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https://github.com/oxylabs/google-jobs-listings-python
Follow this step-by-step tutorial and learn how to build your own Google Jobs scraper that simultaneously scrapes Google Jobs for multiple search queries and geo-locations with Python and Oxylabs’ Google Jobs Scraper API.
https://github.com/oxylabs/google-jobs-listings-python
google google-jobs google-jobs-listings google-scraper google-scraping scrape-google
Last synced: 3 days ago
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Follow this step-by-step tutorial and learn how to build your own Google Jobs scraper that simultaneously scrapes Google Jobs for multiple search queries and geo-locations with Python and Oxylabs’ Google Jobs Scraper API.
- Host: GitHub
- URL: https://github.com/oxylabs/google-jobs-listings-python
- Owner: oxylabs
- Created: 2024-08-21T09:25:12.000Z (3 months ago)
- Default Branch: main
- Last Pushed: 2024-08-21T10:02:48.000Z (3 months ago)
- Last Synced: 2024-08-21T12:39:03.750Z (3 months ago)
- Topics: google, google-jobs, google-jobs-listings, google-scraper, google-scraping, scrape-google
- Homepage:
- Size: 7.81 KB
- Stars: 0
- Watchers: 2
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# How to Scrape Google Jobs Listings With Python
Follow this step-by-step tutorial and learn how to build your own Google Jobs scraper that simultaneously scrapes Google Jobs for multiple search queries and geo-locations with Python and Oxylabs’ Google Jobs Scraper API.
Learn how to build your own Google Jobs scraper that simultaneously scrapes Google Jobs for multiple search queries and geo-locations with Python and Oxylabs’ Google Jobs Scraper API.
To follow along, you'll need an active Oxylabs subscription. You may get a paid plan or a free trial via our self-service dashboard [here](https://dashboard.oxylabs.io/).
In this guide, let’s scrape Google Jobs results asynchronously and extract the following publicly available data:
- Job title
- Company name
- Job location
- Job posted via [platform]
- Job listing date
- Salary
## Send a request
### Install Python
If you don’t have Python installed yet, you can download it from the official Python [website](https://www.python.org/downloads/). This tutorial is written with Python 3.12.0, so ensure that you have a compatible version.
### Send a request for testing
After creating an API user, copy and save your API user credentials, which you’ll use for authentication. Next, open your terminal and install the requests library:
```pip install requests```
Then run the following code that scrapes Google Jobs results and retrieves the entire HTML file:
```
import requests
payload = {
"source": "google",
"url": "https://www.google.com/search?q=developer&ibp=htl;jobs&hl=en&gl=us",
"render": "html"
}
response = requests.post(
"https://realtime.oxylabs.io/v1/queries",
auth=("USERNAME", "PASSWORD"), # Replace with your API user credentials
json=payload
)
print(response.json())
print(response.status_code)
```
Once it finishes running, you should see a JSON response with HTML results and a status code of your request. If everything works correctly, the status code should be `200`.
Now, let's dive into the fun part – building your very own asynchronous Google Jobs scraper.
## Install and import libraries
For this project, let’s use the `asyncio` and `aiohttp` libraries to make asynchronous requests to the API. Additionally, the `json` and pandas libraries will help you deal with JSON and CSV files.
Open your terminal and run the following command to install the necessary libraries:
```pip install asyncio aiohttp pandas```
Then, import them into your Python file:
```
import asyncio, aiohttp, json, pandas as pd
from aiohttp import ClientSession, BasicAuth
```
### Add your API user credentials
Create the API user `credentials` variable and use `BasicAuth`, as aiohttp requires this for authentication:
```
credentials = BasicAuth("USERNAME", "PASSWORD") # Replace with your API user credentials
```
### Set up queries and locations
You can easily form Google Jobs URLs for different queries by manipulating the `q=` parameter:
```
https://www.google.com/search?q=developer&ibp=htl;jobs&hl=en&gl=us
```
This enables you to scrape job listings for as many search queries as you want. It's a good idea to visit the URLs with specific parameters using a VPN that's located in your desired country. This way, you can ensure that the URL works for that location, as Google may use different URL-forming techniques and present different SERPs that are incompatible with the CSS and XPath selectors used in this tutorial.
Note that the `q=`, `ibp=htl;jobs`, `hl=`, and `gl=` parameters are mandatory for the URL to work.
Additionally, you could set the UULE parameter for [geo-location](https://developers.oxylabs.io/scraper-apis/serp-scraper-api/features/geo-location) targeting yourself, but that’s unnecessary since the geo_location parameter of Google Jobs Scraper API does that by default.
