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117 Commits

Author SHA1 Message Date
Cullen Watson
89a3ee231c enh(li): job function (#160) 2024-05-28 16:01:29 -05:00
Cullen
6439f71433 chore: version 2024-05-28 15:39:24 -05:00
adamagassi
7f6271b2e0 LinkedIn scraper fixes: (#159)
Correct initial page offset calculation
Separate page variable from request counter
Fix job offset starting value
Increment offset by number of jobs returned instead of expected value
2024-05-28 15:38:13 -05:00
Cullen Watson
5cb7ffe5fd enh: proxies (#157)
* enh: proxies

* enh: proxies
2024-05-25 14:04:09 -05:00
Cullen Watson
cd29f79796 docs: readme 2024-05-25 11:46:23 -05:00
Cullen Watson
65d2e5e707 Update pyproject.toml 2024-05-20 11:46:36 -05:00
fasih hussain
08d63a87a2 chore: id added for JobPost schema (#152) 2024-05-20 11:45:52 -05:00
Cullen
1ffdb1756f fix: dup line 2024-04-30 12:11:48 -05:00
Cullen Watson
1185693422 delete empty file 2024-04-30 12:06:20 -05:00
Lluís Salord Quetglas
dcd7144318 FIX: Allow Indeed search term with complex syntax (#139) 2024-04-30 12:05:43 -05:00
Cullen Watson
bf73c061bd enh: linkedin company logo (#141) 2024-04-30 12:03:10 -05:00
Lluís Salord Quetglas
8dd08ed9fd FEAT: Allow LinkedIn scraper to get external job apply url (#140) 2024-04-30 11:36:01 -05:00
Cullen Watson
5d3df732e6 docs: readme 2024-03-12 20:46:25 -05:00
Kellen Mace
86f858e06d Update scrape_jobs() parameters info in readme (#130) 2024-03-12 20:45:13 -05:00
Cullen
1089d1f0a5 docs: readme 2024-03-11 21:30:57 -05:00
Cullen
3e93454738 fix(indeed): readd param 2024-03-11 21:23:20 -05:00
Cullen Watson
0d150d519f docs: readme 2024-03-11 14:52:20 -05:00
Cullen Watson
cc3497f929 docs: readme 2024-03-11 14:45:17 -05:00
Cullen Watson
5986f75346 docs: readme 2024-03-11 14:41:12 -05:00
VitaminB16
4b7bdb9313 feat: Adjust log verbosity via verbose arg (#128) 2024-03-11 14:38:44 -05:00
Cullen Watson
80213f28d2 chore: version 2024-03-11 09:43:12 -05:00
Cullen Watson
ada38532c3 fix: indeed empty location term 2024-03-11 09:42:43 -05:00
Cullen Watson
3b0017964c fix: indeed empty search term 2024-03-11 09:21:11 -05:00
VitaminB16
94d8f555fd format: Apply Black formatter to the codebase (#127) 2024-03-10 23:36:27 -05:00
Cullen Watson
e8b4b376b8 docs: readme 2024-03-09 13:40:34 -06:00
Cullen Watson
54ac1bad16 docs: readme 2024-03-09 01:49:05 -06:00
Cullen Watson
0a669e9ba8 enh: indeed more fields (#126) 2024-03-09 01:40:01 -06:00
gigaSec
a4f6851c32 Fix GlassDoor Country Vietnam(#122) 2024-03-04 17:35:57 -06:00
troy-conte
db01bc6bbb log search updates, fix glassdoor (#120) 2024-03-04 16:39:38 -06:00
Cullen Watson
f8a4eccc6b Remove pandas warning (#118) 2024-02-29 21:30:56 -06:00
Cullen Watson
ba3a16b228 Description format (#107) 2024-02-14 16:04:23 -06:00
Cullen Watson
aeb1a50d2c fix job type search (#106) 2024-02-12 11:02:48 -06:00
VitaminB16
91b137ef86 feat: Ability to query by time posted for linkedin, indeed, glassdoor, ziprecruiter (#103) 2024-02-09 14:02:03 -06:00
Cullen Watson
2563c5ca08 enh: Indeed company url (#104) 2024-02-09 12:05:10 -06:00
Cullen Watson
32282305c8 docs: readme 2024-02-08 18:13:19 -06:00
Cullen Watson
ccbea51f3c docs: readme 2024-02-04 09:25:10 -06:00
Cullen Watson
6ec7c24f7f enh(linkedin): search by company ids (#99) 2024-02-04 09:21:45 -06:00
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
Cullen
e2f6885d61 chore: format 2023-10-28 16:52:05 -05:00
Cullen
8d65d1b652 [chore] version 2023-10-28 16:43:44 -05:00
Cullen
216d3fd39f ziprecruiter: 5s delay 2023-10-28 16:41:32 -05:00
Cullen Watson
d3bfdc0a6e ziprecruiter api (#63) 2023-10-28 16:17:28 -05:00
Cullen Watson
ba5ed803ca use ziprecuriter api (#62) 2023-10-28 15:51:29 -05:00
Cullen Watson
ff1eb0f7b0 [docs] update readme 2023-10-18 14:32:21 -05:00
Cullen Watson
f2cc74b7f2 Fix Indeed exceptions on parsing description 2023-10-18 14:25:53 -05:00
Cullen Watson
5e71866630 [docs] link change 2023-10-18 11:18:03 -05:00
Zachary Hampton
4e67c6e5a3 Update README.md 2023-10-17 20:22:56 -07:00
Cullen Watson
caf655525a docs: update readme 2023-10-10 11:54:14 -05:00
Cullen Watson
90fa4a4c4f feat: utils.py 2023-10-10 11:29:29 -05:00
Cullen Watson
e5353e604d Multiple job types for Indeed, urgent keywords column (#56)
* enh(indeed): mult job types

* feat(jobs):  urgent kws

* fix(indeed): use new session obj per request

* fix: emails as comma separated in output

* fix: put num urgent words in output

* chore: readme
2023-10-10 11:23:04 -05:00
Cullen Watson
628f4dee9c [fix] indeed - min & max values swapped (#54) 2023-10-03 09:22:18 -05:00
Cullen Watson
2e59ab03e3 Merge branch 'main' of https://github.com/cullenwatson/JobSpy 2023-09-28 18:53:59 -05:00
Cullen Watson
008ca61e12 [fix] readd hyperlink param 2023-09-28 18:53:21 -05:00
Cullen Watson
8fc4c3bf90 [docs] readme 2023-09-28 18:35:40 -05:00
Cullen Watson
bff39a2625 [fix] util func 2023-09-28 18:33:14 -05:00
Cullen Watson
c676050dc0 [fix] util func 2023-09-28 18:33:02 -05:00
Cullen Watson
37976f7ec2 [chore] version number 2023-09-28 18:26:55 -05:00
Cullen Watson
9fb2fdd80f [fix] add utils.py 2023-09-28 18:25:56 -05:00
Cullen Watson
af07c1ecbd add offset param & email extraction (#51)
* add offset param

* [enh]: extract emails
2023-09-28 18:11:28 -05:00
Cullen Watson
286b9e1256 chore: version number 2023-09-21 20:28:57 -05:00
Cullen Watson
162dd40b0f docs: add usejobspy.com 2023-09-21 20:27:04 -05:00
Cullen Watson
558e352939 fix: job type param bug 2023-09-21 17:42:24 -05:00
Zachary Hampton
efad1a1b7d Update README.md 2023-09-21 09:52:18 -07:00
Cullen Watson
eaa481c2f4 docs: add macos catalina to faq 2023-09-19 12:50:14 -05:00
Zachary Hampton
b914aa6449 Update README.md 2023-09-16 13:52:30 -07:00
Zachary Hampton
6adbfb8b29 Update README.md 2023-09-16 13:51:45 -07:00
Zachary Hampton
a3b9dd50ff (docs) homepage 2023-09-15 16:14:26 -07:00
Zachary Hampton
d3ba3a4878 docs: sales call 2023-09-15 11:51:22 -07:00
Cullen Watson
f524789d74 docs: grammar readme 2023-09-15 10:18:24 -05:00
Cullen Watson
f3890d4830 docs: update 2023-09-09 10:55:33 -05:00
Cullen Watson
60c9728691 docs: typo 2023-09-08 12:27:49 -05:00
Cullen Watson
f79d975e5f docs: clarify - README.md 2023-09-07 13:46:14 -05:00
Cullen Watson
d6368f909b docs: typo 2023-09-07 13:39:56 -05:00
Cullen Watson
6fcf7f666e docs: update typo in example 2023-09-07 13:37:53 -05:00
Cullen Watson
4406f9350f docs: update vid 2023-09-07 13:35:10 -05:00
Cullen Watson
ca5155f234 docs: add feature 2023-09-07 11:36:16 -05:00
Cullen Watson
822a55783e docs: temp update 2023-09-07 11:35:14 -05:00
Cullen Watson
59f739018a Proxy support (#44)
* add proxy support

* return as data frame
2023-09-07 11:28:17 -05:00
Zachary Hampton
a37e7f235e Merge pull request #42 from cullenwatson/fix/class-type-error
- refactor & #41 bug fix
2023-09-06 16:33:59 -07:00
Zachary Hampton
690739e858 - refactor & #41 bug fix 2023-09-06 16:32:51 -07:00
Cullen Watson
43eb2fe0e8 remove gitattr 2023-09-06 11:34:51 -05:00
Cullen Watson
e50227bba6 clear output jupyter 2023-09-06 11:32:32 -05:00
Cullen Watson
45c2d76e15 add yt guide 2023-09-06 11:26:55 -05:00
Cullen Watson
fd883178be Thread sites (#40) 2023-09-06 09:47:11 -05:00
Cullen Watson
70e2218c67 reduce size of jupyter notebook 2023-09-05 13:09:18 -05:00
Cullen Watson
d6947ecdd7 Update README.md 2023-09-05 13:03:32 -05:00
Cullen Watson
5191658562 Update README.md 2023-09-05 12:27:00 -05:00
21 changed files with 3485 additions and 3374 deletions

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@@ -7,27 +7,27 @@ jobs:
runs-on: ubuntu-latest
steps:
- uses: actions/checkout@v3
- name: Set up Python
uses: actions/setup-python@v4
with:
python-version: "3.10"
- uses: actions/checkout@v3
- name: Set up Python
uses: actions/setup-python@v4
with:
python-version: "3.10"
- name: Install poetry
run: >-
python3 -m
pip install
poetry
--user
- name: Install poetry
run: >-
python3 -m
pip install
poetry
--user
- name: Build distribution 📦
run: >-
python3 -m
poetry
build
- name: Build distribution 📦
run: >-
python3 -m
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 }}
- 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 }}

8
.gitignore vendored
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@@ -1,10 +1,10 @@
/.idea
**/.DS_Store
/venv/
/ven/
/.idea
**/__pycache__/
**/.pytest_cache/
/.ipynb_checkpoints/
**/output/
**/.DS_Store
*.pyc
.env
dist
/.ipynb_checkpoints/

7
.pre-commit-config.yaml Normal file
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@@ -0,0 +1,7 @@
repos:
- repo: https://github.com/psf/black
rev: 24.2.0
hooks:
- id: black
language_version: python
args: [--line-length=88, --quiet]

