# Scraping Fnac: How to Extract Product Prices Without Getting Blocked

> Source: https://scrape.do/blog/fnac-scraping/

Published: 2025-02-06 · Updated: 2025-02-06 · Authors: Mert Bekci · Categories: Scraping Use Cases

Fnac is a leading retailer in France and Spain, offering everything from electronics and books to household appliances.

And **if you’ve tried scraping Fnac** for product prices and stock availabilty\*\*, you’ve probably been blocked.\*\*

The site actively prevents automated access through **regional restrictions and custom WAF rules**, making it difficult to retrieve structured data.

*But there’s a way around it.*

In this guide, we’ll break down **why Fnac is difficult to scrape, how its defenses work, and how to bypass them using Scrape.do** to extract product information without getting blocked.

[Find fully-functioning code here ⚙](https://github.com/scrape-do/scrapedo-scrapers/tree/main/fnac-scraper)

## Why Scraping Fnac Is Difficult

Fnac, like many large e-commerce websites, has implemented multiple layers of security to prevent automated data collection.

These protections make traditional scraping techniques unreliable and often result in blocked requests.

### 1\. Regional Restrictions

Fnac enforces strict **geo-blocking**, meaning certain content is only accessible from specific countries. If your requests originate from an unsupported region, you may see incomplete data or be denied access altogether.

* Pages may **redirect** users based on location.
* Some product listings **vary by country**, requiring different approaches depending on the target market.
* Requests from IPs outside **France or Spain** often receive **403 Forbidden** errors.

### 2\. Custom WAF Rules

Fnac uses a **custom Web Application Firewall (WAF)** to detect and block scrapers. Unlike standard WAF solutions, Fnac’s security rules are tailored for its platform and include:

* **Behavior tracking** to detect non-human interaction patterns.
* **Session-based fingerprinting** that correlates multiple requests from the same scraper.
* **Aggressive rate-limiting** that blocks repeated requests within a short time frame.

These protections make it difficult to scrape Fnac without **advanced request management techniques**.

## How Scrape.do Bypasses These Challenges

Scrape.do is designed to handle **geo-restrictions, WAF defenses, and session tracking**, allowing seamless access to Fnac’s data without being blocked.

### Geo-targeted Proxies

Fnac enforces strict **regional access**, making it difficult to scrape without an IP from an approved location.

✅ Scrape.do automatically routes requests through **France- and Spain-based residential and ISP proxies**, ensuring requests appear as legitimate user traffic.

### Advanced Session Management

Many blocks occur because Fnac tracks browsing sessions, flagging scrapers that repeatedly access product pages.

✅ Scrape.do maintains **persistent session handling**, preventing Fnac from detecting bot-like behavior across multiple requests.

### Dynamic Request Optimization

Fnac’s **custom WAF** relies on fingerprinting techniques to detect scrapers based on request headers, TLS fingerprints, and browsing patterns.

Scrape.do dynamically adjusts:

* **Headers and user agents** to mimic real browser requests.
* **TLS fingerprints** to match legitimate users.
* **Request timing** to prevent triggering rate limits.

With these optimizations, Fnac can be scraped without triggering **403 errors, redirects, or temporary bans**.

## Extracting Data from Fnac Without Getting Blocked

Now that we have a strategy to bypass Fnac’s protections, we’ll start extracting product data.

First, we need to ensure our request reaches the page successfully and returns a **200 OK** response.

### 1\. Prerequisites

Before scraping Fnac, install the required dependencies if you haven’t already:

```bash
pip install requests beautifulsoup4
```

You'll also need an API key from Scrape.do, which you can obtain by [signing up for FREE at Scrape.do.](https://dashboard.scrape.do/sign-up)

We'll scrape product information from [this iPhone 16 model](https://www.fnac.com/Apple-iPhone-16-Pro-Max-6-9-5G-256-Go-Double-SIM-Noir-Titane/a17312773/w-4).

![extract iphone data from fnac](/uploads/blog/fnac-data-extraction-iphone_hufbdb7ebdf8534721e4e18c04a4d6667c_185239_1200x0_resize_q80_h2_box_3.webp)

### 2\. Sending a Request and Verifying Access

```python
import requests
import urllib.parse

# Our token provided by Scrape.do
token = "<your_token>"

# Target Fnac product URL
target_url = urllib.parse.quote_plus("https://www.fnac.com/Apple-iPhone-16-Pro-Max-6-9-5G-256-Go-Double-SIM-Noir-Titane/a17312773/w-4")

# Optional parameters
render = "true"
geo_code = "fr"
super_mode = "true"

# Scrape.do API endpoint
url = f"https://api.scrape.do/?token={token}&url={target_url}&geoCode={geo_code}&super={super_mode}"

# Send the request
response = requests.request("GET", url)

# Print response status
print(response)
```

Here's the expected output:

```yaml
<Response [200]>
```

If the request is successful, we can move on to extracting the **product name**.

## Extracting the Product Name

The product name is stored in an `<h1>` tag, making it easy to locate and extract using `BeautifulSoup`.

```python
from bs4 import BeautifulSoup
import requests
import urllib.parse

# Our token provided by Scrape.do
token = "<your_token>"

# Target Fnac product URL
target_url = urllib.parse.quote_plus("https://www.fnac.com/Apple-iPhone-16-Pro-Max-6-9-5G-256-Go-Double-SIM-Noir-Titane/a17312773/w-4")

# Optional parameters
render = "true"
geo_code = "fr"
super_mode = "true"

# Scrape.do API endpoint
url = f"https://api.scrape.do/?token={token}&url={target_url}&geoCode={geo_code}&super={super_mode}"

# Send the request
response = requests.request("GET", url)

# Parse the response using BeautifulSoup
soup = BeautifulSoup(response.text, "html.parser")

# Extract product title
title = soup.find("h1").text.strip()

print("Product Name:", title)
```

Expected output:

```yaml
Product Name: Apple iPhone 16 Pro Max 6,9" 5G 256 Go Double SIM Noir Titane
```

Now that we have the product name, we’ll extract the **price** in the next section.

## Extracting the Product Price

With the product name successfully extracted, we now need to get the price. The price is located inside a `<span>` tag with the class **`f-faPriceBox__price userPrice checked`**, as seen in the inspected HTML.

```python
<----- Previous section until the Print command ----->

# Extract product price
price = soup.find("span", class_="f-faPriceBox__price userPrice checked").text.strip()

print("Product Price:", price)
```

Expected output:

```yaml
Product Price: 1 431 €
```

If the extracted value contains unwanted spaces (`1 431 €`), you can clean it up:

```python
price = price.replace("\xa0", " ")  # Replace non-breaking space
```

**Congratulations 🎉,** you are now able to extract product data from Fnac without any blocks!

## Conclusion

Scraping Fnac is difficult due to **geo-restrictions and custom WAF rules**, but not impossible.

Need to scrape Fnac?

**Scrape.do makes it simple.**

[Get **1000 free API calls** and start now.](https://dashboard.scrape.do/sign-up)