How can I scrape Nordstrom's new arrivals section effectively?

Scraping Nordstrom's New Arrivals section—or any website—effectively requires several steps. You must respect the website's terms of service and robots.txt file, simulate a browser request properly, handle pagination, and parse the HTML to extract the data you want.

Please note: Web scraping can be against the terms of service of some websites. Always check the website's terms of service and robots.txt file to ensure compliance with their rules. The following is a hypothetical educational example and should not be used to scrape any website without permission.

Here are steps to scrape a website like Nordstrom's New Arrivals section effectively:

Step 1: Inspect the Web Page

Use your web browser's Developer Tools to inspect the New Arrivals web page. Look for:

  • How the data is loaded (statically in HTML, via JavaScript, or from an API).
  • The URL structure if the data is loaded from an API.
  • How pagination is handled if there are multiple pages of new arrivals.

Step 2: Get the URL

Identify the URL for the New Arrivals section. If the data is loaded dynamically, find the API endpoint that returns the new arrivals data.

Step 3: Write a Basic Scraper

Python Example with Requests and BeautifulSoup

import requests
from bs4 import BeautifulSoup

# URL of the New Arrivals section
url = ''

headers = {
    'User-Agent': 'Mozilla/5.0 (X11; Ubuntu; Linux x86_64; rv:85.0) Gecko/20100101 Firefox/85.0'

# Make an HTTP GET request to the New Arrivals section
response = requests.get(url, headers=headers)

# Check if the request was successful
if response.status_code == 200:
    # Parse the HTML content
    soup = BeautifulSoup(response.content, 'html.parser')

    # Find the elements containing the new arrivals data
    # For example, if each item is contained within an article tag with a class "item"
    items = soup.find_all('article', class_='item')

    for item in items:
        # Extract the data you need, e.g., title, link, price
        title = item.find('h3').text
        link = item.find('a')['href']
        price = item.find('span', class_='price').text
        print(f'Title: {title}, Link: {link}, Price: {price}')
    print('Failed to retrieve the page')

Step 4: Handle Pagination

If there are multiple pages of new arrivals, you'll need to handle pagination. This could involve iterating over page numbers or following 'next page' links.

# Example of handling pagination by iterating over page numbers
base_url = ''

for page in range(1, max_pages + 1):
    response = requests.get(f"{base_url}{page}", headers=headers)
    # ... parse the response and extract data as above

Step 5: Respect the Website's Terms and Rate Limiting

Make sure to respect the website's rate limiting by spacing out your requests. You can use time.sleep() in Python to add delays between requests.

import time

for page in range(1, max_pages + 1):
    response = requests.get(f"{base_url}{page}", headers=headers)
    # ... parse the response and extract data
    time.sleep(1)  # Sleep for 1 second between requests

Step 6: Error Handling and Logging

Implement error handling and logging to handle potential issues during scraping, such as network problems or changes in the website's HTML structure.

import logging


    # ... scraping code
except requests.exceptions.RequestException as e:
    logging.error(f'Request failed: {e}')
except Exception as e:
    logging.error(f'An error occurred: {e}')

Step 7: Store the Scraped Data

Store the scraped data in a structured format like JSON, CSV, or a database.

import json

data = []

for item in items:
    # Extract the data into a dictionary
        'title': title,
        'link': link,
        'price': price

# Save the data to a JSON file
with open('new_arrivals.json', 'w') as f:
    json.dump(data, f)

JavaScript Example with Node.js

If you prefer JavaScript and Node.js, you can use libraries like axios for HTTP requests and cheerio for HTML parsing.

const axios = require('axios');
const cheerio = require('cheerio');

const url = '';

  .then(response => {
    const $ = cheerio.load(;
    $('article.item').each((i, element) => {
      const title = $(element).find('h3').text();
      const link = $(element).find('a').attr('href');
      const price = $(element).find('span.price').text();
      console.log(`Title: ${title}, Link: ${link}, Price: ${price}`);

In both Python and JavaScript examples, the selectors used (like 'article.item', 'h3', etc.) must match the actual HTML structure of Nordstrom's New Arrivals page, which might be different. You'll need to inspect the page and adjust the selectors accordingly.

Remember, this is a general guide. For Nordstrom or any specific site, you must tailor your approach based on their structure, loading mechanisms, and legal terms.

Related Questions

Get Started Now

WebScraping.AI provides rotating proxies, Chromium rendering and built-in HTML parser for web scraping