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266
README.md
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@@ -1,55 +1,55 @@
<img src="https://github.com/cullenwatson/JobSpy/assets/78247585/ae185b7e-e444-4712-8bb9-fa97f53e896b" width="400">
**JobSpy** is a simple, yet comprehensive, job scraping library.
**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://bunsly.com/)** *to
work with us.*
## 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
- Proxies support
![jobspy](https://github.com/cullenwatson/JobSpy/assets/78247585/ec7ef355-05f6-4fd3-8161-a817e31c5c57)
### Installation
`pip install python-jobspy`
_Python version >= [3.10](https://www.python.org/downloads/release/python-3100/) required_
```
pip install -U python-jobspy
```
_Python version >= [3.10](https://www.python.org/downloads/release/python-3100/) required_
### Usage
```python
import csv
from jobspy import scrape_jobs
import pandas as pd
jobs: pd.DataFrame = scrape_jobs(
site_name=["indeed", "linkedin", "zip_recruiter"],
jobs = scrape_jobs(
site_name=["indeed", "linkedin", "zip_recruiter", "glassdoor"],
search_term="software engineer",
location="Dallas, TX",
results_wanted=10,
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
# linkedin_fetch_description=True # get full description and direct job url for linkedin (slower)
# proxies=["208.195.175.46:65095", "208.195.175.45:65095", "localhost"],
# country: only needed for indeed
country='USA'
)
if jobs.empty:
print("No jobs found.")
else:
#1 print
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) # set to 0 to see full job url / desc
print(jobs)
#2 display in Jupyter Notebook
#display(jobs)
#3 output to .csv
#jobs.to_csv('jobs.csv', index=False)
print(f"Found {len(jobs)} jobs")
print(jobs.head())
jobs.to_csv("jobs.csv", quoting=csv.QUOTE_NONNUMERIC, escapechar="\\", index=False) # to_excel
```
### Output
```
SITE TITLE COMPANY_NAME CITY STATE JOB_TYPE INTERVAL MIN_AMOUNT MAX_AMOUNT JOB_URL DESCRIPTION
SITE TITLE COMPANY CITY STATE JOB_TYPE INTERVAL MIN_AMOUNT MAX_AMOUNT JOB_URL DESCRIPTION
indeed Software Engineer AMERICAN SYSTEMS Arlington VA None yearly 200000 150000 https://www.indeed.com/viewjob?jk=5e409e577046... THIS POSITION COMES WITH A 10K SIGNING BONUS!...
indeed Senior Software Engineer TherapyNotes.com Philadelphia PA fulltime yearly 135000 110000 https://www.indeed.com/viewjob?jk=da39574a40cb... About Us TherapyNotes is the national leader i...
linkedin Software Engineer - Early Career Lockheed Martin Sunnyvale CA fulltime yearly None None https://www.linkedin.com/jobs/view/3693012711 Description:By bringing together people that u...
@@ -57,139 +57,167 @@ linkedin Full-Stack Software Engineer Rain New York
zip_recruiter Software Engineer - New Grad ZipRecruiter Santa Monica CA fulltime yearly 130000 150000 https://www.ziprecruiter.com/jobs/ziprecruiter... We offer a hybrid work environment. Most US-ba...
zip_recruiter Software Developer TEKsystems Phoenix AZ fulltime hourly 65 75 https://www.ziprecruiter.com/jobs/teksystems-0... Top Skills' Details• 6 years of Java developme...
```
### Parameters for `scrape_jobs()`
```plaintext
Required
├── site_type (List[enum]): linkedin, zip_recruiter, indeed
└── search_term (str)
Optional
├── location (int)
├── distance (int): in miles
├── job_type (enum): fulltime, parttime, internship, contract
├── site_name (list|str):
| linkedin, zip_recruiter, indeed, glassdoor
| (default is all four)
├── search_term (str)
├── location (str)
├── distance (int):
| in miles, default 50
├── job_type (str):
| fulltime, parttime, internship, contract
├── proxies ():
| in format ['user:pass@host:port', 'localhost']
| each job board will round robin through the proxies
├── is_remote (bool)
├── results_wanted (int): number of job results to retrieve for each site specified in 'site_type'
├── easy_apply (bool): filters for jobs on LinkedIn that have the 'Easy Apply' option
├── country (enum): uses the corresponding subdomain on Indeed (e.g. Canada on Indeed is ca.indeed.com
├── results_wanted (int):
| 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
├── description_format (str):
| markdown, html (Format type of the job descriptions. Default is markdown.)
├── offset (int):
| starts the search from an offset (e.g. 25 will start the search from the 25th result)
├── hours_old (int):
| filters jobs by the number of hours since the job was posted
| (ZipRecruiter and Glassdoor round up to next day.)
├── verbose (int) {0, 1, 2}:
| Controls the verbosity of the runtime printouts
| (0 prints only errors, 1 is errors+warnings, 2 is all logs. Default is 2.)
├── linkedin_fetch_description (bool):
| fetches full description and direct job url for LinkedIn (Increases requests by O(n))
├── linkedin_company_ids (list[int]):
| searches for linkedin jobs with specific company ids
|
├── country_indeed (str):
| filters the country on Indeed & Glassdoor (see below for correct spelling)
```
```
├── Indeed limitations:
| Only one from this list can be used in a search:
| - hours_old
| - job_type & is_remote
| - easy_apply
└── LinkedIn limitations:
| Only one from this list can be used in a search:
| - hours_old
| - easy_apply
```
### JobPost Schema
```plaintext
JobPost
├── title (str)
├── company_name (str)
├── company (str)
├── company_url (str)
├── job_url (str)
├── location (object)
│ ├── country (str)
│ ├── city (str)
│ ├── state (str)
├── description (str)
├── job_type (enum)
├── job_type (str): fulltime, parttime, internship, contract
├── compensation (object)
│ ├── interval (CompensationInterval): yearly, monthly, weekly, daily, hourly
│ ├── interval (str): yearly, monthly, weekly, daily, hourly
│ ├── min_amount (int)
│ ├── max_amount (int)
│ └── currency (str)
└── date_posted (datetime)
│ └── currency (enum)
└── date_posted (date)
└── emails (str)
└── is_remote (bool)
Indeed specific
├── company_country (str)
└── company_addresses (str)
└── company_industry (str)
└── company_employees_label (str)
└── company_revenue_label (str)
└── company_description (str)
└── ceo_name (str)
└── ceo_photo_url (str)
└── logo_photo_url (str)
└── banner_photo_url (str)
```
## Supported Countries for Job Searching
### **LinkedIn**
LinkedIn searches globally. Use the `location` parameter
LinkedIn searches globally & uses only the `location` parameter.
### **ZipRecruiter**
ZipRecruiter searches for jobs in US/Canada. Use the `location` parameter
ZipRecruiter searches for jobs in **US/Canada** & uses only the `location` parameter.
### **Indeed / Glassdoor**
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, * indicates support for Glassdoor):
| | | | |
|----------------------|--------------|------------|----------------|
| 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 |
| Oman | Pakistan | Panama | Peru |
| Philippines | Poland | Portugal | Qatar |
| Romania | Saudi Arabia | Singapore* | South Africa |
| South Korea | Spain* | Sweden | Switzerland* |
| Taiwan | Thailand | Turkey | Ukraine |
| United Arab Emirates | UK* | USA* | Uruguay |
| Venezuela | Vietnam* | | |
### **Indeed**
For Indeed, you `location` along with `country` param
You can specify the following countries when searching on Indeed (use the exact name):
- 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
- Oman
- Pakistan
- Panama
- Peru
- Philippines
- Poland
- Portugal
- Qatar
- Romania
- Saudi Arabia
- Singapore
- South Africa
- South Korea
- Spain
- Sweden
- Switzerland
- Taiwan
- Thailand
- Turkey
- Ukraine
- United Arab Emirates
- UK
- USA
- Uruguay
- Venezuela
- Vietnam
## Notes
* Indeed is the best scraper currently with no rate limiting.
* All the job board endpoints are capped at around 1000 jobs on a given search.
* LinkedIn is the most restrictive and usually rate limits around the 10th page with one ip. Proxies are a must basically.
## Frequently Asked Questions
---
**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](#).
**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).
---
**Q: Received a response code 429?**
**A:** This indicates that you have been blocked by the job board site for sending too many requests. Currently, **ZipRecruiter** is particularly aggressive with blocking. We recommend:
**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.
- Trying a VPN to change your IP address.
**Note:** Proxy support is in development and coming soon!
- Wait some time between scrapes (site-dependent).
- Try using the proxies param to change your IP address.
---

2262
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@@ -1,8 +1,9 @@
[tool.poetry]
name = "python-jobspy"
version = "1.1.0"
description = "Job scraper for LinkedIn, Indeed & ZipRecruiter"
authors = ["Zachary Hampton <zachary@zacharysproducts.com>", "Cullen Watson <cullen@cullen.ai>"]
version = "1.1.55"
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"
packages = [
@@ -12,16 +13,24 @@ packages = [
[tool.poetry.dependencies]
python = "^3.10"
requests = "^2.31.0"
tls-client = "^0.2.1"
beautifulsoup4 = "^4.12.2"
pandas = "^2.1.0"
NUMPY = "1.24.2"
pydantic = "^2.3.0"
tls-client = "^1.0.1"
markdownify = "^0.11.6"
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

