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CrawlMagic
CrawlMagic

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How to do zillow web scraping using selenium

Zillow is a popular real estate website in the United States that provides information on properties, mortgages, and home values. Selenium is a popular web scraping tool that allows users to automate web browsing tasks. Here's a general outline on how to do Zillow web scraping using Selenium:

  1. Install Selenium: You can install Selenium using pip by running the following command in the terminal or command prompt:

pip install selenium

2.Import Selenium and other necessary libraries: To use Selenium, you need to import it in your Python script. You may also want to import other libraries such as pandas for data manipulation and BeautifulSoup for parsing HTML.

from selenium import webdriver
from bs4 import BeautifulSoup
import pandas as pd

3.Set up a webdriver: A webdriver is a browser automation tool that Selenium uses to control a web browser. You can choose which browser to use, such as Chrome, Firefox, or Safari. Here's an example of setting up a Chrome webdriver:

driver = webdriver.Chrome('/path/to/chromedriver')

Note: You need to download the Chrome webdriver and specify the path to it in the code above.

4.Navigate to the Zillow website: You can navigate to the Zillow website using the get method of the webdriver object.

driver.get('https://www.zillow.com/')

5.Search for a property: You can search for a property by locating the search bar element and entering the search term. You can locate elements using the find_element_by method of the webdriver object.

search_bar = driver.find_element_by_name('search')
search_bar.send_keys('Seattle, WA')
search_bar.submit()

Note: In the code above, we searched for properties in Seattle, Washington.

6.Scrape the data: Once you've navigated to the search results page, you can scrape the data using BeautifulSoup or other HTML parsing libraries. Here's an example of scraping the property prices:

soup = BeautifulSoup(driver.page_source, 'html.parser')

prices = []
price_tags = soup.find_all('div', {'class': 'list-card-price'})
for tag in price_tags:
prices.append(tag.text.strip())
df = pd.DataFrame({'Prices': prices})
print(df)

7.Close the webdriver: Once you're done scraping the data, you should close the webdriver to free up system resources.

driver.quit()

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