### URL parameters
Create the `URL_parameters` list to store your search queries:
```URL_parameters = ["developer", "chef", "manager"]```
### Locations
Then, create the `locations` dictionary where the key refers to the country, and the value is a list of geo-location parameters. This dictionary will be used to dynamically form the API payload and localize Google Jobs results for the specified location. The two-letter country code will be used to modify the `gl=` parameter in the Google Jobs URL:
```
locations = {
"US": ["California,United States", "Virginia,United States", "New York,United States"],
"GB": ["United Kingdom"],
"JP": ["Japan"]
}
```
Visit our [documentation](https://developers.oxylabs.io/scraper-apis/serp-scraper-api/google#geo_location) for more details about geo-locations.
## Prepare the API payload with parsing instructions
Google Jobs Scraper API takes web scraping instructions from a `payload` dictionary, making it the most important configuration to fine-tune. The `url` and `geo_location` keys are set to `None`, as the scraper will pass these values dynamically for each search query and location. The `"render": "html"` parameter enables JavaScript rendering and returns the rendered HTML file:
```
payload = {
"source": "google",
"url": None,
"geo_location": None,
"user_agent_type": "desktop",
"render": "html"
}
```
Next, use Custom Parser to define your own parsing logic with `xPath` or `CSS` selectors and retrieve only the data you need. Remember that you can create as many functions as you want and extract even more data points than shown in this guide. Head to this Google Jobs URL in your browser and open Developer Tools by pressing Ctrl+Shift+I (Windows) or Option + Command + I (macOS). Use Ctrl+F or Command+F to open a search bar and test selector expressions.
As mentioned previously, the job listings are within the `
- ` tag.
As there is more than one `
- ` list on the Google Jobs page, you can form an `xPath` selector by specifying the `div` element that contains the targeted list:
```//div[@class='nJXhWc']//ul/li```
You can use this selector to specify the location of all job listings in the HTML file. In the payload dictionary, set the parse key to `True` and create the `parsing_instructions` parameter with the jobs function:
```
payload = {
"source": "google",
"url": None,
"geo_location": None,
"user_agent_type": "desktop",
"render": "html",
"parse": True,
"parsing_instructions": {
"jobs": {
"_fns": [
{
"_fn": "xpath",
"_args": ["//div[@class='nJXhWc']//ul/li"]
}
],
}
}
}
```
Next, create the `_items` iterator that will loop over the `jobs` list and extract details for each listing:
```
payload = {
"source": "google",
"url": None,
"geo_location": None,
"user_agent_type": "desktop",
"render": "html",
"parse": True,
"parsing_instructions": {
"jobs": {
"_fns": [
{
"_fn": "xpath", # You can use CSS or xPath
"_args": ["//div[@class='nJXhWc']//ul/li"]
}
],
"_items": {
"data_point_1": {
"_fns": [
{
"_fn": "selector_type", # You can use CSS or xPath
"_args": ["selector"]
}
]
},
"data_point_2": {
"_fns": [
{
"_fn": "selector_type",
"_args": ["selector"]
}
]
},
}
}
}
}
```
For each data point, you can create a separate function within the `_items` iterator. Let’s see how xPath selectors should look like for each Google Jobs data point:
### Job title
```.//div[@class='BjJfJf PUpOsf']/text()```
### Company name
```.//div[@class='vNEEBe']/text()```
### Location
```.//div[@class='Qk80Jf'][1]/text()```
### Date
```.//div[@class='PuiEXc']//span[@class='LL4CDc' and contains(@aria-label, 'Posted')]/span/text()```
### Salary
```.//div[@class='PuiEXc']//div[@class='I2Cbhb bSuYSc']//span[@aria-hidden='true']/text()```
### Job posted via
```.//div[@class='Qk80Jf'][2]/text()```
### URL
```.//div[@data-share-url]/@data-share-url```
Please be aware that you can only access this job listing URL in your browser with an IP address from the same country used during web scraping. If you’ve used a United States proxy, make sure to use a US IP address in your browser.