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@@ -1,48 +1,84 @@
from __future__ import annotations
import pandas as pd
from typing import List, Tuple
from typing import Tuple
from concurrent.futures import ThreadPoolExecutor, as_completed
from .jobs import JobType, Location
from .scrapers.utils import logger, set_logger_level
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
SCRAPER_MAPPING = {
Site.LINKEDIN: LinkedInScraper,
Site.INDEED: IndeedScraper,
Site.ZIP_RECRUITER: ZipRecruiterScraper,
}
def _map_str_to_site(site_name: str) -> Site:
return Site[site_name.upper()]
from .scrapers.exceptions import (
LinkedInException,
IndeedException,
ZipRecruiterException,
GlassdoorException,
)
def scrape_jobs(
site_name: str | Site | List[Site],
search_term: str,
location: str = "",
distance: int = None,
site_name: str | list[str] | Site | list[Site] | None = None,
search_term: str | None = None,
location: str | None = None,
distance: int | None = 50,
is_remote: bool = False,
job_type: JobType = None,
easy_apply: bool = False, # linkedin
job_type: str | None = None,
easy_apply: bool | None = None,
results_wanted: int = 15,
country: str = "usa",
country_indeed: str = "usa",
hyperlinks: bool = False,
proxies: list[str] | str | None = None,
description_format: str = "markdown",
linkedin_fetch_description: bool | None = False,
linkedin_company_ids: list[int] | None = None,
offset: int | None = 0,
hours_old: int = None,
verbose: int = 2,
**kwargs,
) -> pd.DataFrame:
"""
Asynchronously scrapes job data from multiple job sites.
:return: results_wanted: pandas dataframe containing job data
Simultaneously scrapes job data from multiple job sites.
:return: pandas dataframe containing job data
"""
SCRAPER_MAPPING = {
Site.LINKEDIN: LinkedInScraper,
Site.INDEED: IndeedScraper,
Site.ZIP_RECRUITER: ZipRecruiterScraper,
Site.GLASSDOOR: GlassdoorScraper,
}
set_logger_level(verbose)
if type(site_name) == str:
site_name = _map_str_to_site(site_name)
def map_str_to_site(site_name: str) -> Site:
return Site[site_name.upper()]
country_enum = Country.from_string(country)
def get_enum_from_value(value_str):
for job_type in JobType:
if value_str in job_type.value:
return job_type
raise Exception(f"Invalid job type: {value_str}")
job_type = get_enum_from_value(job_type) if job_type else None
def get_site_type():
site_types = list(Site)
if isinstance(site_name, str):
site_types = [map_str_to_site(site_name)]
elif isinstance(site_name, Site):
site_types = [site_name]
elif isinstance(site_name, list):
site_types = [
map_str_to_site(site) if isinstance(site, str) else site
for site in site_name
]
return site_types
country_enum = Country.from_string(country_indeed)
site_type = [site_name] if type(site_name) == Site else site_name
scraper_input = ScraperInput(
site_type=site_type,
site_type=get_site_type(),
country=country_enum,
search_term=search_term,
location=location,
@@ -50,72 +86,127 @@ def scrape_jobs(
is_remote=is_remote,
job_type=job_type,
easy_apply=easy_apply,
description_format=description_format,
linkedin_fetch_description=linkedin_fetch_description,
results_wanted=results_wanted,
linkedin_company_ids=linkedin_company_ids,
offset=offset,
hours_old=hours_old,
)
def scrape_site(site: Site) -> Tuple[str, JobResponse]:
scraper_class = SCRAPER_MAPPING[site]
scraper = scraper_class()
scraper = scraper_class(proxies=proxies)
scraped_data: JobResponse = scraper.scrape(scraper_input)
cap_name = site.value.capitalize()
site_name = "ZipRecruiter" if cap_name == "Zip_recruiter" else cap_name
logger.info(f"{site_name} finished scraping")
return site.value, scraped_data
results = {}
for site in scraper_input.site_type:
site_value, scraped_data = scrape_site(site)
results[site_value] = scraped_data
site_to_jobs_dict = {}
dfs = []
def worker(site):
site_val, scraped_info = scrape_site(site)
return site_val, scraped_info
for site, job_response in results.items():
with ThreadPoolExecutor() as executor:
future_to_site = {
executor.submit(worker, site): site for site in scraper_input.site_type
}
for future in as_completed(future_to_site):
site_value, scraped_data = future.result()
site_to_jobs_dict[site_value] = scraped_data
jobs_dfs: list[pd.DataFrame] = []
for site, job_response in site_to_jobs_dict.items():
for job in job_response.jobs:
data = job.dict()
data["site"] = site
data["company"] = data["company_name"]
if data["job_type"]:
# Take the first value from the job type tuple
data["job_type"] = data["job_type"].value[0]
else:
data["job_type"] = None
job_data = job.dict()
job_url = job_data["job_url"]
job_data["job_url_hyper"] = f'<a href="{job_url}">{job_url}</a>'
job_data["site"] = site
job_data["company"] = job_data["company_name"]
job_data["job_type"] = (
", ".join(job_type.value[0] for job_type in job_data["job_type"])
if job_data["job_type"]
else None
)
job_data["emails"] = (
", ".join(job_data["emails"]) if job_data["emails"] else None
)
if job_data["location"]:
job_data["location"] = Location(
**job_data["location"]
).display_location()
data["location"] = Location(**data["location"]).display_location()
compensation_obj = data.get("compensation")
compensation_obj = job_data.get("compensation")
if compensation_obj and isinstance(compensation_obj, dict):
data["interval"] = (
job_data["interval"] = (
compensation_obj.get("interval").value
if compensation_obj.get("interval")
else None
)
data["min_amount"] = compensation_obj.get("min_amount")
data["max_amount"] = compensation_obj.get("max_amount")
data["currency"] = compensation_obj.get("currency", "USD")
job_data["min_amount"] = compensation_obj.get("min_amount")
job_data["max_amount"] = compensation_obj.get("max_amount")
job_data["currency"] = compensation_obj.get("currency", "USD")
else:
data["interval"] = None
data["min_amount"] = None
data["max_amount"] = None
data["currency"] = None
job_data["interval"] = None
job_data["min_amount"] = None
job_data["max_amount"] = None
job_data["currency"] = None
job_df = pd.DataFrame([data])
dfs.append(job_df)
job_df = pd.DataFrame([job_data])
jobs_dfs.append(job_df)
if dfs:
df = pd.concat(dfs, ignore_index=True)
if jobs_dfs:
# Step 1: Filter out all-NA columns from each DataFrame before concatenation
filtered_dfs = [df.dropna(axis=1, how="all") for df in jobs_dfs]
# Step 2: Concatenate the filtered DataFrames
jobs_df = pd.concat(filtered_dfs, ignore_index=True)
# Desired column order
desired_order = [
"id",
"site",
"job_url_hyper" if hyperlinks else "job_url",
"job_url_direct",
"title",
"company",
"location",
"job_type",
"date_posted",
"interval",
"min_amount",
"max_amount",
"currency",
"job_url",
"is_remote",
"job_function",
"emails",
"description",
"company_url",
"company_url_direct",
"company_addresses",
"company_industry",
"company_num_employees",
"company_revenue",
"company_description",
"logo_photo_url",
"banner_photo_url",
"ceo_name",
"ceo_photo_url",
]
df = df[desired_order]
else:
df = pd.DataFrame()
return df
# Step 3: Ensure all desired columns are present, adding missing ones as empty
for column in desired_order:
if column not in jobs_df.columns:
jobs_df[column] = None # Add missing columns as empty
# Reorder the DataFrame according to the desired order
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])
else:
return pd.DataFrame()

View File

@@ -1,8 +1,9 @@
from typing import Union, Optional
from __future__ import annotations
from typing import Optional
from datetime import date
from enum import Enum
from pydantic import BaseModel, validator
from pydantic import BaseModel
class JobType(Enum):
@@ -37,10 +38,16 @@ class JobType(Enum):
"повназайнятість",
"toànthờigian",
)
PART_TIME = ("parttime", "teilzeit")
PART_TIME = ("parttime", "teilzeit", "částečnýúvazek", "deltid")
CONTRACT = ("contract", "contractor")
TEMPORARY = ("temporary",)
INTERNSHIP = ("internship", "prácticas", "ojt(onthejobtraining)", "praktikum")
INTERNSHIP = (
"internship",
"prácticas",
"ojt(onthejobtraining)",
"praktikum",
"praktik",
)
PER_DIEM = ("perdiem",)
NIGHTS = ("nights",)
@@ -50,40 +57,46 @@ 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 (and API country code if there's a ':' separator) 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")
@@ -96,54 +109,66 @@ 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:gb", "co.uk")
USA = ("usa,us,united states", "www:us", "com")
URUGUAY = ("uruguay", "uy")
VENEZUELA = ("venezuela", "ve")
VIETNAM = ("vietnam", "vn")
VIETNAM = ("vietnam", "vn", "com")
# 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):
subdomain, _, api_country_code = self.value[1].partition(":")
if subdomain and api_country_code:
return subdomain, api_country_code.upper()
return self.value[1], self.value[1].upper()
@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_glassdoor_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 | str | None = None
city: Optional[str] = None
state: Optional[str] = None
@@ -153,11 +178,19 @@ class Location(BaseModel):
location_parts.append(self.city)
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())
if isinstance(self.country, str):
location_parts.append(self.country)
elif self.country and self.country not in (
Country.US_CANADA,
Country.WORLDWIDE,
):
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)
@@ -168,43 +201,62 @@ class CompensationInterval(Enum):
DAILY = "daily"
HOURLY = "hourly"
@classmethod
def get_interval(cls, pay_period):
interval_mapping = {
"YEAR": cls.YEARLY,
"HOUR": cls.HOURLY,
}
if pay_period in interval_mapping:
return interval_mapping[pay_period].value
else:
return cls[pay_period].value if pay_period in cls.__members__ else None
class Compensation(BaseModel):
interval: CompensationInterval
min_amount: int = None
max_amount: int = None
interval: Optional[CompensationInterval] = None
min_amount: float | None = None
max_amount: float | None = None
currency: Optional[str] = "USD"
class DescriptionFormat(Enum):
MARKDOWN = "markdown"
HTML = "html"
class JobPost(BaseModel):
id: str | None = None
title: str
company_name: str
company_name: str | None
job_url: str
job_url_direct: str | None = None
location: Optional[Location]
description: Optional[str] = None
job_type: Optional[JobType] = None
compensation: Optional[Compensation] = None
date_posted: Optional[date] = None
description: str | None = None
company_url: str | None = None
company_url_direct: str | None = None
job_type: list[JobType] | None = None
compensation: Compensation | None = None
date_posted: date | None = None
emails: list[str] | None = None
is_remote: bool | None = None
# indeed specific
company_addresses: str | None = None
company_industry: str | None = None
company_num_employees: str | None = None
company_revenue: str | None = None
company_description: str | None = None
ceo_name: str | None = None
ceo_photo_url: str | None = None
logo_photo_url: str | None = None
banner_photo_url: str | None = None
# linkedin only atm
job_function: str | None = None
class JobResponse(BaseModel):
success: bool
error: str = None
total_results: Optional[int] = None
jobs: list[JobPost] = []
returned_results: int = None
@validator("returned_results", pre=True, always=True)
def set_returned_results(cls, v, values):
jobs_list = values.get("jobs")
if v is None:
if jobs_list is not None:
return len(jobs_list)
else:
return 0
return v

View File

@@ -1,43 +1,47 @@
from ..jobs import Enum, BaseModel, JobType, JobResponse, Country
from typing import List, Optional, Any
from __future__ import annotations
from abc import ABC, abstractmethod
class StatusException(Exception):
def __init__(self, status_code: int):
self.status_code = status_code
from ..jobs import (
Enum,
BaseModel,
JobType,
JobResponse,
Country,
DescriptionFormat,
)
class Site(Enum):
LINKEDIN = "linkedin"
INDEED = "indeed"
ZIP_RECRUITER = "zip_recruiter"
GLASSDOOR = "glassdoor"
class ScraperInput(BaseModel):
site_type: List[Site]
search_term: str
site_type: list[Site]
search_term: str | None = None
location: str = None
country: Optional[Country] = Country.USA
distance: Optional[int] = None
location: str | None = None
country: Country | None = Country.USA
distance: int | None = None
is_remote: bool = False
job_type: Optional[JobType] = None
easy_apply: bool = None # linkedin
job_type: JobType | None = None
easy_apply: bool | None = None
offset: int = 0
linkedin_fetch_description: bool = False
linkedin_company_ids: list[int] | None = None
description_format: DescriptionFormat | None = DescriptionFormat.MARKDOWN
results_wanted: int = 15
hours_old: int | None = None
class CommonResponse(BaseModel):
status: Optional[str]
error: Optional[str]
linkedin: Optional[Any] = None
indeed: Optional[Any] = None
zip_recruiter: Optional[Any] = None
class Scraper:
def __init__(self, site: Site):
class Scraper(ABC):
def __init__(self, site: Site, proxies: list[str] | None = None):
self.proxies = proxies
self.site = site
def scrape(self, scraper_input: ScraperInput) -> JobResponse:
...
@abstractmethod
def scrape(self, scraper_input: ScraperInput) -> JobResponse: ...

