAL":
interval = CompensationInterval.YEARLY
elif pay_period:
interval = CompensationInterval.get_interval(pay_period)
min_amount = int(adjusted_pay.get("p10") // 1)
max_amount = int(adjusted_pay.get("p90") // 1)
return Compensation(
interval=interval,
min_amount=min_amount,
max_amount=max_amount,
currency=currency,
)
def get_location(self, location: str, is_remote: bool) -> (int, str):
if not location or is_remote:
return "11047", "STATE" # remote options
url = f"{self.url}/findPopularLocationAjax.htm?maxLocationsToReturn=10&term={location}"
session = create_session(self.proxy, has_retry=True)
response = session.get(url)
if response.status_code != 200:
raise GlassdoorException(
f"bad response status code: {response.status_code}"
)
items = response.json()
if not items:
raise ValueError(f"Location '{location}' not found on Glassdoor")
location_type = items[0]["locationType"]
if location_type == "C":
location_type = "CITY"
elif location_type == "S":
location_type = "STATE"
elif location_type == 'N':
location_type = "COUNTRY"
return int(items[0]["locationId"]), location_type
@staticmethod
def add_payload(
scraper_input,
location_id: int,
location_type: str,
page_num: int,
cursor: str | None = None,
) -> str:
payload = {
"operationName": "JobSearchResultsQuery",
"variables": {
"excludeJobListingIds": [],
"filterParams": [{"filterKey": "applicationType", "values": "1"}] if scraper_input.easy_apply else [],
"keyword": scraper_input.search_term,
"numJobsToShow": 30,
"locationType": location_type,
"locationId": int(location_id),
"parameterUrlInput": f"IL.0,12_I{location_type}{location_id}",
"pageNumber": page_num,
"pageCursor": cursor,
},
"query": "query JobSearchResultsQuery($excludeJobListingIds: [Long!], $keyword: String, $locationId: Int, $locationType: LocationTypeEnum, $numJobsToShow: Int!, $pageCursor: String, $pageNumber: Int, $filterParams: [FilterParams], $originalPageUrl: String, $seoFriendlyUrlInput: String, $parameterUrlInput: String, $seoUrl: Boolean) {\n jobListings(\n contextHolder: {searchParams: {excludeJobListingIds: $excludeJobListingIds, keyword: $keyword, locationId: $locationId, locationType: $locationType, numPerPage: $numJobsToShow, pageCursor: $pageCursor, pageNumber: $pageNumber, filterParams: $filterParams, originalPageUrl: $originalPageUrl, seoFriendlyUrlInput: $seoFriendlyUrlInput, parameterUrlInput: $parameterUrlInput, seoUrl: $seoUrl, searchType: SR}}\n ) {\n companyFilterOptions {\n id\n shortName\n __typename\n }\n filterOptions\n indeedCtk\n jobListings {\n ...JobView\n __typename\n }\n jobListingSeoLinks {\n linkItems {\n position\n url\n __typename\n }\n __typename\n }\n jobSearchTrackingKey\n jobsPageSeoData {\n pageMetaDescription\n pageTitle\n __typename\n }\n paginationCursors {\n cursor\n pageNumber\n __typename\n }\n indexablePageForSeo\n searchResultsMetadata {\n searchCriteria {\n implicitLocation {\n id\n localizedDisplayName\n type\n __typename\n }\n keyword\n location {\n id\n shortName\n localizedShortName\n localizedDisplayName\n type\n __typename\n }\n __typename\n }\n footerVO {\n countryMenu {\n childNavigationLinks {\n id\n link\n textKey\n __typename\n }\n __typename\n }\n __typename\n }\n helpCenterDomain\n helpCenterLocale\n jobAlert {\n jobAlertExists\n __typename\n }\n jobSerpFaq {\n questions {\n answer\n question\n __typename\n }\n __typename\n }\n jobSerpJobOutlook {\n occupation\n paragraph\n __typename\n }\n showMachineReadableJobs\n __typename\n }\n serpSeoLinksVO {\n relatedJobTitlesResults\n searchedJobTitle\n searchedKeyword\n searchedLocationIdAsString\n searchedLocationSeoName\n searchedLocationType\n topCityIdsToNameResults {\n key\n value\n __typename\n }\n topEmployerIdsToNameResults {\n key\n value\n __typename\n }\n topEmployerNameResults\n topOccupationResults\n __typename\n }\n totalJobsCount\n __typename\n }\n}\n\nfragment JobView on JobListingSearchResult {\n jobview {\n header {\n adOrderId\n advertiserType\n adOrderSponsorshipLevel\n ageInDays\n divisionEmployerName\n easyApply\n employer {\n id\n name\n shortName\n __typename\n }\n employerNameFromSearch\n goc\n gocConfidence\n gocId\n jobCountryId\n jobLink\n jobResultTrackingKey\n jobTitleText\n locationName\n locationType\n locId\n needsCommission\n payCurrency\n payPeriod\n payPeriodAdjustedPay {\n p10\n p50\n p90\n __typename\n }\n rating\n salarySource\n savedJobId\n sponsored\n __typename\n }\n job {\n descriptionFragments\n importConfigId\n jobTitleId\n jobTitleText\n listingId\n __typename\n }\n jobListingAdminDetails {\n cpcVal\n importConfigId\n jobListingId\n jobSourceId\n userEligibleForAdminJobDetails\n __typename\n }\n overview {\n shortName\n squareLogoUrl\n __typename\n }\n __typename\n }\n __typename\n}\n",
