How to Scrape Pinterest: Full Tutorial with ScrapingBee

In this tutorial, I’ll show you how to scrape Pinterest using ScrapingBee’s API. Whether you want to scrape Pinterest data for trending images, individual pins, Pinterest profiles, or entire boards, this guide explains how to build a web scraper that works.

Scraping Pinterest can be tough. Its anti-bot protection often trips up typical web scrapers. That's why I prefer using ScrapingBee. With this tool, you won't need to run a headless browser or wait for page elements to load manually. You just plug in your API key, decide what data to collect, and extract Pinterest data with ease.

Without further ado, let's explore how to scrape data from Pinterest.

Quick Answer (TL;DR)

You can scrape Pinterest with a single ScrapingBee API call – it’s that straightforward. The Pinterest Scraping API handles all the complex aspects of data extraction automatically. JavaScript rendering and rotating proxies are enabled by default, so you don’t need to worry about dynamic content or getting blocked.

Here's the complete code that immediately extracts image URLs from a Pinterest board or profile page:

import requests
import json
import csv
from bs4 import BeautifulSoup

# Step 1: Setup
api_key = "YOUR_API_KEY"
url = "https://www.pinterest.com/pinterest/official-news/"

# Step 4: Scrolling with js_scenario
js_scenario = [
    {"wait": 2000},
    {"scrollY": 1000},
    {"wait": 2000},
    {"scrollY": 2000},
    {"wait": 2000},
    {"scrollY": 3000},
    {"wait": 2000},
    {"scrollY": 4000},
    {"wait": 2000},
    {"scrollY": 5000}
]

params = {
    "api_key": api_key,
    "url": url,
    "js_scenario": json.dumps(js_scenario)
}

# Step 2: Get HTML
response = requests.get("https://app.scrapingbee.com/api/v1/", params=params)
html = response.text

# Step 3: Extract image URLs
soup = BeautifulSoup(html, 'html.parser')
images = soup.find_all('img')
image_links = []

for img in images:
    src = img.get('src')
    if src and "pinimg.com" in src:
        image_links.append(src)

# Step 5: Export to CSV
with open('pinterest_images.csv', 'w', newline='') as f:
    writer = csv.writer(f)
    writer.writerow(['Image URL'])
    for link in image_links:
        writer.writerow([link])

That's how easy it is to scrape data from Pinterest. However, if you would like to gain a deeper understanding of the process, continue reading. I'll break down each step in detail to give you a full picture of how to collect data using a Pinterest scraper.

Scrape Pinterest with ScrapingBee

Pinterest is a popular scraping target. After all, this website is a gold mine of data and content you can use for trend analysis or machine learning. For this reason, Pinterest implements strict measures to render all mediocre web scrapers useless.

Out of all the Pinterest scrapers I tried, ScrapingBee has consistently delivered the best results. Initially, I had to glance at the documentation a few times, but now I can complete the task in just a few steps.

Let me demonstrate:

Set Up Your Environment

First, you’ll need to install the required packages and get your API key:

pip install requests beautifulsoup4

Now, get your ScrapingBee API key from your dashboard.

If you don’t have an account yet, signing up gives you free credits to start with. I recommend testing with a small number of API calls first to ensure your extraction logic works correctly before scaling up.

Make a Basic API Call to Pinterest

Here's the code example for launching your first request to Pinterest. Make sure to replace YOUR_API_KEY with your actual key. Then replace pinboard_name with a specific board name you choose.

import requests

api_key = "YOUR_API_KEY"
url = "https://www.pinterest.com/pinterest/pinboard_name/"

params = {
    "api_key": api_key,
    "url": url
}

response = requests.get("https://app.scrapingbee.com/api/v1/", params=params)
html = response.text

Extract Pins and Image URLs

Once we have the HTML response, we need to parse it to extract the image URLs. BeautifulSoup makes this process straightforward:

from bs4 import BeautifulSoup

soup = BeautifulSoup(html, 'html.parser')
images = soup.find_all('img')

for img in images:
    src = img.get('src')
    if src and "pinimg.com" in src:
        print(src)

The key here is filtering for images that contain “pinimg.com” in their source URL. This ensures we’re not capturing icons or other non-pin images.

In my experience, this approach catches most pins; however, you may need to adjust the filter based on your specific needs.

If you want to learn more, check out our Python Web Scraping 101 tutorial.

Understanding the JavaScript Scenario Feature

Now, I want to briefly introduce you to what makes our Pinterest scraper reliable. It's the JavaScript scenario feature, which allows you to simulate human interactions, such as scrolling. This is crucial for Pinterest because new content loads as you scroll down the page. Without this feature, you’d only get the initially loaded pins.

Let's see how it works in action.

Handle Pagination and Scrolling

Learning how to use js_scenario effectively is crucial for scraping infinite scroll pages, such as those found on Pinterest. By using this feature, we'll be able to scrape data more effectively.