View File

@@ -0,0 +1,26 @@
"""
jobspy.scrapers.exceptions
~~~~~~~~~~~~~~~~~~~
This module contains the set of Scrapers' exceptions.
"""
class LinkedInException(Exception):
def __init__(self, message=None):
super().__init__(message or "An error occurred with LinkedIn")
class IndeedException(Exception):
def __init__(self, message=None):
super().__init__(message or "An error occurred with Indeed")
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 +1,534 @@
"""
jobspy.scrapers.glassdoor
~~~~~~~~~~~~~~~~~~~
This module contains routines to scrape Glassdoor.
"""
from __future__ import annotations
import re
import json
import requests
from typing import Optional, Tuple
from datetime import datetime, timedelta
from concurrent.futures import ThreadPoolExecutor, as_completed
from .. import Scraper, ScraperInput, Site
from ..utils import extract_emails_from_text
from ..exceptions import GlassdoorException
from ..utils import (
create_session,
markdown_converter,
logger,
)
from ...jobs import (
JobPost,
Compensation,
CompensationInterval,
Location,
JobResponse,
JobType,
DescriptionFormat,
)
class GlassdoorScraper(Scraper):
def __init__(self, proxies: list[str] | str | None = None):
"""
Initializes GlassdoorScraper with the Glassdoor job search url
"""
site = Site(Site.GLASSDOOR)
super().__init__(site, proxies=proxies)
self.base_url = None
self.country = None
self.session = None
self.scraper_input = None
self.jobs_per_page = 30
self.max_pages = 30
self.seen_urls = set()
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.
"""
self.scraper_input = scraper_input
self.scraper_input.results_wanted = min(900, scraper_input.results_wanted)
self.base_url = self.scraper_input.country.get_glassdoor_url()
self.session = create_session(proxies=self.proxies, is_tls=True, has_retry=True)
token = self._get_csrf_token()
self.headers["gd-csrf-token"] = token if token else self.fallback_token
location_id, location_type = self._get_location(
scraper_input.location, scraper_input.is_remote
)
if location_type is None:
logger.error("Glassdoor: location not parsed")
return JobResponse(jobs=[])
all_jobs: list[JobPost] = []
cursor = None
range_start = 1 + (scraper_input.offset // self.jobs_per_page)
tot_pages = (scraper_input.results_wanted // self.jobs_per_page) + 2
range_end = min(tot_pages, self.max_pages + 1)
for page in range(range_start, range_end):
logger.info(f"Glassdoor search page: {page}")
try:
jobs, cursor = self._fetch_jobs_page(
scraper_input, location_id, location_type, page, cursor
)
all_jobs.extend(jobs)
if not jobs or len(all_jobs) >= scraper_input.results_wanted:
all_jobs = all_jobs[: scraper_input.results_wanted]
break
except Exception as e:
logger.error(f"Glassdoor: {str(e)}")
break
return JobResponse(jobs=all_jobs)
def _fetch_jobs_page(
self,
scraper_input: ScraperInput,
location_id: int,
location_type: str,
page_num: int,
cursor: str | None,
) -> Tuple[list[JobPost], str | None]:
"""
Scrapes a page of Glassdoor for jobs with scraper_input criteria
"""
jobs = []
self.scraper_input = scraper_input
try:
payload = self._add_payload(location_id, location_type, page_num, cursor)
response = self.session.post(
f"{self.base_url}/graph",
headers=self.headers,
timeout_seconds=15,
data=payload,
)
if response.status_code != 200:
exc_msg = f"bad response status code: {response.status_code}"
raise GlassdoorException(exc_msg)
res_json = response.json()[0]
if "errors" in res_json:
raise ValueError("Error encountered in API response")
except (
requests.exceptions.ReadTimeout,
GlassdoorException,
ValueError,
Exception,
) as e:
logger.error(f"Glassdoor: {str(e)}")
return jobs, None
jobs_data = res_json["data"]["jobListings"]["jobListings"]
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):
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 _get_csrf_token(self):
"""
Fetches csrf token needed for API by visiting a generic page
"""
res = self.session.get(
f"{self.base_url}/Job/computer-science-jobs.htm", headers=self.headers
)
pattern = r'"token":\s*"([^"]+)"'
matches = re.findall(pattern, res.text)
token = None
if matches:
token = matches[0]
return token
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.base_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_diff = (datetime.now() - timedelta(days=age_in_days)).date()
date_posted = date_diff if age_in_days is not None 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:
description = None
company_url = f"{self.base_url}Overview/W-EI_IE{company_id}.htm"
return JobPost(
id=str(job_id),
title=title,
company_url=company_url 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,
)
def _fetch_job_description(self, job_id):
"""
Fetches the job description for a single job ID.
"""
url = f"{self.base_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
}
}
""",
}
]
res = requests.post(url, json=body, headers=self.headers)
if res.status_code != 200:
return None
data = res.json()[0]
desc = data["data"]["jobview"]["job"]["description"]
if self.scraper_input.description_format == DescriptionFormat.MARKDOWN:
desc = markdown_converter(desc)
return desc
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.base_url}/findPopularLocationAjax.htm?maxLocationsToReturn=10&term={location}"
res = self.session.get(url, headers=self.headers)
if res.status_code != 200:
if res.status_code == 429:
err = f"429 Response - Blocked by Glassdoor for too many requests"
logger.error(err)
return None, None
else:
err = f"Glassdoor response status code {res.status_code}"
err += f" - {res.text}"
logger.error(f"Glassdoor response status code {res.status_code}")
return None, None
items = res.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
def _add_payload(
self,
location_id: int,
location_type: str,
page_num: int,
cursor: str | None = None,
) -> str:
fromage = None
if self.scraper_input.hours_old:
fromage = max(self.scraper_input.hours_old // 24, 1)
filter_params = []
if self.scraper_input.easy_apply:
filter_params.append({"filterKey": "applicationType", "values": "1"})
if fromage:
filter_params.append({"filterKey": "fromAge", "values": str(fromage)})
payload = {
"operationName": "JobSearchResultsQuery",
"variables": {
"excludeJobListingIds": [],
"filterParams": filter_params,
"keyword": self.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,
"fromage": fromage,
"sort": "date",
},
"query": self.query_template,
}
if self.scraper_input.job_type:
payload["variables"]["filterParams"].append(
{"filterKey": "jobType", "values": self.scraper_input.job_type.value[0]}
)
return json.dumps([payload])
@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,
)
@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"]
fallback_token = "Ft6oHEWlRZrxDww95Cpazw:0pGUrkb2y3TyOpAIqF2vbPmUXoXVkD3oEGDVkvfeCerceQ5-n8mBg3BovySUIjmCPHCaW0H2nQVdqzbtsYqf4Q:wcqRqeegRUa9MVLJGyujVXB7vWFPjdaS1CtrrzJq-ok"
headers = {
"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",
"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",
}
query_template = """
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
) {
jobListings(
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
}
}
) {
companyFilterOptions {
id
shortName
__typename
}
filterOptions
indeedCtk
jobListings {
...JobView
__typename
}
jobListingSeoLinks {
linkItems {
position
url
__typename
}
__typename
}
jobSearchTrackingKey
jobsPageSeoData {
pageMetaDescription
pageTitle
__typename
}
paginationCursors {
cursor
pageNumber
__typename
}
indexablePageForSeo
searchResultsMetadata {
searchCriteria {
implicitLocation {
id
localizedDisplayName
type
__typename
}
keyword
location {
id
shortName
localizedShortName
localizedDisplayName
type
__typename
}
__typename
}
helpCenterDomain
helpCenterLocale
jobSerpJobOutlook {
occupation
paragraph
__typename
}
showMachineReadableJobs
__typename
}
totalJobsCount
__typename
}
}
fragment JobView on JobListingSearchResult {
jobview {
header {
adOrderId
advertiserType
adOrderSponsorshipLevel
ageInDays
divisionEmployerName
easyApply
employer {
id
name
shortName
__typename
}
employerNameFromSearch
goc
gocConfidence
gocId
jobCountryId
jobLink
jobResultTrackingKey
jobTitleText
locationName
locationType
locId
needsCommission
payCurrency
payPeriod
payPeriodAdjustedPay {
p10
p50
p90
__typename
}
rating
salarySource
savedJobId
sponsored
__typename
}
job {
description
importConfigId
jobTitleId
jobTitleText
listingId
__typename
}
jobListingAdminDetails {
cpcVal
importConfigId
jobListingId
jobSourceId
userEligibleForAdminJobDetails
__typename
}
overview {
shortName
squareLogoUrl
__typename
}
__typename
}
__typename
}
"""