}
job_type_filters = {
JobType.FULL_TIME: "fulltime",
JobType.PART_TIME: "parttime",
JobType.CONTRACT: "contract",
JobType.INTERNSHIP: "internship",
JobType.TEMPORARY: "temporary",
}
if scraper_input.job_type in job_type_filters:
filter_value = job_type_filters[scraper_input.job_type]
payload["variables"]["filterParams"].append(
{"filterKey": "jobType", "values": filter_value}
)
return json.dumps([payload])
@staticmethod
def get_job_type_enum(job_type_str: str) -> list[JobType] | None:
for job_type in JobType:
if job_type_str in job_type.value:
return [job_type]
@staticmethod
def parse_location(location_name: str) -> Location | None:
if not location_name or location_name == "Remote":
return
city, _, state = location_name.partition(", ")
return Location(city=city, state=state)
@staticmethod
def get_cursor_for_page(pagination_cursors, page_num):
for cursor_data in pagination_cursors:
if cursor_data["pageNumber"] == page_num:
return cursor_data["cursor"]
@staticmethod
def headers() -> dict:
"""
Returns headers needed for requests
:return: dict - Dictionary containing headers
"""
return {
"authority": "www.glassdoor.com",
"accept": "*/*",
"accept-language": "en-US,en;q=0.9",
"apollographql-client-name": "job-search-next",
"apollographql-client-version": "4.65.5",
"content-type": "application/json",
"cookie": 'gdId=91e2dfc4-c8b5-4fa7-83d0-11512b80262c; G_ENABLED_IDPS=google; trs=https%3A%2F%2Fwww.redhat.com%2F:referral:referral:2023-07-05+09%3A50%3A14.862:undefined:undefined; g_state={"i_p":1688587331651,"i_l":1}; _cfuvid=.7llazxhYFZWi6EISSPdVjtqF0NMVwzxr_E.cB1jgLs-1697828392979-0-604800000; GSESSIONID=undefined; JSESSIONID=F03DD1B5EE02DB6D842FE42B142F88F3; cass=1; jobsClicked=true; indeedCtk=1hd77b301k79i801; asst=1697829114.2; G_AUTHUSER_H=0; uc=8013A8318C98C517FE6DD0024636DFDEF978FC33266D93A2FAFEF364EACA608949D8B8FA2DC243D62DE271D733EB189D809ABE5B08D7B1AE865D217BD4EEBB97C282F5DA5FEFE79C937E3F6110B2A3A0ADBBA3B4B6DF5A996FEE00516100A65FCB11DA26817BE8D1C1BF6CFE36B5B68A3FDC2CFEC83AB797F7841FBB157C202332FC7E077B56BD39B167BDF3D9866E3B; AWSALB=zxc/Yk1nbWXXT6HjNyn3H4h4950ckVsFV/zOrq5LSoChYLE1qV+hDI8Axi3fUa9rlskndcO0M+Fw+ZnJ+AQ2afBFpyOd1acouLMYgkbEpqpQaWhY6/Gv4QH1zBcJ; AWSALBCORS=zxc/Yk1nbWXXT6HjNyn3H4h4950ckVsFV/zOrq5LSoChYLE1qV+hDI8Axi3fUa9rlskndcO0M+Fw+ZnJ+AQ2afBFpyOd1acouLMYgkbEpqpQaWhY6/Gv4QH1zBcJ; gdsid=1697828393025:1697830776351:668396EDB9E6A832022D34414128093D; at=HkH8Hnqi9uaMC7eu0okqyIwqp07ht9hBvE1_St7E_hRqPvkO9pUeJ1Jcpds4F3g6LL5ADaCNlxrPn0o6DumGMfog8qI1-zxaV_jpiFs3pugntw6WpVyYWdfioIZ1IDKupyteeLQEM1AO4zhGjY_rPZynpsiZBPO_B1au94sKv64rv23yvP56OiWKKfI-8_9hhLACEwWvM-Az7X-4aE2QdFt93VJbXbbGVf07bdDZfimsIkTtgJCLSRhU1V0kEM1Efyu66vo3m77gFFaMW7lxyYnb36I5PdDtEXBm3aL-zR7-qa5ywd94ISEivgqQOA4FPItNhqIlX4XrfD1lxVz6rfPaoTIDi4DI6UMCUjwyPsuv8mn0rYqDfRnmJpZ97fJ5AnhrknAd_6ZWN5v1OrxJczHzcXd8LO820QPoqxzzG13bmSTXLwGSxMUCtSrVsq05hicimQ3jpRt0c1dA4OkTNqF7_770B9JfcHcM8cr8-C4IL56dnOjr9KBGfN1Q2IvZM2cOBRbV7okiNOzKVZ3qJ24AE34WA2F3U6Whiu6H8nIuGG5hSNkVygY6CtglNZfFF9p8pJAZm79PngrrBv-CXFBZmhYLFo46lmFetDkiJ6mirtez4tKpzTIYjIp4_JAkiZFwbLJ2QGH4mK8kyyW0lZiX1DTuQec50N_5wvRo0Gt7nlKxzLsApMnaNhuQeH5ygh_pa381ORo9mQGi0EYF9zk00pa2--z4PtjfQ8KFq36GgpxKy5-o4qgqygZj8F01L8r-FiX2G4C7PREMIpAyHX2A4-_JxA1IS2j12EyqKTLqE9VcP06qm2Z-YuIW3ctmpMxy5G9_KiEiGv17weizhSFnl6SbpAEY-2VSmQ5V6jm3hoMp2jemkuGCRkZeFstLDEPxlzFN7WM; __cf_bm=zGaVjIJw4irf40_7UVw54B6Ohm271RUX4Tc8KVScrbs-1697830777-0-AYv2GnKTnnCU+cY9xHbJunO0DwlLDO6SIBnC/s/qldpKsGK0rRAjD6y8lbyATT/KlS7g29OZaN4fbd0lrJg0KmWbIybZIzfWVLHSYePVuOhu; asst=1697829114.2; at=dFhXf64wsf2TlnWy41xLs7skJkuxgKToEGcjGtDfUvW4oEAJ4tTIR5dKQ8wbwT75aIaGgdCfvcb-da7vwrCGWscCncmfLFQpJ9l-LLwoRfk-pMsxHhd77wvf-W7I0HSm7-Q5lQJqI9WyNGRxOa-RpzBTf4L8_Et4-3FzjPaAoYY5pY1FhuwXbN5asGOAMW-p8cjpbfn3PumlIYuckguWnjrcY2F31YJ_1noeoHM9tCGpymANbqGXRkG6aXY7yCfVXtdgZU1K5SMeaSPZIuF_iLUxjc_corzpNiH6qq7BIAmh-e5Aa-g7cwpZcln1fmwTVw4uTMZf1eLIMTa9WzgqZNkvG-sGaq_XxKA_Wai6xTTkOHfRgm4632Ba2963wdJvkGmUUa3tb_L4_wTgk3eFnHp5JhghLfT2Pe3KidP-yX__vx8JOsqe3fndCkKXgVz7xQKe1Dur-sMNlGwi4LXfguTT2YUI8C5Miq3pj2IHc7dC97eyyAiAM4HvyGWfaXWZcei6oIGrOwMvYgy0AcwFry6SIP2SxLT5TrxinRRuem1r1IcOTJsMJyUPp1QsZ7bOyq9G_0060B4CPyovw5523hEuqLTM-R5e5yavY6C_1DHUyE15C3mrh7kdvmlGZeflnHqkFTEKwwOftm-Mv-CKD5Db9ABFGNxKB2FH7nDH67hfOvm4tGNMzceBPKYJ3wciTt9jK3wy39_7cOYVywfrZ-oLhw_XtsbGSSeGn3HytrfgSADAh2sT0Gg6eCC9Xy1vh-Za337SVLUDXZ73W2xJxxUHBkFzZs8L_Xndo5DsbpWhVs9IYUGyraJdqB3SLgDbAppIBCJl4fx6_DG8-xOQPBvuFMlTROe1JVdHOzXI1GElwFDTuH1pjkg4I2G0NhAbE06Y-1illQE; gdsid=1697828393025:1697831731408:99C30D94108AC3030D61C736DDCDF11C',
