JobSpy/examples/JobSpy_Demo.py

46 lines
1.6 KiB
Python

import json
import os
from jobspy import scrape_jobs
import pandas as pd
# load location list
def read_location_list(location_file):
with open(location_file) as f:
location_list = [location['name'] for location in json.load(f)]
return location_list
# formatting for pandas
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
# fetch jobs for each location
# locations = read_location_list('location_seed.json')
# for location in locations:
# try:
# jobs: pd.DataFrame = scrape_jobs(
# # site_name=["indeed", "linkedin", "zip_recruiter"],
# site_name=["indeed"],
# search_term="software engineer",
# location=location,
# results_wanted=30,
# # be wary the higher it is, the more likey you'll get blocked (rotating proxy should work tho)
# country_indeed='USA',
# # offset=25 # start jobs from an offset (use if search failed and want to continue)
# proxy="http://34.120.172.140:8123",
# # proxy="http://crawler-gost-proxy.jobright-internal.com:8080",
# )
# except Exception as e:
# print(f'Error when process: {location}')
# print(e)
# continue
# print(f'{location}: {jobs.shape[0]} rows append.')
# if os.path.isfile('./jobs.csv'):
# jobs.to_csv('./jobs.csv', index=False, mode='a', header=False)
# else:
# jobs.to_csv('./jobs.csv', index=False, mode='a', header=True)