View File

@@ -1,17 +1,25 @@
import re
import math
import io
import json
import traceback
from datetime import datetime
from typing import Optional
"""
jobspy.scrapers.indeed
~~~~~~~~~~~~~~~~~~~
import tls_client
import urllib.parse
from bs4 import BeautifulSoup
from bs4.element import Tag
This module contains routines to scrape Indeed.
"""
from __future__ import annotations
import math
from typing import Tuple
from datetime import datetime
from concurrent.futures import ThreadPoolExecutor, Future
from .. import Scraper, ScraperInput, Site
from ..utils import (
extract_emails_from_text,
get_enum_from_job_type,
markdown_converter,
logger,
create_session,
)
from ...jobs import (
JobPost,
Compensation,
@@ -19,143 +27,26 @@ from ...jobs import (
Location,
JobResponse,
JobType,
DescriptionFormat,
)
from .. import Scraper, ScraperInput, Site, Country, StatusException
class ParsingException(Exception):
pass
class IndeedScraper(Scraper):
def __init__(self):
def __init__(self, proxies: list[str] | str | None = None):
"""
Initializes IndeedScraper with the Indeed job search url
Initializes IndeedScraper with the Indeed API url
"""
site = Site(Site.INDEED)
super().__init__(site)
super().__init__(Site.INDEED, proxies=proxies)
self.jobs_per_page = 15
self.session = create_session(proxies=self.proxies, is_tls=False)
self.scraper_input = None
self.jobs_per_page = 100
self.num_workers = 10
self.seen_urls = set()
def scrape_page(
self, scraper_input: ScraperInput, page: int, session: tls_client.Session
) -> tuple[list[JobPost], int]:
"""
Scrapes a page of Indeed for jobs with scraper_input criteria
:param scraper_input:
:param page:
:param session:
:return: jobs found on page, total number of jobs found for search
"""
self.country = scraper_input.country
domain = self.country.domain_value
self.url = f"https://{domain}.indeed.com"
job_list = []
params = {
"q": scraper_input.search_term,
"l": scraper_input.location,
"filter": 0,
"start": 0 + 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) + ";"
response = session.get(self.url + "/jobs", params=params, allow_redirects=True)
# print(response.status_code)
if response.status_code not in range(200, 400):
raise StatusException(response.status_code)
soup = BeautifulSoup(response.content, "html.parser")
with open("text2.html", "w", encoding="utf-8") as f:
f.write(str(soup))
if "did not match any jobs" in str(soup):
raise ParsingException("Search did not match any jobs")
jobs = IndeedScraper.parse_jobs(
soup
) #: can raise exception, handled by main scrape function
total_num_jobs = IndeedScraper.total_jobs(soup)
if (
not jobs.get("metaData", {})
.get("mosaicProviderJobCardsModel", {})
.get("results")
):
raise Exception("No jobs found.")
def process_job(job) -> Optional[JobPost]:
job_url = f'{self.url}/jobs/viewjob?jk={job["jobkey"]}'
job_url_client = f'{self.url}/viewjob?jk={job["jobkey"]}'
if job_url in self.seen_urls:
return None
extracted_salary = job.get("extractedSalary")
compensation = None
if extracted_salary:
salary_snippet = job.get("salarySnippet")
currency = salary_snippet.get("currency") if salary_snippet else None
interval = (extracted_salary.get("type"),)
if isinstance(interval, tuple):
interval = interval[0]
interval = interval.upper()
if interval in CompensationInterval.__members__:
compensation = Compensation(
interval=CompensationInterval[interval],
min_amount=int(extracted_salary.get("max")),
max_amount=int(extracted_salary.get("min")),
currency=currency,
)
job_type = IndeedScraper.get_job_type(job)
timestamp_seconds = job["pubDate"] / 1000
date_posted = datetime.fromtimestamp(timestamp_seconds)
date_posted = date_posted.strftime("%Y-%m-%d")
description = self.get_description(job_url, session)
with io.StringIO(job["snippet"]) as f:
soup = BeautifulSoup(f, "html.parser")
li_elements = soup.find_all("li")
if description is None and li_elements:
description = " ".join(li.text for li in li_elements)
job_post = JobPost(
title=job["normTitle"],
description=description,
company_name=job["company"],
location=Location(
city=job.get("jobLocationCity"),
state=job.get("jobLocationState"),
country=self.country,
),
job_type=job_type,
compensation=compensation,
date_posted=date_posted,
job_url=job_url_client,
)
return job_post
with ThreadPoolExecutor(max_workers=1) as executor:
job_results: list[Future] = [
executor.submit(process_job, job)
for job in jobs["metaData"]["mosaicProviderJobCardsModel"]["results"]
]
job_list = [result.result() for result in job_results if result.result()]
return job_list, total_num_jobs
self.headers = None
self.api_country_code = None
self.base_url = None
self.api_url = "https://apis.indeed.com/graphql"
def scrape(self, scraper_input: ScraperInput) -> JobResponse:
"""
@@ -163,167 +54,381 @@ class IndeedScraper(Scraper):
:param scraper_input:
:return: job_response
"""
session = tls_client.Session(
client_identifier="chrome112", random_tls_extension_order=True
)
self.scraper_input = scraper_input
domain, self.api_country_code = self.scraper_input.country.indeed_domain_value
self.base_url = f"https://{domain}.indeed.com"
self.headers = self.api_headers.copy()
self.headers["indeed-co"] = self.scraper_input.country.indeed_domain_value
job_list = []
page = 1
pages_to_process = (
math.ceil(scraper_input.results_wanted / self.jobs_per_page) - 1
)
cursor = None
offset_pages = math.ceil(self.scraper_input.offset / 100)
for _ in range(offset_pages):
logger.info(f"Indeed skipping search page: {page}")
__, cursor = self._scrape_page(cursor)
if not __:
logger.info(f"Indeed found no jobs on page: {page}")
break
try:
#: get first page to initialize session
job_list, total_results = self.scrape_page(scraper_input, 0, session)
while len(self.seen_urls) < scraper_input.results_wanted:
logger.info(f"Indeed search page: {page}")
jobs, cursor = self._scrape_page(cursor)
if not jobs:
logger.info(f"Indeed found no jobs on page: {page}")
break
job_list += jobs
page += 1
return JobResponse(jobs=job_list[: scraper_input.results_wanted])
with ThreadPoolExecutor(max_workers=1) as executor:
futures: list[Future] = [
executor.submit(self.scrape_page, scraper_input, page, session)
for page in range(1, pages_to_process + 1)
]
for future in futures:
jobs, _ = future.result()
job_list += jobs
except StatusException as e:
return JobResponse(
success=False,
error=f"Indeed returned status code {e.status_code}",
)
except ParsingException as e:
return JobResponse(
success=False,
error=f"Indeed failed to parse response: {e}",
)
except Exception as e:
print(f"LinkedIn failed to scrape: {e}\n{traceback.format_exc()}")
return JobResponse(
success=False,
error=f"Indeed failed to scrape: {e}",
)
if len(job_list) > scraper_input.results_wanted:
job_list = job_list[: scraper_input.results_wanted]
job_response = JobResponse(
success=True,
jobs=job_list,
total_results=total_results,
)
return job_response
def get_description(self, job_page_url: str, session: tls_client.Session) -> str:
def _scrape_page(self, cursor: str | None) -> Tuple[list[JobPost], str | None]:
"""
Retrieves job description by going to the job page url
:param job_page_url:
:param session:
:return: description
Scrapes a page of Indeed for jobs with scraper_input criteria
:param cursor:
:return: jobs found on page, next page cursor
"""
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"
try:
response = session.get(
formatted_url, allow_redirects=True, timeout_seconds=5
jobs = []
new_cursor = None
filters = self._build_filters()
search_term = (
self.scraper_input.search_term.replace('"', '\\"')
if self.scraper_input.search_term
else ""
)
query = self.job_search_query.format(
what=(f'what: "{search_term}"' if search_term else ""),
location=(
f'location: {{where: "{self.scraper_input.location}", radius: {self.scraper_input.distance}, radiusUnit: MILES}}'
if self.scraper_input.location
else ""
),
dateOnIndeed=self.scraper_input.hours_old,
cursor=f'cursor: "{cursor}"' if cursor else "",
filters=filters,
)
payload = {
"query": query,
}
api_headers = self.api_headers.copy()
api_headers["indeed-co"] = self.api_country_code
response = self.session.post(
self.api_url,
headers=api_headers,
json=payload,
timeout=10,
)
if response.status_code != 200:
logger.info(
f"Indeed responded with status code: {response.status_code} (submit GitHub issue if this appears to be a bug)"
)
except requests.exceptions.Timeout:
print("The request timed out.")
return None
return jobs, new_cursor
data = response.json()
jobs = data["data"]["jobSearch"]["results"]
new_cursor = data["data"]["jobSearch"]["pageInfo"]["nextCursor"]
if response.status_code not in range(200, 400):
print("status code not in range")
return None
with ThreadPoolExecutor(max_workers=self.num_workers) as executor:
job_results: list[Future] = [
executor.submit(self._process_job, job["job"]) for job in jobs
]
job_list = [result.result() for result in job_results if result.result()]
return job_list, new_cursor
raw_description = response.json()["body"]["jobInfoWrapperModel"][
"jobInfoModel"
]["sanitizedJobDescription"]
with io.StringIO(raw_description) as f:
soup = BeautifulSoup(f, "html.parser")
text_content = " ".join(soup.get_text().split()).strip()
return text_content
def _build_filters(self):
"""
Builds the filters dict for job type/is_remote. If hours_old is provided, composite filter for job_type/is_remote is not possible.
IndeedApply: filters: { keyword: { field: "indeedApplyScope", keys: ["DESKTOP"] } }
"""
filters_str = ""
if self.scraper_input.hours_old:
filters_str = """
filters: {{
date: {{
field: "dateOnIndeed",
start: "{start}h"
}}
}}
""".format(
start=self.scraper_input.hours_old
)
elif self.scraper_input.easy_apply:
filters_str = """
filters: {
keyword: {
field: "indeedApplyScope",
keys: ["DESKTOP"]
}
}
"""
elif self.scraper_input.job_type or self.scraper_input.is_remote:
job_type_key_mapping = {
JobType.FULL_TIME: "CF3CP",
JobType.PART_TIME: "75GKK",
JobType.CONTRACT: "NJXCK",
JobType.INTERNSHIP: "VDTG7",
}
keys = []
if self.scraper_input.job_type:
key = job_type_key_mapping[self.scraper_input.job_type]
keys.append(key)
if self.scraper_input.is_remote:
keys.append("DSQF7")
if keys:
keys_str = '", "'.join(keys) # Prepare your keys string
filters_str = f"""
filters: {{
composite: {{
filters: [{{
keyword: {{
field: "attributes",
keys: ["{keys_str}"]
}}
}}]
}}
}}
"""
return filters_str
def _process_job(self, job: dict) -> JobPost | None:
"""
Parses the job dict into JobPost model
:param job: dict to parse
:return: JobPost if it's a new job
"""
job_url = f'{self.base_url}/viewjob?jk={job["key"]}'
if job_url in self.seen_urls:
return
self.seen_urls.add(job_url)
description = job["description"]["html"]
if self.scraper_input.description_format == DescriptionFormat.MARKDOWN:
description = markdown_converter(description)
job_type = self._get_job_type(job["attributes"])
timestamp_seconds = job["datePublished"] / 1000
date_posted = datetime.fromtimestamp(timestamp_seconds).strftime("%Y-%m-%d")
employer = job["employer"].get("dossier") if job["employer"] else None
employer_details = employer.get("employerDetails", {}) if employer else {}
rel_url = job["employer"]["relativeCompanyPageUrl"] if job["employer"] else None
return JobPost(
id=str(job["key"]),
title=job["title"],
description=description,
company_name=job["employer"].get("name") if job.get("employer") else None,
company_url=(f"{self.base_url}{rel_url}" if job["employer"] else None),
company_url_direct=(
employer["links"]["corporateWebsite"] if employer else None
),
location=Location(
city=job.get("location", {}).get("city"),
state=job.get("location", {}).get("admin1Code"),
country=job.get("location", {}).get("countryCode"),
),
job_type=job_type,
compensation=self._get_compensation(job),
date_posted=date_posted,
job_url=job_url,
job_url_direct=(
job["recruit"].get("viewJobUrl") if job.get("recruit") else None
),
emails=extract_emails_from_text(description) if description else None,
is_remote=self._is_job_remote(job, description),
company_addresses=(
employer_details["addresses"][0]
if employer_details.get("addresses")
else None
),
company_industry=(
employer_details["industry"]
.replace("Iv1", "")
.replace("_", " ")
.title()
if employer_details.get("industry")
else None
),
company_num_employees=employer_details.get("employeesLocalizedLabel"),
company_revenue=employer_details.get("revenueLocalizedLabel"),
company_description=employer_details.get("briefDescription"),
ceo_name=employer_details.get("ceoName"),
ceo_photo_url=employer_details.get("ceoPhotoUrl"),
logo_photo_url=(
employer["images"].get("squareLogoUrl")
if employer and employer.get("images")
else None
),
banner_photo_url=(
employer["images"].get("headerImageUrl")
if employer and employer.get("images")
else None
),
)
@staticmethod
def get_job_type(job: dict) -> Optional[JobType]:
def _get_job_type(attributes: list) -> list[JobType]:
"""
Parses the job to get JobTypeIndeed
Parses the attributes to get list of job types
:param attributes:
:return: list of JobType
"""
job_types: list[JobType] = []
for attribute in attributes:
job_type_str = attribute["label"].replace("-", "").replace(" ", "").lower()
job_type = get_enum_from_job_type(job_type_str)
if job_type:
job_types.append(job_type)
return job_types
@staticmethod
def _get_compensation(job: dict) -> Compensation | None:
"""
Parses the job to get compensation
:param job:
:return:
:param job:
:return: compensation object
"""
for taxonomy in job["taxonomyAttributes"]:
if taxonomy["label"] == "job-types":
if len(taxonomy["attributes"]) > 0:
label = taxonomy["attributes"][0].get("label")
if label:
job_type_str = label.replace("-", "").replace(" ", "").lower()
# print(f"Debug: job_type_str = {job_type_str}")
return IndeedScraper.get_enum_from_value(job_type_str)
return None
@staticmethod
def get_enum_from_value(value_str):
for job_type in JobType:
if value_str in job_type.value:
return job_type
return None
@staticmethod
def parse_jobs(soup: BeautifulSoup) -> dict:
"""
Parses the jobs from the soup object
:param soup:
:return: jobs
"""
def find_mosaic_script() -> Optional[Tag]:
"""
Finds jobcards script tag
:return: script_tag
"""
script_tags = soup.find_all("script")
for tag in script_tags:
if (
tag.string
and "mosaic.providerData" in tag.string
and "mosaic-provider-jobcards" in tag.string
):
return tag
comp = job["compensation"]["baseSalary"]
if not comp:
return None
script_tag = find_mosaic_script()
if script_tag:
script_str = script_tag.string
pattern = r'window.mosaic.providerData\["mosaic-provider-jobcards"\]\s*=\s*({.*?});'
p = re.compile(pattern, re.DOTALL)
m = p.search(script_str)
if m:
jobs = json.loads(m.group(1).strip())
return jobs
else:
raise ParsingException("Could not find mosaic provider job cards data")
else:
raise ParsingException(
"Could not find a script tag containing mosaic provider data"
)
interval = IndeedScraper._get_compensation_interval(comp["unitOfWork"])
if not interval:
return None
min_range = comp["range"].get("min")
max_range = comp["range"].get("max")
return Compensation(
interval=interval,
min_amount=round(min_range, 2) if min_range is not None else None,
max_amount=round(max_range, 2) if max_range is not None else None,
currency=job["compensation"]["currencyCode"],
)
@staticmethod
def total_jobs(soup: BeautifulSoup) -> int:
def _is_job_remote(job: dict, description: str) -> bool:
"""