"gd-csrf-token": "Ft6oHEWlRZrxDww95Cpazw:0pGUrkb2y3TyOpAIqF2vbPmUXoXVkD3oEGDVkvfeCerceQ5-n8mBg3BovySUIjmCPHCaW0H2nQVdqzbtsYqf4Q:wcqRqeegRUa9MVLJGyujVXB7vWFPjdaS1CtrrzJq-ok",
"origin": "https://www.glassdoor.com",
"referer": "https://www.glassdoor.com/",
"sec-ch-ua": '"Chromium";v="118", "Google Chrome";v="118", "Not=A?Brand";v="99"',
"sec-ch-ua-mobile": "?0",
"sec-ch-ua-platform": '"macOS"',
"sec-fetch-dest": "empty",
"sec-fetch-mode": "cors",
"sec-fetch-site": "same-origin",
"user-agent": "Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/118.0.0.0 Safari/537.36",
}

View File

@@ -8,6 +8,7 @@ import re
import math
import io
import json
from typing import Any
from datetime import datetime
import urllib.parse
@@ -21,6 +22,7 @@ from ..utils import (
extract_emails_from_text,
create_session,
get_enum_from_job_type,
modify_and_get_description
)
from ...jobs import (
JobPost,
@@ -43,7 +45,7 @@ class IndeedScraper(Scraper):
site = Site(Site.INDEED)
super().__init__(site, proxy=proxy)
self.jobs_per_page = 15
self.jobs_per_page = 25
self.seen_urls = set()
def scrape_page(
@@ -56,32 +58,15 @@ class IndeedScraper(Scraper):
:return: jobs found on page, total number of jobs found for search
"""
self.country = scraper_input.country
domain = self.country.domain_value
domain = self.country.indeed_domain_value
self.url = f"https://{domain}.indeed.com"
session = create_session(self.proxy)
params = {
"q": scraper_input.search_term,
"l": scraper_input.location,
"filter": 0,
"start": scraper_input.offset + page * 10,
}
if scraper_input.distance:
params["radius"] = scraper_input.distance
sc_values = []
if scraper_input.is_remote:
sc_values.append("attr(DSQF7)")
if scraper_input.job_type:
sc_values.append("jt({})".format(scraper_input.job_type.value))
if sc_values:
params["sc"] = "0kf:" + "".join(sc_values) + ";"
try:
session = create_session(self.proxy)
response = session.get(
f"{self.url}/jobs",
f"{self.url}/m/jobs",
headers=self.get_headers(),
params=params,
params=self.add_params(scraper_input, page),
allow_redirects=True,
timeout_seconds=10,
)
@@ -110,8 +95,8 @@ class IndeedScraper(Scraper):
):
raise IndeedException("No jobs found.")
def process_job(job) -> JobPost | None:
job_url = f'{self.url}/jobs/viewjob?jk={job["jobkey"]}'
def process_job(job: dict) -> JobPost | None:
job_url = f'{self.url}/m/jobs/viewjob?jk={job["jobkey"]}'
job_url_client = f'{self.url}/viewjob?jk={job["jobkey"]}'
if job_url in self.seen_urls:
return None
@@ -139,7 +124,8 @@ class IndeedScraper(Scraper):
date_posted = datetime.fromtimestamp(timestamp_seconds)
date_posted = date_posted.strftime("%Y-%m-%d")
description = self.get_description(job_url)
description = self.get_description(job_url) if scraper_input.full_description else None
with io.StringIO(job["snippet"]) as f:
soup_io = BeautifulSoup(f, "html.parser")
li_elements = soup_io.find_all("li")
@@ -150,6 +136,7 @@ class IndeedScraper(Scraper):
title=job["normTitle"],
description=description,
company_name=job["company"],
company_url=self.url + job["companyOverviewLink"] if "companyOverviewLink" in job else None,
location=Location(
city=job.get("jobLocationCity"),
state=job.get("jobLocationState"),
@@ -167,8 +154,9 @@ class IndeedScraper(Scraper):
)
return job_post
workers = 10 if scraper_input.full_description else 10 # possibly lessen 10 when fetching desc based on feedback
jobs = jobs["metaData"]["mosaicProviderJobCardsModel"]["results"]
with ThreadPoolExecutor(max_workers=1) as executor:
with ThreadPoolExecutor(max_workers=workers) as executor:
job_results: list[Future] = [
executor.submit(process_job, job) for job in jobs
]
@@ -190,7 +178,7 @@ class IndeedScraper(Scraper):
#: get first page to initialize session
job_list, total_results = self.scrape_page(scraper_input, 0)
with ThreadPoolExecutor(max_workers=1) as executor:
with ThreadPoolExecutor(max_workers=10) as executor:
futures: list[Future] = [
executor.submit(self.scrape_page, scraper_input, page)
for page in range(1, pages_to_process + 1)
@@ -219,7 +207,7 @@ class IndeedScraper(Scraper):
parsed_url = urllib.parse.urlparse(job_page_url)
params = urllib.parse.parse_qs(parsed_url.query)