Here’s how to implement scrolling using js_scenario parameter:

import json

js_scenario = [
    {"wait": 2000},
    {"scrollY": 1000},
    {"wait": 2000},
    {"scrollY": 2000},
    {"wait": 2000},
    {"scrollY": 3000},
    {"wait": 2000},
    {"scrollY": 4000},
    {"wait": 2000},
    {"scrollY": 5000}
]

params = {
    "api_key": api_key,
    "url": url,
    "js_scenario": json.dumps(js_scenario)
}

response = requests.get("https://app.scrapingbee.com/api/v1/", params=params)
html = response.text

This js_scenario does the following:

1. Waits 2 seconds for the initial page to load

2. Scrolls down 1000 pixels

3. Waits another 2 seconds for new content to load

4. Continues this pattern, scrolling further each time

The waiting periods are crucial – they give Pinterest time to load new pins. I’ve found that 2-second intervals work well, but you may need to adjust them based on your connection speed and Pinterest’s response time.

Export the Data from Pinterest

Once you’ve extracted the data, you’ll want to save it for analysis. CSV is a standard format that works well with most data analysis tools:

import csv

with open('pinterest_images.csv', 'w', newline='') as f:
    writer = csv.writer(f)
    writer.writerow(['Image URL'])
    for img in images:
        src = img.get('src')
        if src and "pinimg.com" in src:
            writer.writerow([src])

This creates a simple CSV with one column for image URLs. You can expand this to include additional data, such as pin titles and descriptions, by extracting those elements from the HTML as well.

Common Issues When Scraping Pinterest Data

When a request fails in Python during Pinterest scraping, it’s often due to rate limiting or IP blocking.

Here are some common issues you might encounter and how to address them:

1. Rate Limiting: Pinterest may restrict the number of requests from a single IP. ScrapingBee’s proxy rotation helps mitigate this issue, but you may still need to implement delays between requests for large-scale scraping.

2. Dynamic Content Changes: Pinterest occasionally updates its HTML structure. If your scraper suddenly stops working, check if the selectors or patterns you’re using to extract data are still valid.

3. JavaScript Rendering Issues: Some Pinterest features rely heavily on JavaScript. If you’re not seeing expected content, ensure JavaScript rendering is enabled.

4. Incomplete Data: If you’re not getting all the pins from a board, you might need to adjust your js_scenario to scroll more or wait longer between scrolls.

5. Authentication Requirements: Some Pinterest content requires a login. Our platform can handle cookies and sessions, but you’ll need to set these up correctly.

From my experience, the most frequent issue is incomplete data extraction due to insufficient scrolling. I recommend starting with more scrolling operations than you think you’ll need, then optimizing based on the results.

Simplify How You Scrape Data from Pinterest

Scraping Pinterest with ScrapingBee simplifies what would otherwise be a complex process. By handling JavaScript rendering, proxy rotation, and browser automation, it enables you to focus on extracting and utilizing the data, rather than overcoming technical challenges.

The approach we’ve covered is suitable for most Pinterest scraping needs, ranging from collecting inspiration images to tracking trends and monitoring competitors. Remember to respect Pinterest’s terms of service and implement reasonable delays between requests to be a good web citizen.

Start Scraping Pinterest Now

Ready to extract valuable Pinterest data for your projects? Sign up for our platform and start scraping Pinterest profiles today.

With free credits for new users, you can test the platform and see how it simplifies the web scraping process.

Whether you’re researching market trends, gathering design inspiration, trying to gain more followers, or building a Pinterest-based application, ScrapingBee’s API provides the tools you need to extract data efficiently and reliably.

Frequently Asked Questions (FAQs)

Can I scrape Pinterest with Python and ScrapingBee?

Yes, you can easily scrape Pinterest using Python and ScrapingBee. The combination provides a powerful solution that handles JavaScript rendering, proxy rotation, and anti-bot measures automatically. As shown in this tutorial, you only need a few lines of Python code to make API calls to our scraper and process the returned HTML.

Do I need to log in to scrape Pinterest boards?

For public Pinterest boards, no login is required. Our platform can access and scrape these boards directly. However, for private boards or content that requires authentication, you’ll need to provide login credentials or cookies through ScrapingBee’s parameters. This process involves capturing your session cookies and passing them with your API request.

Does ScrapingBee handle Pinterest JavaScript?

Yes, our patform handles Pinterest’s JavaScript rendering by default. This is crucial for Pinterest scraping since much of the content loads dynamically through JavaScript. It uses real browsers behind the scenes to ensure all JavaScript executes properly, giving you access to the fully rendered page content.

How do I scroll Pinterest pages with ScrapingBee?

You can scroll Pinterest pages using ScrapingBee’s js_scenario parameter. This allows you to define a sequence of actions, including waiting periods and scrolling operations. By simulating scrolling, you trigger Pinterest’s infinite scroll mechanism, loading additional pins that wouldn’t be visible in the initial page load.