In the end, you should have a `payload` that looks like shown below. Save it to a separate JSON file and ensure that the `None` and `True` parameter values are converted to respective JSON values: `null` and `true`:
```
import json
payload = {
"source": "google",
"url": None,
"geo_location": None,
"user_agent_type": "desktop",
"render": "html",
"parse": True,
"parsing_instructions": {
"jobs": {
"_fns": [
{
"_fn": "xpath",
"_args": ["//div[@class='nJXhWc']//ul/li"]
}
],
"_items": {
"job_title": {
"_fns": [
{
"_fn": "xpath_one",
"_args": [".//div[@class='BjJfJf PUpOsf']/text()"]
}
]
},
"company_name": {
"_fns": [
{
"_fn": "xpath_one",
"_args": [".//div[@class='vNEEBe']/text()"]
}
]
},
"location": {
"_fns": [
{
"_fn": "xpath_one",
"_args": [".//div[@class='Qk80Jf'][1]/text()"]
}
]
},
"date": {
"_fns": [
{
"_fn": "xpath_one",
"_args": [".//div[@class='PuiEXc']//span[@class='LL4CDc' and contains(@aria-label, 'Posted')]/span/text()"]
}
]
},
"salary": {
"_fns": [
{
"_fn": "xpath_one",
"_args": [".//div[@class='PuiEXc']//div[@class='I2Cbhb bSuYSc']//span[@aria-hidden='true']/text()"]
}
]
},
"posted_via": {
"_fns": [
{
"_fn": "xpath_one",
"_args": [".//div[@class='Qk80Jf'][2]/text()"]
}
]
},
"URL": {
"_fns": [
{
"_fn": "xpath_one",
"_args": [".//div[@data-share-url]/@data-share-url"]
}
]
}
}
}
}
}
with open("payload.json", "w") as f:
json.dump(payload, f, indent=4)
```
This allows you to import the payload and make the scraper code much shorter:
```
payload = {}
with open("payload.json", "r") as f:
payload = json.load(f)
```
## Define functions
There are several ways you can [integrate](https://developers.oxylabs.io/scraper-apis/serp-scraper-api/integration-methods) Oxylabs Scraper APIs, namely Realtime, [Push-Pull](https://developers.oxylabs.io/scraper-apis/serp-scraper-api/integration-methods/push-pull), and Proxy endpoint. For this guide, let’s use Push-Pull, as you won’t have to keep your connection open after submitting a scraping job to the API. The API endpoint to use in this scenario is `https://data.oxylabs.io/v1/queries`.
You could also use another endpoint to submit batches of up to 5,000 URLs or queries. Keep in mind that making this choice will require you to modify the code shown in this tutorial. Read up about batch queries in our [documentation](https://developers.oxylabs.io/scraper-apis/serp-scraper-api/integration-methods/push-pull-batch).
## Submit job
Define an `async` function called `submit_job` and pass the `session: ClientSession` together with the `payload` to submit a web scraping job to the Oxylabs API using the `POST` method. This will return the ID number of the submitted job:
```
async def submit_job(session: ClientSession, payload):
async with session.post(
"https://data.oxylabs.io/v1/queries",
auth=credentials,
json=payload
) as response:
return (await response.json())["id"]
```
### Check job status
Then, create another `async` function that passes the `job_id` (this will be defined later) and returns the `status` of the scraping job from the response:
```
async def check_job_status(session: ClientSession, job_id):
async with session.get(f"https://data.oxylabs.io/v1/queries/{job_id}", auth=credentials) as response:
return (await response.json())["status"]
```
### Get job results
Next, create an `async` function that retrieves the scraped and parsed `jobs` results. Note that the response is a JSON string that contains the API job details and the scraped content that you can access by parsing nested JSON properties:
```
async def get_job_results(session: ClientSession, job_id):
async with session.get(f"https://data.oxylabs.io/v1/queries/{job_id}/results", auth=credentials) as response:
return (await response.json())["results"][0]["content"]["jobs"]
```
### Save data to a CSV file
Define another `async` function that saves the scraped and parsed data to a CSV file. Later on, we’ll create the four parameters that are passed to the function. As the pandas library is synchronous, you must use `asyncio.to_thread()` to run the `df.to_csv` asynchronously in a separate thread:
```
async def save_to_csv(job_id, query, location, results):
print(f"Saving data for {job_id}")
data = []
for job in results:
data.append({
"Job title": job["job_title"],
"Company name": job["company_name"],
"Location": job["location"],
"Date": job["date"],
"Salary": job["salary"],
"Posted via": job["posted_via"],
"URL": job["URL"]
})
df = pd.DataFrame(data)
filename = f"{query}_jobs_{location.replace(',', '_').replace(' ', '_')}.csv"
await asyncio.to_thread(df.to_csv, filename, index=False)
```
### Scrape Google Jobs
Make another async function that passes parameters to form the Google Jobs URL and the payload dynamically. Create a variable `job_id` and then call the submit_job function to submit the request to the API and create a while True loop by calling the `check_job_status` function to keep checking whether the API has finished web scraping. At the end, initiate the `get_job_results` and `save_to_csv` functions:
```
async def scrape_jobs(session: ClientSession, query, country_code, location):
URL = f"https://www.google.com/search?q={query}&ibp=htl;jobs&hl=en&gl={country_code}"
payload["url"] = URL
payload["geo_location"] = location
job_id = await submit_job(session, payload)
await asyncio.sleep(15)
print(f"Checking status for {job_id}")
while True:
status = await check_job_status(session, job_id)
if status == "done":
print(f"Job {job_id} done. Retrieving {query} jobs in {location}.")