jk_value = params.get("jk", [None])[0]
formatted_url = f"{self.url}/viewjob?jk={jk_value}&spa=1"
formatted_url = f"{self.url}/m/viewjob?jk={jk_value}&spa=1"
session = create_session(self.proxy)
try:
@@ -235,37 +223,24 @@ class IndeedScraper(Scraper):
if response.status_code not in range(200, 400):
return None
soup = BeautifulSoup(response.text, "html.parser")
script_tag = soup.find(
"script", text=lambda x: x and "window._initialData" in x
)
if not script_tag:
return None
script_code = script_tag.string
match = re.search(r"window\._initialData\s*=\s*({.*?})\s*;", script_code, re.S)
if not match:
return None
json_string = match.group(1)
data = json.loads(json_string)
try:
job_description = data["jobInfoWrapperModel"]["jobInfoModel"][
"sanitizedJobDescription"
]
soup = BeautifulSoup(response.text, 'html.parser')
script_tags = soup.find_all('script')
job_description = ''
for tag in script_tags:
if 'window._initialData' in tag.text:
json_str = tag.text
json_str = json_str.split('window._initialData=')[1]
json_str = json_str.rsplit(';', 1)[0]
data = json.loads(json_str)
job_description = data["jobInfoWrapperModel"]["jobInfoModel"]["sanitizedJobDescription"]
break
except (KeyError, TypeError, IndexError):
return None
soup = BeautifulSoup(
job_description, "html.parser"
)
text_content = " ".join(
soup.get_text(separator=" ").split()
).strip()
return text_content
soup = BeautifulSoup(job_description, "html.parser")
return modify_and_get_description(soup)
@staticmethod
def get_job_type(job: dict) -> list[JobType] | None:
@@ -324,7 +299,7 @@ class IndeedScraper(Scraper):
raise IndeedException("Could not find mosaic provider job cards data")
else:
raise IndeedException(
"Could not find a script tag containing mosaic provider data"
"Could not find any results for the search"
)
@staticmethod
@@ -348,17 +323,14 @@ class IndeedScraper(Scraper):
@staticmethod
def get_headers():
return {
"authority": "www.indeed.com",
"accept": "*/*",
"accept-language": "en-US,en;q=0.9",
"referer": "https://www.indeed.com/viewjob?jk=fe6182337d72c7b1&tk=1hcbfcmd0k62t802&from=serp&vjs=3&advn=8132938064490989&adid=408692607&ad=-6NYlbfkN0A3Osc99MJFDKjquSk4WOGT28ALb_ad4QMtrHreCb9ICg6MiSVy9oDAp3evvOrI7Q-O9qOtQTg1EPbthP9xWtBN2cOuVeHQijxHjHpJC65TjDtftH3AXeINjBvAyDrE8DrRaAXl8LD3Fs1e_xuDHQIssdZ2Mlzcav8m5jHrA0fA64ZaqJV77myldaNlM7-qyQpy4AsJQfvg9iR2MY7qeC5_FnjIgjKIy_lNi9OPMOjGRWXA94CuvC7zC6WeiJmBQCHISl8IOBxf7EdJZlYdtzgae3593TFxbkd6LUwbijAfjax39aAuuCXy3s9C4YgcEP3TwEFGQoTpYu9Pmle-Ae1tHGPgsjxwXkgMm7Cz5mBBdJioglRCj9pssn-1u1blHZM4uL1nK9p1Y6HoFgPUU9xvKQTHjKGdH8d4y4ETyCMoNF4hAIyUaysCKdJKitC8PXoYaWhDqFtSMR4Jys8UPqUV&xkcb=SoDD-_M3JLQfWnQTDh0LbzkdCdPP&xpse=SoBa6_I3JLW9FlWZlB0PbzkdCdPP&sjdu=i6xVERweJM_pVUvgf-MzuaunBTY7G71J5eEX6t4DrDs5EMPQdODrX7Nn-WIPMezoqr5wA_l7Of-3CtoiUawcHw",
"sec-ch-ua": '"Google Chrome";v="119", "Chromium";v="119", "Not?A_Brand";v="24"',
"sec-ch-ua-mobile": "?0",
"sec-ch-ua-platform": '"Windows"',
"sec-fetch-dest": "empty",
"sec-fetch-mode": "cors",
"sec-fetch-site": "same-origin",
"user-agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/119.0.0.0 Safari/537.36",
'Host': 'www.indeed.com',
'accept': 'text/html,application/xhtml+xml,application/xml;q=0.9,*/*;q=0.8',
'sec-fetch-site': 'same-origin',
'sec-fetch-dest': 'document',
'accept-language': 'en-US,en;q=0.9',
'sec-fetch-mode': 'navigate',
'user-agent': 'Mozilla/5.0 (iPhone; CPU iPhone OS 16_6_1 like Mac OS X) AppleWebKit/605.1.15 (KHTML, like Gecko) Mobile/15E148 Indeed App 192.0',
'referer': 'https://www.indeed.com/m/jobs?q=software%20intern&l=Dallas%2C%20TX&from=serpso&rq=1&rsIdx=3',
}
@staticmethod
@@ -371,3 +343,29 @@ class IndeedScraper(Scraper):
if taxonomy["label"] == "remote" and len(taxonomy["attributes"]) > 0:
return True
return False
@staticmethod
def add_params(scraper_input: ScraperInput, page: int) -> dict[str, str | Any]:
params = {
"q": scraper_input.search_term,
"l": scraper_input.location,
"filter": 0,
"start": scraper_input.offset + page * 10,
"sort": "date"
}
if scraper_input.distance:
params["radius"] = scraper_input.distance
sc_values = []
if scraper_input.is_remote:
sc_values.append("attr(DSQF7)")
if scraper_input.job_type:
sc_values.append("jt({})".format(scraper_input.job_type.value))
if sc_values:
params["sc"] = "0kf:" + "".join(sc_values) + ";"
if scraper_input.easy_apply:
params['iafilter'] = 1
return params

View File

@@ -4,31 +4,40 @@ jobspy.scrapers.linkedin
This module contains routines to scrape LinkedIn.
