add long scrape example (#81)

pull/82/head
Harish Vadaparty 2024-01-12 23:54:00 +05:30 committed by GitHub
parent a7ad616567
commit 89a5264391
No known key found for this signature in database
GPG Key ID: 4AEE18F83AFDEB23
2 changed files with 80 additions and 4 deletions

View File

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

View File

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