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  • Writer's pictureChristopher Cialone

Building a Web Scraper with Python & Beautiful Soup

Cialone Codes' Guide to Building a Python Web Scraper for Property Management Companies in New York City


Web scraping is a powerful technique that allows us to extract valuable information from websites and create useful databases. In this step-by-step guide, we will learn how to build a web scraper using Python to collect essential information like name, address, website, phone number, and contact person for property management companies in the New York City area. We will use the `requests` and `BeautifulSoup` libraries to fetch and parse the data from web pages.


Before proceeding, make sure you have Python installed on your system. Additionally, install the necessary libraries needed by running the following command in your terminal or command prompt:


pip install requests beautifulsoup4


Step 1: Identify Target Websites

The first step is to identify websites that list property management companies in the New York City area. Consider using online directories, business listing platforms, or real estate websites that are likely to have this information.

‘’’ Inspecting the HTML of a website means examining the underlying structure and content of the web page in its raw form. HTML (Hypertext Markup Language) is the markup language used to create the structure of web pages. When you visit a website, your web browser downloads the HTML code and renders it into the visual page that you see.

Inspecting the HTML allows you to understand how the information is organized on the web page, identify the specific HTML elements that contain the data you want to scrape, and determine the attributes (such as class names or IDs) that help target those elements. It is a crucial step in web scraping because it provides the necessary information to write a Python script that can accurately extract the desired data.

Here's how you can inspect the HTML of a web page:

1. Open the web page in your web browser (Chrome, Brave, Safari, etc.).

2. Right-click on the section of the page containing the information you want to extract.

3. From the context menu that appears, select "Inspect" or "Inspect Element." This will open the browser's developer tools, showing the HTML code for the selected element and its surrounding elements.

4. Alternatively, you can use the keyboard shortcut `Ctrl+Shift+I` (or `Cmd+Option+I` on macOS) to open the developer tools.

5. In the developer tools, you'll see the HTML code on the left and the rendered view of the web page on the right. The HTML elements are nested within each other, forming a tree-like structure called the DOM (Document Object Model).

6. Clicking on an HTML element in the developer tools will highlight the corresponding part of the rendered page. This helps you visually identify the elements containing the data you want to scrape.

7. Take note of the HTML tags, classes, IDs, or any other attributes that are associated with the data you want to extract. These will be essential in writing your web scraping script using BeautifulSoup.

By inspecting the HTML, you gain insight into how the website is structured, which elements contain the desired information, and how to navigate and extract data from the page. Armed with this knowledge, you can proceed to write a Python web scraping script using libraries like `requests` and `BeautifulSoup` to extract the information you need from the website.


Step 2: Inspect Website Structure

Once you have identified a suitable website, inspect its HTML structure using your web browser's developer tools (usually accessible via right-click -> Inspect). Look for HTML tags and classes that contain the relevant information (name, address, website, phone number, and contact person).

Step 3: Write the Python Script

Now let's write the Python script to scrape the information. We will create a function that takes the URL of the target website and extracts the required data.

```python code

import requests

from bs4 import BeautifulSoup

def scrape_property_management_companies(url):

response = requests.get(url)

if response.status_code != 200:

print(f"Failed to fetch the page. Status code: {response.status_code}")


soup = BeautifulSoup(response.content, 'html.parser')

# Write code to extract names, addresses, websites, phone numbers, and contact persons

# using BeautifulSoup and print or store the data as needed.

# Example: Extracting names and printing them

company_names = soup.find_all('h3', class_='company-name')

for name in company_names:


# Replace this with the URL of the website you want to scrape

url = ''



Step 4: Parsing HTML Content

Using BeautifulSoup's methods such as `find`, `find_all`, and `get_text`, extract the relevant information from the HTML content. Customize the script to find and store the data in appropriate data structures (e.g., lists, dictionaries) for later use.

Step 5: Handle Pagination (If Needed)

If the information is spread across multiple pages, you will need to handle pagination. Modify the script to navigate through the pages and scrape data from each page.

Step 6: Respect Robots.txt and Website Policies

Before scraping any website, review its `robots.txt` file to check if web scraping is allowed or if there are any specific guidelines to follow. Always respect the website's terms of service and avoid overwhelming the server with too many requests.

Step 7: Save Data to a Database

After scraping the data, consider saving it to a database for easier access and management. You can use Python's built-in `sqlite3` module or connect to other database systems like MySQL or MongoDB using appropriate libraries.

And in the end...

We pulled it off!! We have now built a web scraper in Python to extract information from property management company websites in the New York City area. Web scraping can be a valuable tool to create comprehensive databases and gain insights into various domains. Remember to use web scraping responsibly and adhere to the terms and policies of the websites you interact with.

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