"""
import time
import random
from typing import Optional
from datetime import datetime
import requests
import time
from requests.exceptions import ProxyError
from concurrent.futures import ThreadPoolExecutor, as_completed
from bs4 import BeautifulSoup
from bs4.element import Tag
from threading import Lock
from bs4.element import Tag
from bs4 import BeautifulSoup
from urllib.parse import urlparse, urlunparse
from .. import Scraper, ScraperInput, Site
from ..utils import count_urgent_words, extract_emails_from_text, get_enum_from_job_type
from ..exceptions import LinkedInException
from ..utils import create_session
from ...jobs import (
JobPost,
Location,
JobResponse,
JobType,
Country,
Compensation
)
from ..utils import (
count_urgent_words,
extract_emails_from_text,
get_enum_from_job_type,
currency_parser,
modify_and_get_description
)
class LinkedInScraper(Scraper):
MAX_RETRIES = 3
DELAY = 10
DELAY = 3
def __init__(self, proxy: Optional[str] = None):
"""
@@ -62,6 +71,7 @@ class LinkedInScraper(Scraper):
return mapping.get(job_type_enum, "")
while len(job_list) < scraper_input.results_wanted and page < 1000:
session = create_session(is_tls=False, has_retry=True, delay=5)
params = {
"keywords": scraper_input.search_term,
"location": scraper_input.location,
@@ -71,53 +81,35 @@ class LinkedInScraper(Scraper):
if scraper_input.job_type
else None,
"pageNum": 0,
page: page + scraper_input.offset,
"start": page + scraper_input.offset,
"f_AL": "true" if scraper_input.easy_apply else None,
}
params = {k: v for k, v in params.items() if v is not None}
params = {k: v for k, v in params.items() if v is not None}
retries = 0
while retries < self.MAX_RETRIES:
try:
response = requests.get(
response = session.get(
f"{self.url}/jobs-guest/jobs/api/seeMoreJobPostings/search?",
params=params,
allow_redirects=True,
proxies=self.proxy,
headers=self.headers(),
timeout=10,
)
response.raise_for_status()
break
except requests.HTTPError as e:
if hasattr(e, "response") and e.response is not None:
if e.response.status_code == 429:
time.sleep(self.DELAY)
retries += 1
continue
else:
raise LinkedInException(
f"bad response status code: {e.response.status_code}"
)
else:
raise
raise LinkedInException(f"bad response status code: {e.response.status_code}")
except ProxyError as e:
raise LinkedInException("bad proxy")
except Exception as e:
raise LinkedInException(str(e))
else:
# Raise an exception if the maximum number of retries is reached
raise LinkedInException(
"Max retries reached, failed to get a valid response"
)
soup = BeautifulSoup(response.text, "html.parser")
job_cards = soup.find_all("div", class_="base-search-card")
if len(job_cards) == 0:
return JobResponse(jobs=job_list)
with ThreadPoolExecutor(max_workers=5) as executor:
futures = []
for job_card in soup.find_all("div", class_="base-search-card"):
for job_card in job_cards:
job_url = None
href_tag = job_card.find("a", class_="base-card__full-link")
if href_tag and "href" in href_tag.attrs:
@@ -130,28 +122,47 @@ class LinkedInScraper(Scraper):
continue
seen_urls.add(job_url)
futures.append(executor.submit(self.process_job, job_card, job_url))
for future in as_completed(futures):
# Call process_job directly without threading
try:
job_post = future.result()
job_post = self.process_job(job_card, job_url, scraper_input.full_description)
if job_post:
job_list.append(job_post)
except Exception as e:
raise LinkedInException(
"Exception occurred while processing jobs"
)
raise LinkedInException("Exception occurred while processing jobs")
page += 25
time.sleep(random.uniform(LinkedInScraper.DELAY, LinkedInScraper.DELAY + 2))
job_list = job_list[: scraper_input.results_wanted]
return JobResponse(jobs=job_list)
def process_job(self, job_card: Tag, job_url: str) -> Optional[JobPost]:
def process_job(self, job_card: Tag, job_url: str, full_descr: bool) -> Optional[JobPost]:
salary_tag = job_card.find('span', class_='job-search-card__salary-info')
compensation = None
if salary_tag:
salary_text = salary_tag.get_text(separator=' ').strip()
salary_values = [currency_parser(value) for value in salary_text.split('-')]
salary_min = salary_values[0]
salary_max = salary_values[1]
currency = salary_text[0] if salary_text[0] != '$' else 'USD'
compensation = Compensation(
min_amount=int(salary_min),
max_amount=int(salary_max),
currency=currency,
)
title_tag = job_card.find("span", class_="sr-only")
title = title_tag.get_text(strip=True) if title_tag else "N/A"
company_tag = job_card.find("h4", class_="base-search-card__subtitle")
company_a_tag = company_tag.find("a") if company_tag else None
company_url = (
urlunparse(urlparse(company_a_tag.get("href"))._replace(query=""))
if company_a_tag and company_a_tag.has_attr("href")
else ""
)
company = company_a_tag.get_text(strip=True) if company_a_tag else "N/A"
metadata_card = job_card.find("div", class_="base-search-card__metadata")
@@ -162,7 +173,7 @@ class LinkedInScraper(Scraper):
if metadata_card
else None
)
date_posted = None
date_posted = description = job_type = None
if datetime_tag and "datetime" in datetime_tag.attrs:
datetime_str = datetime_tag["datetime"]
try:
@@ -171,19 +182,20 @@ class LinkedInScraper(Scraper):
date_posted = None
benefits_tag = job_card.find("span", class_="result-benefits__text")