Parses the total jobs for that search from soup object
:param soup:
:return: total_num_jobs
Searches the description, location, and attributes to check if job is remote
"""
script = soup.find("script", string=lambda t: t and "window._initialData" in t)
remote_keywords = ["remote", "work from home", "wfh"]
is_remote_in_attributes = any(
any(keyword in attr["label"].lower() for keyword in remote_keywords)
for attr in job["attributes"]
)
is_remote_in_description = any(
keyword in description.lower() for keyword in remote_keywords
)
is_remote_in_location = any(
keyword in job["location"]["formatted"]["long"].lower()
for keyword in remote_keywords
)
return (
is_remote_in_attributes or is_remote_in_description or is_remote_in_location
)
pattern = re.compile(r"window._initialData\s*=\s*({.*})\s*;", re.DOTALL)
match = pattern.search(script.string)
total_num_jobs = 0
if match:
json_str = match.group(1)
data = json.loads(json_str)
total_num_jobs = int(data["searchTitleBarModel"]["totalNumResults"])
return total_num_jobs
@staticmethod
def _get_compensation_interval(interval: str) -> CompensationInterval:
interval_mapping = {
"DAY": "DAILY",
"YEAR": "YEARLY",
"HOUR": "HOURLY",
"WEEK": "WEEKLY",
"MONTH": "MONTHLY",
}
mapped_interval = interval_mapping.get(interval.upper(), None)
if mapped_interval and mapped_interval in CompensationInterval.__members__:
return CompensationInterval[mapped_interval]
else:
raise ValueError(f"Unsupported interval: {interval}")
api_headers = {
"Host": "apis.indeed.com",
"content-type": "application/json",
"indeed-api-key": "161092c2017b5bbab13edb12461a62d5a833871e7cad6d9d475304573de67ac8",
"accept": "application/json",
"indeed-locale": "en-US",
"accept-language": "en-US,en;q=0.9",
"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 193.1",
"indeed-app-info": "appv=193.1; appid=com.indeed.jobsearch; osv=16.6.1; os=ios; dtype=phone",
}
job_search_query = """
query GetJobData {{
jobSearch(
{what}
{location}
includeSponsoredResults: NONE
limit: 100
sort: DATE
{cursor}
{filters}
) {{
pageInfo {{
nextCursor
}}
results {{
trackingKey
job {{
key
title
datePublished
dateOnIndeed
description {{
html
}}
location {{
countryName
countryCode
admin1Code
city
postalCode
streetAddress
formatted {{
short
long
}}
}}
compensation {{
baseSalary {{
unitOfWork
range {{
... on Range {{
min
max
}}
}}
}}
currencyCode
}}
attributes {{
key
label
}}
employer {{
relativeCompanyPageUrl
name
dossier {{
employerDetails {{
addresses
industry
employeesLocalizedLabel
revenueLocalizedLabel
briefDescription
ceoName
ceoPhotoUrl
}}
images {{
headerImageUrl
squareLogoUrl
}}
links {{
corporateWebsite
}}
}}
}}
recruit {{
viewJobUrl
detailedSalary
workSchedule
}}
}}
}}
}}
}}
"""

View File

@@ -1,29 +1,65 @@
from typing import Optional, Tuple
"""
jobspy.scrapers.linkedin
~~~~~~~~~~~~~~~~~~~
This module contains routines to scrape LinkedIn.
"""
from __future__ import annotations
import time
import random
import regex as re
from typing import Optional
from datetime import datetime
import requests
from bs4 import BeautifulSoup
from bs4.element import Tag
from bs4 import BeautifulSoup
from urllib.parse import urlparse, urlunparse, unquote
from .. import Scraper, ScraperInput, Site
from ..exceptions import LinkedInException
from ..utils import create_session
from ...jobs import (
JobPost,
Location,
JobResponse,
JobType,
Country,
Compensation,
CompensationInterval,
DescriptionFormat,
)
from ..utils import (
logger,
extract_emails_from_text,
get_enum_from_job_type,
currency_parser,
markdown_converter,
)
class LinkedInScraper(Scraper):
def __init__(self):
base_url = "https://www.linkedin.com"
delay = 3
band_delay = 4
jobs_per_page = 25
def __init__(self, proxies: list[str] | str | None = None):
"""
Initializes LinkedInScraper with the LinkedIn job search url
"""
site = Site(Site.LINKEDIN)
self.url = "https://www.linkedin.com"
super().__init__(site)
super().__init__(Site.LINKEDIN, proxies=proxies)
self.session = create_session(
proxies=self.proxies,
is_tls=False,
has_retry=True,
delay=5,
clear_cookies=True,
)
self.session.headers.update(self.headers)
self.scraper_input = None
self.country = "worldwide"
self.job_url_direct_regex = re.compile(r'(?<=\?url=)[^"]+')
def scrape(self, scraper_input: ScraperInput) -> JobResponse:
"""
@@ -31,189 +67,235 @@ class LinkedInScraper(Scraper):
:param scraper_input:
:return: job_response
"""
self.country = "worldwide"
self.scraper_input = scraper_input
job_list: list[JobPost] = []
seen_urls = set()
page, processed_jobs, job_count = 0, 0, 0
def job_type_code(job_type):
mapping = {
JobType.FULL_TIME: "F",
JobType.PART_TIME: "P",
JobType.INTERNSHIP: "I",
JobType.CONTRACT: "C",
JobType.TEMPORARY: "T",
}
return mapping.get(job_type, "")
with requests.Session() as session:
while len(job_list) < scraper_input.results_wanted:
params = {
"keywords": scraper_input.search_term,
"location": scraper_input.location,
"distance": scraper_input.distance,
"f_WT": 2 if scraper_input.is_remote else None,
"f_JT": job_type_code(scraper_input.job_type)
page = scraper_input.offset // 10 * 10 if scraper_input.offset else 0
request_count = 0
seconds_old = (
scraper_input.hours_old * 3600 if scraper_input.hours_old else None
)
continue_search = (
lambda: len(job_list) < scraper_input.results_wanted and page < 1000
)
while continue_search():
request_count += 1
logger.info(f"LinkedIn search page: {request_count}")
params = {
"keywords": scraper_input.search_term,
"location": scraper_input.location,
"distance": scraper_input.distance,
"f_WT": 2 if scraper_input.is_remote else None,
"f_JT": (
self.job_type_code(scraper_input.job_type)
if scraper_input.job_type
else None,
"pageNum": page,
"f_AL": "true" if scraper_input.easy_apply else None,
}
else None
),
"pageNum": 0,
"start": page,
"f_AL": "true" if scraper_input.easy_apply else None,
"f_C": (
",".join(map(str, scraper_input.linkedin_company_ids))
if scraper_input.linkedin_company_ids
else None
),
}
if seconds_old is not None:
params["f_TPR"] = f"r{seconds_old}"
params = {k: v for k, v in params.items() if v is not None}
response = session.get(
f"{self.url}/jobs/search", params=params, allow_redirects=True
params = {k: v for k, v in params.items() if v is not None}
try:
response = self.session.get(
f"{self.base_url}/jobs-guest/jobs/api/seeMoreJobPostings/search?",
params=params,
timeout=10,
)
if response.status_code != 200:
return JobResponse(
success=False,
error=f"Response returned {response.status_code}",
)
soup = BeautifulSoup(response.text, "html.parser")
if page == 0:
job_count_text = soup.find(
"span", class_="results-context-header__job-count"
).text
job_count = int("".join(filter(str.isdigit, job_count_text)))
for job_card in soup.find_all(
"div",
class_="base-card relative w-full hover:no-underline focus:no-underline base-card--link base-search-card base-search-card--link job-search-card",
):
processed_jobs += 1
data_entity_urn = job_card.get("data-entity-urn", "")
job_id = (
data_entity_urn.split(":")[-1] if data_entity_urn else "N/A"
)
job_url = f"{self.url}/jobs/view/{job_id}"
if job_url in seen_urls:
continue
seen_urls.add(job_url)
job_info = job_card.find("div", class_="base-search-card__info")
if job_info is None:
continue
title_tag = job_info.find("h3", class_="base-search-card__title")
title = title_tag.text.strip() if title_tag else "N/A"
company_tag = job_info.find("a", class_="hidden-nested-link")
company = company_tag.text.strip() if company_tag else "N/A"
metadata_card = job_info.find(
"div", class_="base-search-card__metadata"
)
location: Location = self.get_location(metadata_card)
datetime_tag = metadata_card.find(
"time", class_="job-search-card__listdate"
)
description, job_type = LinkedInScraper.get_description(job_url)
if datetime_tag:
datetime_str = datetime_tag["datetime"]
date_posted = datetime.strptime(datetime_str, "%Y-%m-%d")
if response.status_code not in range(200, 400):
if response.status_code == 429:
err = (
f"429 Response - Blocked by LinkedIn for too many requests"
)
else:
date_posted = None
err = f"LinkedIn response status code {response.status_code}"
err += f" - {response.text}"
logger.error(err)
return JobResponse(jobs=job_list)
except Exception as e:
if "Proxy responded with" in str(e):
logger.error(f"LinkedIn: Bad proxy")
else:
logger.error(f"LinkedIn: {str(e)}")
return JobResponse(jobs=job_list)
job_post = JobPost(
title=title,
description=description,
company_name=company,
location=location,
date_posted=date_posted,
job_url=job_url,
job_type=job_type,
compensation=Compensation(
interval=CompensationInterval.YEARLY, currency=None
),
)
job_list.append(job_post)
if (
len(job_list) >= scraper_input.results_wanted
or processed_jobs >= job_count
):
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)
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:
href = href_tag.attrs["href"].split("?")[0]
job_id = href.split("-")[-1]
job_url = f"{self.base_url}/jobs/view/{job_id}"
if job_url in seen_urls:
continue
seen_urls.add(job_url)
try:
fetch_desc = scraper_input.linkedin_fetch_description
job_post = self._process_job(job_card, job_url, fetch_desc)
if job_post:
job_list.append(job_post)
if not continue_search():
break
if (
len(job_list) >= scraper_input.results_wanted
or processed_jobs >= job_count
):
break
except Exception as e:
raise LinkedInException(str(e))
page += 1
if continue_search():
time.sleep(random.uniform(self.delay, self.delay + self.band_delay))
page += len(job_list)
job_list = job_list[: scraper_input.results_wanted]
job_response = JobResponse(
success=True,
jobs=job_list,
total_results=job_count,
)
return job_response
return JobResponse(jobs=job_list)
@staticmethod
def get_description(job_page_url: str) -> Optional[str]:
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")
location = self._get_location(metadata_card)
datetime_tag = (
metadata_card.find("time", class_="job-search-card__listdate")
if metadata_card
else None
)
date_posted = None
if datetime_tag and "datetime" in datetime_tag.attrs:
datetime_str = datetime_tag["datetime"]
try:
date_posted = datetime.strptime(datetime_str, "%Y-%m-%d")
except:
date_posted = None
job_details = {}
if full_descr:
job_details = self._get_job_details(job_url)
return JobPost(
id=self._get_id(job_url),
title=title,
company_name=company,
company_url=company_url,
location=location,
date_posted=date_posted,
job_url=job_url,
compensation=compensation,
job_type=job_details.get("job_type"),
description=job_details.get("description"),
job_url_direct=job_details.get("job_url_direct"),
emails=extract_emails_from_text(job_details.get("description")),
logo_photo_url=job_details.get("logo_photo_url"),
job_function=job_details.get("job_function"),
)
def _get_id(self, url: str):
"""
Retrieves job description by going to the job page url
Extracts the job id from the job url
:param url:
:return: str
"""
if not url:
return None
return url.split("/")[-1]
def _get_job_details(self, job_page_url: str) -> dict:
"""
Retrieves job description and other job details by going to the job page url
:param job_page_url:
:return: description or None
:return: dict
"""
response = requests.get(job_page_url, allow_redirects=True)
if response.status_code not in range(200, 400):
return None, None
try:
response = self.session.get(job_page_url, timeout=5)
response.raise_for_status()
except:
return {}
if "linkedin.com/signup" in response.url:
return {}
soup = BeautifulSoup(response.text, "html.parser")
div_content = soup.find(
"div", class_=lambda x: x and "show-more-less-html__markup" in x
)
description = None
if div_content is not None:
text_content = None
if div_content:
text_content = " ".join(div_content.get_text().split()).strip()
def remove_attributes(tag):
for attr in list(tag.attrs):
del tag[attr]
return tag
def get_job_type(
soup: BeautifulSoup,
) -> Tuple[Optional[str], Optional[JobType]]:
"""
Gets the job type from job page
:param soup:
:return: JobType
"""
h3_tag = soup.find(
"h3",
class_="description__job-criteria-subheader",
string=lambda text: "Employment type" in text,
div_content = remove_attributes(div_content)
description = div_content.prettify(formatter="html")
if self.scraper_input.description_format == DescriptionFormat.MARKDOWN:
description = markdown_converter(description)
h3_tag = soup.find(
"h3", text=lambda text: text and "Job function" in text.strip()
)
job_function = None
if h3_tag:
job_function_span = h3_tag.find_next(
"span", class_="description__job-criteria-text"
)
if job_function_span:
job_function = job_function_span.text.strip()
return {
"description": description,
"job_type": self._parse_job_type(soup),
"job_url_direct": self._parse_job_url_direct(soup),
"logo_photo_url": soup.find("img", {"class": "artdeco-entity-image"}).get(
"data-delayed-url"
),
"job_function": job_function,
}
employment_type = None
if h3_tag:
employment_type_span = h3_tag.find_next_sibling(
"span",
class_="description__job-criteria-text description__job-criteria-text--criteria",
)
if employment_type_span:
employment_type = employment_type_span.get_text(strip=True)
employment_type = employment_type.lower()
employment_type = employment_type.replace("-", "")
return LinkedInScraper.get_enum_from_value(employment_type)
return text_content, get_job_type(soup)
@staticmethod
def get_enum_from_value(value_str):
for job_type in JobType:
if value_str in job_type.value:
return job_type
return None
def get_location(self, metadata_card: Optional[Tag]) -> Location:
def _get_location(self, metadata_card: Optional[Tag]) -> Location:
"""
Extracts the location data from the job metadata card.
: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"
@@ -225,7 +307,71 @@ 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
country = Country.from_string(country)
location = Location(city=city, state=state, country=country)
return location
@staticmethod
def _parse_job_type(soup_job_type: BeautifulSoup) -> list[JobType] | None:
"""
Gets the job type from job page
:param soup_job_type:
:return: JobType
"""
h3_tag = soup_job_type.find(
"h3",
class_="description__job-criteria-subheader",
string=lambda text: "Employment type" in text,
)
employment_type = None
if h3_tag:
employment_type_span = h3_tag.find_next_sibling(
"span",
class_="description__job-criteria-text description__job-criteria-text--criteria",
)
if employment_type_span:
employment_type = employment_type_span.get_text(strip=True)
employment_type = employment_type.lower()
employment_type = employment_type.replace("-", "")
return [get_enum_from_job_type(employment_type)] if employment_type else []
def _parse_job_url_direct(self, soup: BeautifulSoup) -> str | None:
"""
Gets the job url direct from job page
:param soup:
:return: str
"""
job_url_direct = None
job_url_direct_content = soup.find("code", id="applyUrl")
if job_url_direct_content:
job_url_direct_match = self.job_url_direct_regex.search(
job_url_direct_content.decode_contents().strip()
)
if job_url_direct_match:
job_url_direct = unquote(job_url_direct_match.group())
return job_url_direct
@staticmethod
def job_type_code(job_type_enum: JobType) -> str:
return {
JobType.FULL_TIME: "F",
JobType.PART_TIME: "P",
JobType.INTERNSHIP: "I",
JobType.CONTRACT: "C",
JobType.TEMPORARY: "T",
}.get(job_type_enum, "")
headers = {
"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",
"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