break
elif status == "failed":
print(f"Job {job_id} encountered an issue. Status: {status}")
return
await asyncio.sleep(5)
results = await get_job_results(session, job_id)
await save_to_csv(job_id, query, location, results)
```
## Create the main() function
You’ve written most of the code, what’s left is to pull everything together by defining an async function called `main()` that creates an `aiohttp` session. It makes a list of tasks to scrape jobs for each combination of location and query and executes each task concurrently using `asyncio.gather()`.
```
async def main():
async with aiohttp.ClientSession() as session:
tasks = []
for country_code, location_list in locations.items():
for location in location_list:
for query in URL_parameters:
task = asyncio.ensure_future(scrape_jobs(session, query, country_code, location))
tasks.append(task)
await asyncio.gather(*tasks)
```
Lastly, initialize the event loop and call the `main()` function:
```
if __name__ == "__main__":
loop = asyncio.new_event_loop()
asyncio.set_event_loop(loop)
loop.run_until_complete(main())
print("Completed!")
```
## Run the complete code
Here’s the full Python code that scrapes Google Jobs listings for each query and location asynchronously:
```
import asyncio, aiohttp, json, pandas as pd
from aiohttp import ClientSession, BasicAuth
credentials = BasicAuth("USERNAME", "PASSWORD") # Replace with your API user credentials
URL_parameters = ["developer", "chef", "manager"]
locations = {
"US": ["California,United States", "Virginia,United States", "New York,United States"],
"GB": ["United Kingdom"],
"JP": ["Japan"]
}
payload = {}
with open("payload.json", "r") as f:
payload = json.load(f)
async def submit_job(session: ClientSession, payload):
async with session.post(
"https://data.oxylabs.io/v1/queries",
auth=credentials,
json=payload
) as response:
return (await response.json())["id"]
async def check_job_status(session: ClientSession, job_id):
async with session.get(f"https://data.oxylabs.io/v1/queries/{job_id}", auth=credentials) as response:
return (await response.json())["status"]
async def get_job_results(session: ClientSession, job_id):
async with session.get(f"https://data.oxylabs.io/v1/queries/{job_id}/results", auth=credentials) as response:
return (await response.json())["results"][0]["content"]["jobs"]
async def save_to_csv(job_id, query, location, results):
print(f"Saving data for {job_id}")
data = []
for job in results:
data.append({
"Job title": job["job_title"],
"Company name": job["company_name"],
"Location": job["location"],
"Date": job["date"],
"Salary": job["salary"],
"Posted via": job["posted_via"],
"URL": job["URL"]
})
df = pd.DataFrame(data)
filename = f"{query}_jobs_{location.replace(',', '_').replace(' ', '_')}.csv"
await asyncio.to_thread(df.to_csv, filename, index=False)
async def scrape_jobs(session: ClientSession, query, country_code, location):
URL = f"https://www.google.com/search?q={query}&ibp=htl;jobs&hl=en&gl={country_code}"
payload["url"] = URL
payload["geo_location"] = location
job_id = await submit_job(session, payload)
await asyncio.sleep(15)
print(f"Checking status for {job_id}")
while True:
status = await check_job_status(session, job_id)
if status == "done":
print(f"Job {job_id} done. Retrieving {query} jobs in {location}.")
break
elif status == "failed":
print(f"Job {job_id} encountered an issue. Status: {status}")
return
await asyncio.sleep(5)
results = await get_job_results(session, job_id)
await save_to_csv(job_id, query, location, results)
async def main():
async with aiohttp.ClientSession() as session:
tasks = []
for country_code, location_list in locations.items():
for location in location_list:
for query in URL_parameters:
task = asyncio.ensure_future(scrape_jobs(session, query, country_code, location))
tasks.append(task)
await asyncio.gather(*tasks)
if __name__ == "__main__":
loop = asyncio.new_event_loop()
asyncio.set_event_loop(loop)
loop.run_until_complete(main())
print("Completed!")
```
After the scraper finishes running, you’ll see all the CSV files saved in your local directory.