benefits = " ".join(benefits_tag.get_text().split()) if benefits_tag else None
if full_descr:
description, job_type = self.get_job_description(job_url)
return JobPost(
title=title,
description=description,
company_name=company,
company_url=company_url,
location=location,
date_posted=date_posted,
job_url=job_url,
# job_type=[JobType.FULL_TIME],
job_type=job_type,
compensation=compensation,
benefits=benefits,
job_type=job_type,
description=description,
emails=extract_emails_from_text(description) if description else None,
num_urgent_words=count_urgent_words(description) if description else None,
)
@@ -197,10 +209,15 @@ class LinkedInScraper(Scraper):
:return: description or None
"""
try:
response = requests.get(job_page_url, timeout=5, proxies=self.proxy)
session = create_session(is_tls=False, has_retry=True)
response = session.get(job_page_url, timeout=5, proxies=self.proxy)
response.raise_for_status()
except requests.HTTPError as e:
return None, None
except Exception as e:
return None, None
if response.url == "https://www.linkedin.com/signup":
return None, None
soup = BeautifulSoup(response.text, "html.parser")
div_content = soup.find(
@@ -209,7 +226,7 @@ class LinkedInScraper(Scraper):
description = None
if div_content:
description = " ".join(div_content.get_text().split()).strip()
description = modify_and_get_description(div_content)
def get_job_type(
soup_job_type: BeautifulSoup,
@@ -236,7 +253,7 @@ class LinkedInScraper(Scraper):
employment_type = employment_type.lower()
employment_type = employment_type.replace("-", "")
return [get_enum_from_job_type(employment_type)]
return [get_enum_from_job_type(employment_type)] if employment_type else []
return description, get_job_type(soup)
@@ -246,7 +263,7 @@ class LinkedInScraper(Scraper):
:param metadata_card
:return: location
"""
location = Location(country=self.country)
location = Location(country=Country.from_string(self.country))
if metadata_card is not None:
location_tag = metadata_card.find(
"span", class_="job-search-card__location"
@@ -258,7 +275,32 @@ class LinkedInScraper(Scraper):
location = Location(
city=city,
state=state,
country=self.country,
country=Country.from_string(self.country),
)
elif len(parts) == 3:
city, state, country = parts
location = Location(
city=city,
state=state,
country=Country.from_string(country),
)
return location
@staticmethod
def headers() -> dict:
return {
'authority': 'www.linkedin.com',
'accept': 'text/html,application/xhtml+xml,application/xml;q=0.9,image/avif,image/webp,image/apng,*/*;q=0.8,application/signed-exchange;v=b3;q=0.7',
'accept-language': 'en-US,en;q=0.9',
'cache-control': 'max-age=0',
'sec-ch-ua': '"Not_A Brand";v="8", "Chromium";v="120", "Google Chrome";v="120"',
# 'sec-ch-ua-mobile': '?0',
# 'sec-ch-ua-platform': '"macOS"',
# 'sec-fetch-dest': 'document',
# 'sec-fetch-mode': 'navigate',
# 'sec-fetch-site': 'none',
# 'sec-fetch-user': '?1',
'upgrade-insecure-requests': '1',
'user-agent': 'Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/120.0.0.0 Safari/537.36'
}

View File

@@ -1,8 +1,22 @@
import re
import numpy as np
import tls_client
import requests
from requests.adapters import HTTPAdapter, Retry
from ..jobs import JobType
def modify_and_get_description(soup):
for li in soup.find_all('li'):
li.string = "- " + li.get_text()
description = soup.get_text(separator='\n').strip()
description = re.sub(r'\n+', '\n', description)
return description
def count_urgent_words(description: str) -> int:
"""
Count the number of urgent words or phrases in a job description.
@@ -24,23 +38,33 @@ def extract_emails_from_text(text: str) -> list[str] | None:
return email_regex.findall(text)
def create_session(proxy: str | None = None):
def create_session(proxy: dict | None = None, is_tls: bool = True, has_retry: bool = False, delay: int = 1) -> requests.Session:
"""
Creates a tls client session
Creates a requests session with optional tls, proxy, and retry settings.
:return: A session object with or without proxies.
:return: A session object
"""
if is_tls:
session = tls_client.Session(
client_identifier="chrome112",
random_tls_extension_order=True,
)
session.proxies = proxy
# TODO multiple proxies
# if self.proxies:
# session.proxies = {
# "http": random.choice(self.proxies),
# "https": random.choice(self.proxies),
# }
else:
session = requests.Session()
session.allow_redirects = True
if proxy:
session.proxies.update(proxy)
if has_retry:
retries = Retry(total=3,
connect=3,
status=3,
status_forcelist=[500, 502, 503, 504, 429],
backoff_factor=delay)
adapter = HTTPAdapter(max_retries=retries)
session.mount('http://', adapter)
session.mount('https://', adapter)
return session
@@ -54,3 +78,19 @@ def get_enum_from_job_type(job_type_str: str) -> JobType | None:
if job_type_str in job_type.value:
res = job_type
return res
def currency_parser(cur_str):
# Remove any non-numerical characters
# except for ',' '.' or '-' (e.g. EUR)
cur_str = re.sub("[^-0-9.,]", '', cur_str)
# Remove any 000s separators (either , or .)
cur_str = re.sub("[.,]", '', cur_str[:-3]) + cur_str[-3:]
if '.' in list(cur_str[-3:]):
num = float(cur_str)
elif ',' in list(cur_str[-3:]):
num = float(cur_str.replace(',', '.'))
else:
num = float(cur_str)
return np.round(num, 2)

View File

@@ -6,58 +6,49 @@ This module contains routines to scrape ZipRecruiter.