@@ -0,0 +1,180 @@
from __future__ import annotations
import re
import logging
from itertools import cycle
import requests
import tls_client
import numpy as np
from markdownify import markdownify as md
from requests.adapters import HTTPAdapter, Retry
from ..jobs import JobType
logger = logging.getLogger("JobSpy")
logger.propagate = False
if not logger.handlers:
logger.setLevel(logging.INFO)
console_handler = logging.StreamHandler()
format = "%(asctime)s - %(name)s - %(levelname)s - %(message)s"
formatter = logging.Formatter(format)
console_handler.setFormatter(formatter)
logger.addHandler(console_handler)
class RotatingProxySession:
def __init__(self, proxies=None):
if isinstance(proxies, str):
self.proxy_cycle = cycle([self.format_proxy(proxies)])
elif isinstance(proxies, list):
self.proxy_cycle = (
cycle([self.format_proxy(proxy) for proxy in proxies])
if proxies
else None
)
else:
self.proxy_cycle = None
@staticmethod
def format_proxy(proxy):
"""Utility method to format a proxy string into a dictionary."""
if proxy.startswith("http://") or proxy.startswith("https://"):
return {"http": proxy, "https": proxy}
return {"http": f"http://{proxy}", "https": f"http://{proxy}"}
class RequestsRotating(RotatingProxySession, requests.Session):
def __init__(self, proxies=None, has_retry=False, delay=1, clear_cookies=False):
RotatingProxySession.__init__(self, proxies=proxies)
requests.Session.__init__(self)
self.clear_cookies = clear_cookies
self.allow_redirects = True
self.setup_session(has_retry, delay)
def setup_session(self, has_retry, delay):
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)
self.mount("http://", adapter)
self.mount("https://", adapter)
def request(self, method, url, **kwargs):
if self.clear_cookies:
self.cookies.clear()
if self.proxy_cycle:
next_proxy = next(self.proxy_cycle)
if next_proxy["http"] != "http://localhost":
self.proxies = next_proxy
else:
self.proxies = {}
return requests.Session.request(self, method, url, **kwargs)
class TLSRotating(RotatingProxySession, tls_client.Session):
def __init__(self, proxies=None):
RotatingProxySession.__init__(self, proxies=proxies)
tls_client.Session.__init__(self, random_tls_extension_order=True)
def execute_request(self, *args, **kwargs):
if self.proxy_cycle:
next_proxy = next(self.proxy_cycle)
if next_proxy["http"] != "http://localhost":
self.proxies = next_proxy
else:
self.proxies = {}
response = tls_client.Session.execute_request(self, *args, **kwargs)
return response
def create_session(
*,
proxies: dict | str | None = None,
is_tls: bool = True,
has_retry: bool = False,
delay: int = 1,
clear_cookies: bool = False,
) -> requests.Session:
"""
Creates a requests session with optional tls, proxy, and retry settings.
:return: A session object
"""
if is_tls:
session = TLSRotating(proxies=proxies)
else:
session = RequestsRotating(
proxies=proxies,
has_retry=has_retry,
delay=delay,
clear_cookies=clear_cookies,
)
return session
def set_logger_level(verbose: int = 2):
"""
Adjusts the logger's level. This function allows the logging level to be changed at runtime.
Parameters:
- verbose: int {0, 1, 2} (default=2, all logs)
"""
if verbose is None:
return
level_name = {2: "INFO", 1: "WARNING", 0: "ERROR"}.get(verbose, "INFO")
level = getattr(logging, level_name.upper(), None)
if level is not None:
logger.setLevel(level)
else:
raise ValueError(f"Invalid log level: {level_name}")
def markdown_converter(description_html: str):
if description_html is None:
return None
markdown = md(description_html)
return markdown.strip()
def extract_emails_from_text(text: str) -> list[str] | None:
if not text:
return None
email_regex = re.compile(r"[a-zA-Z0-9._%+-]+@[a-zA-Z0-9.-]+\.[a-zA-Z]{2,}")
return email_regex.findall(text)
def get_enum_from_job_type(job_type_str: str) -> JobType | None:
"""
Given a string, returns the corresponding JobType enum member if a match is found.
"""
res = None
for job_type in JobType:
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