"""
import math
import time
import re
from datetime import datetime, date
from datetime import datetime, timezone
from typing import Optional, Tuple, Any
from urllib.parse import urlparse, parse_qs, urlunparse
import requests
from bs4 import BeautifulSoup
from bs4.element import Tag
from concurrent.futures import ThreadPoolExecutor, Future
from concurrent.futures import ThreadPoolExecutor
from .. import Scraper, ScraperInput, Site
from ..exceptions import ZipRecruiterException
from ..utils import count_urgent_words, extract_emails_from_text, create_session
from ...jobs import (
JobPost,
Compensation,
CompensationInterval,
Location,
JobResponse,
JobType,
Country,
)
from ...jobs import JobPost, Compensation, Location, JobResponse, JobType, Country
from ..utils import count_urgent_words, extract_emails_from_text, create_session, modify_and_get_description
class ZipRecruiterScraper(Scraper):
def __init__(self, proxy: Optional[str] = None):
"""
Initializes LinkedInScraper with the ZipRecruiter job search url
Initializes ZipRecruiterScraper with the ZipRecruiter job search url
"""
site = Site(Site.ZIP_RECRUITER)
self.url = "https://www.ziprecruiter.com"
self.session = create_session(proxy)
self.get_cookies()
super().__init__(site, proxy=proxy)
self.jobs_per_page = 20
self.seen_urls = set()
def find_jobs_in_page(self, scraper_input: ScraperInput, continue_token: Optional[str] = None) -> Tuple[list[JobPost], Optional[str]]:
def find_jobs_in_page(
self, scraper_input: ScraperInput, continue_token: str | None = None
) -> Tuple[list[JobPost], Optional[str]]:
"""
Scrapes a page of ZipRecruiter for jobs with scraper_input criteria
:param scraper_input:
:param continue_token:
:return: jobs found on page
"""
params = self.add_params(scraper_input)
if continue_token:
params['continue'] = continue_token
params["continue_from"] = continue_token
try:
response = requests.get(
response = self.session.get(
f"https://api.ziprecruiter.com/jobs-app/jobs",
headers=self.headers(),
params=self.add_params(scraper_input),
allow_redirects=True,
timeout=10,
params=params
)
if response.status_code != 200:
raise ZipRecruiterException(
@@ -71,15 +62,12 @@ class ZipRecruiterScraper(Scraper):
time.sleep(5)
response_data = response.json()
jobs_list = response_data.get("jobs", [])
next_continue_token = response_data.get('continue', None)
next_continue_token = response_data.get("continue", None)
with ThreadPoolExecutor(max_workers=10) as executor:
job_results = [
executor.submit(self.process_job, job)
for job in jobs_list
]
with ThreadPoolExecutor(max_workers=self.jobs_per_page) as executor:
job_results = [executor.submit(self.process_job, job) for job in jobs_list]
job_list = [result.result() for result in job_results if result.result()]
job_list = list(filter(None, (result.result() for result in job_results)))
return job_list, next_continue_token
def scrape(self, scraper_input: ScraperInput) -> JobResponse:
@@ -97,46 +85,40 @@ class ZipRecruiterScraper(Scraper):
if len(job_list) >= scraper_input.results_wanted:
break
jobs_on_page, continue_token = self.find_jobs_in_page(scraper_input, continue_token)
jobs_on_page, continue_token = self.find_jobs_in_page(
scraper_input, continue_token
)
if jobs_on_page:
job_list.extend(jobs_on_page)
if not continue_token:
break
if len(job_list) > scraper_input.results_wanted:
job_list = job_list[:scraper_input.results_wanted]
return JobResponse(jobs=job_list[: scraper_input.results_wanted])
return JobResponse(jobs=job_list)
def process_job(self, job: dict) -> JobPost:
"""the most common type of jobs page on ZR"""
def process_job(self, job: dict) -> JobPost | None:
"""Processes an individual job dict from the response"""
title = job.get("name")
job_url = job.get("job_url")
job_url = f"https://www.ziprecruiter.com/jobs//j?lvk={job['listing_key']}"
if job_url in self.seen_urls:
return
self.seen_urls.add(job_url)
job_description_html = job.get("job_description", "").strip()
description_soup = BeautifulSoup(job_description_html, "html.parser")
description = modify_and_get_description(description_soup)
description = BeautifulSoup(
job.get("job_description", "").strip(), "html.parser"
).get_text()
company = job["hiring_company"].get("name") if "hiring_company" in job else None
country_value = "usa" if job.get("job_country") == "US" else "canada"
country_enum = Country.from_string(country_value)
company = job['hiring_company'].get("name") if "hiring_company" in job else None
location = Location(
city=job.get("job_city"), state=job.get("job_state"), country='usa' if job.get("job_country") == 'US' else 'canada'
city=job.get("job_city"), state=job.get("job_state"), country=country_enum
)
job_type = ZipRecruiterScraper.get_job_type_enum(
job.get("employment_type", "").replace("_", "").lower()
)
save_job_url = job.get("SaveJobURL", "")
posted_time_match = re.search(
r"posted_time=(\d{4}-\d{2}-\d{2}T\d{2}:\d{2}:\d{2}Z)", save_job_url
)
if posted_time_match:
date_time_str = posted_time_match.group(1)
date_posted_obj = datetime.strptime(date_time_str, "%Y-%m-%dT%H:%M:%SZ")
date_posted = date_posted_obj.date()
else:
date_posted = date.today()
date_posted = datetime.fromisoformat(job['posted_time'].rstrip("Z")).date()
return JobPost(
title=title,
@@ -144,9 +126,15 @@ class ZipRecruiterScraper(Scraper):
location=location,
job_type=job_type,
compensation=Compensation(
interval="yearly" if job.get("compensation_interval") == "annual" else job.get("compensation_interval") ,
min_amount=int(job["compensation_min"]) if "compensation_min" in job else None,
max_amount=int(job["compensation_max"]) if "compensation_max" in job else None,
interval="yearly"
if job.get("compensation_interval") == "annual"
else job.get("compensation_interval"),
min_amount=int(job["compensation_min"])
if "compensation_min" in job
else None,
max_amount=int(job["compensation_max"])
if "compensation_max" in job
else None,
currency=job.get("compensation_currency"),
),
date_posted=date_posted,
@@ -156,6 +144,11 @@ class ZipRecruiterScraper(Scraper):
num_urgent_words=count_urgent_words(description) if description else None,
)
def get_cookies(self):
url="https://api.ziprecruiter.com/jobs-app/event"
data="event_type=session&logged_in=false&number_of_retry=1&property=model%3AiPhone&property=os%3AiOS&property=locale%3Aen_us&property=app_build_number%3A4734&property=app_version%3A91.0&property=manufacturer%3AApple&property=timestamp%3A2024-01-12T12%3A04%3A42-06%3A00&property=screen_height%3A852&property=os_version%3A16.6.1&property=source%3Ainstall&property=screen_width%3A393&property=device_model%3AiPhone%2014%20Pro&property=brand%3AApple"
self.session.post(url, data=data, headers=ZipRecruiterScraper.headers())
@staticmethod
def get_job_type_enum(job_type_str: str) -> list[JobType] | None:
for job_type in JobType:
@@ -168,7 +161,6 @@ class ZipRecruiterScraper(Scraper):
params = {
"search": scraper_input.search_term,
"location": scraper_input.location,
"form": "jobs-landing",
}
job_type_value = None
if scraper_input.job_type:
@@ -178,6 +170,8 @@ class ZipRecruiterScraper(Scraper):
job_type_value = "part_time"
else:
job_type_value = scraper_input.job_type.value
if scraper_input.easy_apply:
params['zipapply'] = 1
if job_type_value:
params[
@@ -192,107 +186,6 @@ class ZipRecruiterScraper(Scraper):
return params
@staticmethod
def get_interval(interval_str: str):
"""
Maps the interval alias to its appropriate CompensationInterval.