@@ -1,425 +1,213 @@
"""
jobspy.scrapers.ziprecruiter
~~~~~~~~~~~~~~~~~~~
This module contains routines to scrape ZipRecruiter.
"""
from __future__ import annotations
import math
import json
import re
import traceback
import time
from datetime import datetime
from typing import Optional, Tuple
from urllib.parse import urlparse, parse_qs
from typing import Optional, Tuple, Any
import tls_client
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, StatusException
from .. import Scraper, ScraperInput, Site
from ..utils import (
logger,
extract_emails_from_text,
create_session,
markdown_converter,
)
from ...jobs import (
JobPost,
Compensation,
CompensationInterval,
Location,
JobResponse,
JobType,
Country,
DescriptionFormat,
)
class ZipRecruiterScraper(Scraper):
def __init__(self):
"""
Initializes LinkedInScraper with the ZipRecruiter job search url
"""
site = Site(Site.ZIP_RECRUITER)
self.url = "https://www.ziprecruiter.com"
super().__init__(site)
base_url = "https://www.ziprecruiter.com"
api_url = "https://api.ziprecruiter.com"
def __init__(self, proxies: list[str] | str | None = None):
"""
Initializes ZipRecruiterScraper with the ZipRecruiter job search url
"""
super().__init__(Site.ZIP_RECRUITER, proxies=proxies)
self.scraper_input = None
self.session = create_session(proxies=proxies)
self._get_cookies()
self.delay = 5
self.jobs_per_page = 20
self.seen_urls = set()
self.session = tls_client.Session(
client_identifier="chrome112", random_tls_extension_order=True
)
def scrape_page(
self, scraper_input: ScraperInput, page: int
) -> tuple[list[JobPost], int | None]:
"""
Scrapes a page of ZipRecruiter for jobs with scraper_input criteria
:param scraper_input:
:param page:
:param session:
:return: jobs found on page, total number of jobs found for search
"""
job_list = []
job_type_value = None
if scraper_input.job_type:
if scraper_input.job_type.value == "fulltime":
job_type_value = "full_time"
elif scraper_input.job_type.value == "parttime":
job_type_value = "part_time"
else:
job_type_value = scraper_input.job_type.value
params = {
"search": scraper_input.search_term,
"location": scraper_input.location,
"page": page,
"form": "jobs-landing",
}
if scraper_input.is_remote:
params["refine_by_location_type"] = "only_remote"
if scraper_input.distance:
params["radius"] = scraper_input.distance
if job_type_value:
params[
"refine_by_employment"
] = f"employment_type:employment_type:{job_type_value}"
response = self.session.get(
self.url + "/jobs-search",
headers=ZipRecruiterScraper.headers(),
params=params,
allow_redirects=True,
)
# print(response.status_code)
if response.status_code != 200:
raise StatusException(response.status_code)
html_string = response.text
soup = BeautifulSoup(html_string, "html.parser")
script_tag = soup.find("script", {"id": "js_variables"})
data = json.loads(script_tag.string)
if page == 1:
job_count = int(data["totalJobCount"].replace(",", ""))
else:
job_count = None
with ThreadPoolExecutor(max_workers=10) as executor:
if "jobList" in data and data["jobList"]:
jobs_js = data["jobList"]
job_results = [
executor.submit(self.process_job_js, job) for job in jobs_js
]
else:
jobs_html = soup.find_all("div", {"class": "job_content"})
job_results = [
executor.submit(self.process_job_html, job) for job in jobs_html
]
job_list = [result.result() for result in job_results if result.result()]
return job_list, job_count
def scrape(self, scraper_input: ScraperInput) -> JobResponse:
"""
Scrapes ZipRecruiter for jobs with scraper_input criteria
:param scraper_input:
:return: job_response
Scrapes ZipRecruiter for jobs with scraper_input criteria.
:param scraper_input: Information about job search criteria.
:return: JobResponse containing a list of jobs.
"""
self.scraper_input = scraper_input
job_list: list[JobPost] = []
continue_token = None
pages_to_process = max(
3, math.ceil(scraper_input.results_wanted / self.jobs_per_page)
)
try:
#: get first page to initialize session
job_list, total_results = self.scrape_page(scraper_input, 1)
with ThreadPoolExecutor(max_workers=10) as executor:
futures: list[Future] = [
executor.submit(self.scrape_page, scraper_input, page)
for page in range(2, pages_to_process + 1)
]
for future in futures:
jobs, _ = future.result()
job_list += jobs
except StatusException as e:
return JobResponse(
success=False,
error=f"ZipRecruiter returned status code {e.status_code}",
max_pages = math.ceil(scraper_input.results_wanted / self.jobs_per_page)
for page in range(1, max_pages + 1):
if len(job_list) >= scraper_input.results_wanted:
break
if page > 1:
time.sleep(self.delay)
logger.info(f"ZipRecruiter search page: {page}")
jobs_on_page, continue_token = self._find_jobs_in_page(
scraper_input, continue_token
)
except Exception as e:
print(f"ZipRecruiter failed to scrape: {e}\n{traceback.format_exc()}")
return JobResponse(
success=False,
error=f"ZipRecruiter failed to scrape: {e}",
)
#: note: this does not handle if the results are more or less than the results_wanted
if len(job_list) > scraper_input.results_wanted:
job_list = job_list[: scraper_input.results_wanted]
job_response = JobResponse(
success=True,
jobs=job_list,
total_results=total_results,
)
return job_response
def process_job_html(self, job: Tag) -> Optional[JobPost]:
"""
Parses a job from the job content tag
:param job: BeautifulSoup Tag for one job post
:return JobPost
"""
job_url = job.find("a", {"class": "job_link"})["href"]
if job_url in self.seen_urls:
return None
title = job.find("h2", {"class": "title"}).text
company = job.find("a", {"class": "company_name"}).text.strip()
description, updated_job_url = self.get_description(job_url)
if updated_job_url is not None:
job_url = updated_job_url
if description is None:
description = job.find("p", {"class": "job_snippet"}).text.strip()
job_type_element = job.find("li", {"class": "perk_item perk_type"})
job_type = None
if job_type_element:
job_type_text = (
job_type_element.text.strip().lower().replace("-", "").replace(" ", "")
)
job_type = ZipRecruiterScraper.get_job_type_enum(job_type_text)
date_posted = ZipRecruiterScraper.get_date_posted(job)
job_post = JobPost(
title=title,
description=description,
company_name=company,
location=ZipRecruiterScraper.get_location(job),
job_type=job_type,
compensation=ZipRecruiterScraper.get_compensation(job),
date_posted=date_posted,
job_url=job_url,
)
return job_post
def process_job_js(self, job: dict) -> JobPost:
title = job.get("Title")
description = BeautifulSoup(
job.get("Snippet", "").strip(), "html.parser"
).get_text()
company = job.get("OrgName")
location = Location(
city=job.get("City"), state=job.get("State"), country=Country.US_CANADA
)
try:
job_type = ZipRecruiterScraper.get_job_type_enum(
job.get("EmploymentType", "").replace("-", "_").lower()
)
except ValueError:
# print(f"Skipping job due to unrecognized job type: {job.get('EmploymentType')}")
return None
formatted_salary = job.get("FormattedSalaryShort", "")
salary_parts = formatted_salary.split(" ")
min_salary_str = salary_parts[0][1:].replace(",", "")
if "." in min_salary_str:
min_amount = int(float(min_salary_str) * 1000)
else:
min_amount = int(min_salary_str.replace("K", "000"))
if len(salary_parts) >= 3 and salary_parts[2].startswith("$"):
max_salary_str = salary_parts[2][1:].replace(",", "")
if "." in max_salary_str:
max_amount = int(float(max_salary_str) * 1000)
if jobs_on_page:
job_list.extend(jobs_on_page)
else:
max_amount = int(max_salary_str.replace("K", "000"))
else:
max_amount = 0
break
if not continue_token:
break
return JobResponse(jobs=job_list[: scraper_input.results_wanted])
compensation = Compensation(
interval=CompensationInterval.YEARLY,
min_amount=min_amount,
max_amount=max_amount,
currency="USD/CAD",
)
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()
job_url = job.get("JobURL")
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
"""
jobs_list = []
params = self._add_params(scraper_input)
if continue_token:
params["continue_from"] = continue_token
try:
res = self.session.get(
f"{self.api_url}/jobs-app/jobs", headers=self.headers, params=params
)
if res.status_code not in range(200, 400):
if res.status_code == 429:
err = "429 Response - Blocked by ZipRecruiter for too many requests"
else:
err = f"ZipRecruiter response status code {res.status_code}"
err += f" with response: {res.text}" # ZipRecruiter likely not available in EU
logger.error(err)
return jobs_list, ""
except Exception as e:
if "Proxy responded with" in str(e):
logger.error(f"Indeed: Bad proxy")
else:
logger.error(f"Indeed: {str(e)}")
return jobs_list, ""
res_data = res.json()
jobs_list = res_data.get("jobs", [])
next_continue_token = res_data.get("continue", None)
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 = list(filter(None, (result.result() for result in job_results)))
return job_list, next_continue_token
def _process_job(self, job: dict) -> JobPost | None:
"""
Processes an individual job dict from the response
"""
title = job.get("name")
job_url = f"{self.base_url}/jobs//j?lvk={job['listing_key']}"
if job_url in self.seen_urls:
return
self.seen_urls.add(job_url)
description = job.get("job_description", "").strip()
description = (
markdown_converter(description)
if self.scraper_input.description_format == DescriptionFormat.MARKDOWN
else description
)
company = job.get("hiring_company", {}).get("name")
country_value = "usa" if job.get("job_country") == "US" else "canada"
country_enum = Country.from_string(country_value)
location = Location(
city=job.get("job_city"), state=job.get("job_state"), country=country_enum
)
job_type = self._get_job_type_enum(
job.get("employment_type", "").replace("_", "").lower()
)
date_posted = datetime.fromisoformat(job["posted_time"].rstrip("Z")).date()
comp_interval = job.get("compensation_interval")
comp_interval = "yearly" if comp_interval == "annual" else comp_interval
comp_min = int(job["compensation_min"]) if "compensation_min" in job else None
comp_max = int(job["compensation_max"]) if "compensation_max" in job else None
comp_currency = job.get("compensation_currency")
return JobPost(
id=str(job["listing_key"]),
title=title,
description=description,
company_name=company,
location=location,
job_type=job_type,
compensation=compensation,
compensation=Compensation(
interval=comp_interval,
min_amount=comp_min,
max_amount=comp_max,
currency=comp_currency,
),
date_posted=date_posted,
job_url=job_url,
description=description,
emails=extract_emails_from_text(description) if description else None,
)
return job_post
def _get_cookies(self):
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"
url = f"{self.api_url}/jobs-app/event"
self.session.post(url, data=data, headers=self.headers)
@staticmethod
def get_enum_from_value(value_str):
for job_type in JobType:
if value_str in job_type.value:
return job_type
return None
@staticmethod
def get_job_type_enum(job_type_str: str) -> Optional[JobType]:
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
return None
def get_description(self, job_page_url: str) -> Tuple[Optional[str], Optional[str]]:
"""
Retrieves job description by going to the job page url
:param job_page_url:
:param session:
:return: description or None, response url
"""
try:
response = self.session.get(
job_page_url,
headers=ZipRecruiterScraper.headers(),
allow_redirects=True,
timeout_seconds=5,
)
except requests.exceptions.Timeout:
print("The request timed out.")
return None
html_string = response.content
soup_job = BeautifulSoup(html_string, "html.parser")
job_description_div = soup_job.find("div", {"class": "job_description"})
if job_description_div:
return job_description_div.text.strip(), response.url
return None, response.url
@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: BeautifulSoup) -> 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 [job_type]
return None
@staticmethod
def get_compensation(job: BeautifulSoup) -> 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: BeautifulSoup) -> 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:
"""
Returns headers needed for requests
:return: dict - Dictionary containing headers
"""
return {
"User-Agent": "Mozilla/5.0 (Macintosh; Intel Mac OS X 10_14_6) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/78.0.3904.97 Safari/537.36"
def _add_params(scraper_input) -> dict[str, str | Any]:
params = {
"search": scraper_input.search_term,
"location": scraper_input.location,
}
if scraper_input.hours_old:
params["days"] = max(scraper_input.hours_old // 24, 1)
job_type_map = {JobType.FULL_TIME: "full_time", JobType.PART_TIME: "part_time"}
if scraper_input.job_type:
job_type = scraper_input.job_type
params["employment_type"] = job_type_map.get(job_type, job_type.value[0])
if scraper_input.easy_apply:
params["zipapply"] = 1
if scraper_input.is_remote:
params["remote"] = 1
if scraper_input.distance:
params["radius"] = scraper_input.distance
return {k: v for k, v in params.items() if v is not None}
headers = {
"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",
}

14
src/tests/test_all.py Normal file
View File

@@ -0,0 +1,14 @@
from ..jobspy import scrape_jobs
import pandas as pd
def test_all():
result = scrape_jobs(
site_name=["linkedin", "indeed", "zip_recruiter", "glassdoor"],
search_term="software engineer",
results_wanted=5,
)
assert (
isinstance(result, pd.DataFrame) and not result.empty
), "Result should be a non-empty DataFrame"

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

@@ -1,9 +1,11 @@
from ..jobspy import scrape_jobs
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 result is not None
assert (
isinstance(result, pd.DataFrame) and not result.empty
), "Result should be a non-empty DataFrame"

View File

@@ -1,4 +1,5 @@
from jobspy import scrape_jobs
from ..jobspy import scrape_jobs
import pandas as pd
def test_linkedin():
@@ -6,4 +7,6 @@ def test_linkedin():
site_name="linkedin",
search_term="software engineer",
)
assert result is not None
assert (
isinstance(result, pd.DataFrame) and not result.empty
), "Result should be a non-empty DataFrame"

View File

@@ -1,4 +1,5 @@
from jobspy import scrape_jobs
from ..jobspy import scrape_jobs
import pandas as pd
def test_ziprecruiter():
@@ -7,4 +8,6 @@ def test_ziprecruiter():
search_term="software engineer",
)
assert result is not None
assert (
isinstance(result, pd.DataFrame) and not result.empty
), "Result should be a non-empty DataFrame"