:param interval_str
:return: CompensationInterval
"""
interval_alias = {"annually": CompensationInterval.YEARLY}
interval_str = interval_str.lower()
if interval_str in interval_alias:
return interval_alias[interval_str]
return CompensationInterval(interval_str)
@staticmethod
def get_date_posted(job: Tag) -> Optional[datetime.date]:
"""
Extracts the date a job was posted
:param job
:return: date the job was posted or None
"""
button = job.find(
"button", {"class": "action_input save_job zrs_btn_secondary_200"}
)
if not button:
return None
url_time = button.get("data-href", "")
url_components = urlparse(url_time)
params = parse_qs(url_components.query)
posted_time_str = params.get("posted_time", [None])[0]
if posted_time_str:
posted_date = datetime.strptime(
posted_time_str, "%Y-%m-%dT%H:%M:%SZ"
).date()
return posted_date
return None
@staticmethod
def get_compensation(job: Tag) -> Optional[Compensation]:
"""
Parses the compensation tag from the job BeautifulSoup object
:param job
:return: Compensation object or None
"""
pay_element = job.find("li", {"class": "perk_item perk_pay"})
if pay_element is None:
return None
pay = pay_element.find("div", {"class": "value"}).find("span").text.strip()
def create_compensation_object(pay_string: str) -> Compensation:
"""
Creates a Compensation object from a pay_string
:param pay_string
:return: compensation
"""
interval = ZipRecruiterScraper.get_interval(pay_string.split()[-1])
amounts = []
for amount in pay_string.split("to"):
amount = amount.replace(",", "").strip("$ ").split(" ")[0]
if "K" in amount:
amount = amount.replace("K", "")
amount = int(float(amount)) * 1000
else:
amount = int(float(amount))
amounts.append(amount)
compensation = Compensation(
interval=interval,
min_amount=min(amounts),
max_amount=max(amounts),
currency="USD/CAD",
)
return compensation
return create_compensation_object(pay)
@staticmethod
def get_location(job: Tag) -> Location:
"""
Extracts the job location from BeatifulSoup object
:param job:
:return: location
"""
location_link = job.find("a", {"class": "company_location"})
if location_link is not None:
location_string = location_link.text.strip()
parts = location_string.split(", ")
if len(parts) == 2:
city, state = parts
else:
city, state = None, None
else:
city, state = None, None
return Location(city=city, state=state, country=Country.US_CANADA)
@staticmethod
def headers() -> dict:
"""
@@ -300,13 +193,12 @@ class ZipRecruiterScraper(Scraper):
:return: dict - Dictionary containing headers
"""
return {
'Host': 'api.ziprecruiter.com',
'Cookie': 'ziprecruiter_browser=018188e0-045b-4ad7-aa50-627a6c3d43aa; ziprecruiter_session=5259b2219bf95b6d2299a1417424bc2edc9f4b38; SplitSV=2016-10-19%3AU2FsdGVkX19f9%2Bx70knxc%2FeR3xXR8lWoTcYfq5QjmLU%3D%0A; __cf_bm=qXim3DtLPbOL83GIp.ddQEOFVFTc1OBGPckiHYxcz3o-1698521532-0-AfUOCkgCZyVbiW1ziUwyefCfzNrJJTTKPYnif1FZGQkT60dMowmSU/Y/lP+WiygkFPW/KbYJmyc+MQSkkad5YygYaARflaRj51abnD+SyF9V; zglobalid=68d49bd5-0326-428e-aba8-8a04b64bc67c.af2d99ff7c03.653d61bb; ziprecruiter_browser=018188e0-045b-4ad7-aa50-627a6c3d43aa; ziprecruiter_session=5259b2219bf95b6d2299a1417424bc2edc9f4b38',
'accept': '*/*',
'x-zr-zva-override': '100000000;vid:ZT1huzm_EQlDTVEc',
'x-pushnotificationid': '0ff4983d38d7fc5b3370297f2bcffcf4b3321c418f5c22dd152a0264707602a0',
'x-deviceid': 'D77B3A92-E589-46A4-8A39-6EF6F1D86006',
'user-agent': 'Job Search/87.0 (iPhone; CPU iOS 16_6_1 like Mac OS X)',
'authorization': 'Basic YTBlZjMyZDYtN2I0Yy00MWVkLWEyODMtYTI1NDAzMzI0YTcyOg==',
'accept-language': 'en-US,en;q=0.9'
"Host": "api.ziprecruiter.com",
"accept": "*/*",
"x-zr-zva-override": "100000000;vid:ZT1huzm_EQlDTVEc",
"x-pushnotificationid": "0ff4983d38d7fc5b3370297f2bcffcf4b3321c418f5c22dd152a0264707602a0",
"x-deviceid": "D77B3A92-E589-46A4-8A39-6EF6F1D86006",
"user-agent": "Job Search/87.0 (iPhone; CPU iOS 16_6_1 like Mac OS X)",
"authorization": "Basic YTBlZjMyZDYtN2I0Yy00MWVkLWEyODMtYTI1NDAzMzI0YTcyOg==",
"accept-language": "en-US,en;q=0.9",
}

View File

@@ -4,7 +4,7 @@ import pandas as pd
def test_all():
result = scrape_jobs(
site_name=["linkedin", "indeed", "zip_recruiter"],
site_name=["linkedin", "indeed", "zip_recruiter", "glassdoor"],
search_term="software engineer",
results_wanted=5,
)

View File

@@ -0,0 +1,11 @@
from ..jobspy import scrape_jobs
import pandas as pd
def test_indeed():
result = scrape_jobs(
site_name="glassdoor", search_term="software engineer", country_indeed="USA"
)
assert (
isinstance(result, pd.DataFrame) and not result.empty
), "Result should be a non-empty DataFrame"

View File

@@ -4,8 +4,7 @@ import pandas as pd
def test_indeed():
result = scrape_jobs(
site_name="indeed",
search_term="software engineer",
site_name="indeed", search_term="software engineer", country_indeed="usa"
)
assert (
isinstance(result, pd.DataFrame) and not result.empty