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Web scraping is a powerful technique that can be used to collect data from websites in an automated manner. Scraping Eventbrite, a popular event management platform, can provide valuable data on upcoming events, ticket prices, attendee numbers, and event descriptions. Additionally, scraping event data from platforms like Eventbrite can reveal emerging trends and consumer preferences, enabling businesses to stay ahead of the competition and deliver memorable experiences. While it offers a wealth of public event data, it also comes with built-in restrictions. These restrictions, such as rate limits, CAPTCHAs, and IP blocking, make it difficult to scrape data at scale without hitting roadblocks.
In this article, you’ll learn how to scrape Eventbrite using Python and BeautifulSoup to extract events data using The Social Proxy mobile proxies to overcome technical challenges. The dataset will then be exported into a CSV file for further analysis. Finally, we’ll also perform a trend analysis of the scraped data to identify patterns like event frequency, seasonality, popular event types, and emerging trends.
Before we start scraping, it’s important to note Eventbrite’s website structure and how the data is organized. Eventbrite lists events under specific categories and keywords, such as “fintech,” “business,” “music,” etc. with each event displayed in a consistent format. When viewing search results, you’ll notice that each event entry contains key data points: the event title, date and time, location, and whether the event is paid or free. These elements are structured within the HTML tags of the page, making them easily accessible for scraping.
To scrape data effectively, use your browser’s developer tools (like Chrome DevTools) to inspect these elements. Right-click on an event and select “Inspect” to open the DevTools. You’ll see that event titles are often inside `<h3>` or `<a>` tags, while dates and locations are found within `<span>` or `<div>` elements. This is where your scraper will extract data from.
Another important aspect is pagination. Eventbrite uses a dynamic URL structure where page numbers or queries for the keyword “fintech” and location “New York” are part of the URL. Event pages load dynamically as you scroll, so identifying URL patterns for pages is important to accessing a comprehensive dataset.
Scraping data has its own set of challenges which are generally designed to prevent automated data extraction. Anti-bot methods used by websites like Eventbrite include CAPTCHAs, rate limits, and IP blocking to protect the platform from excessive scraping and attacks. CAPTCHAs challenge scrapers with tests that only human users can solve, while rate limits restrict how often you can request data. Additionally, IP blocking occurs when too many requests are made from the same IP address, leading to temporary or permanent bans.
Standard scraping techniques often fall short due to these restrictions. However, you can use The Social Proxy’s mobile or residential proxies to overcome these barriers. These proxies route your requests through real devices and IPs, making them appear as if they’re coming from different genuine users, which helps avoid detection and bypass rate limits or CAPTCHAs. This ensures your scraping process remains efficient and uninterrupted.
In order to start scraping Airbnb data, you’ll need to set up the following tools in your development environment:
Next, open the proxy switching service (BP proxy switcher in this case), and enter your new proxy in the relevant field.
Use the following format in the proxy switching service:
host:port:user:pass
(You can also copy the details from the Dashboard.)
Once added, select the proxy of your choice.
You can verify the proxy is working and check for speeds here.
Once the proxy is set up, you can continue setting up the development environment.
Use the following code to set up the Python virtual environment:
python3 -m venv scraper_env
Source scraper_env/bin/activate
pip install beautifulsoup4 requests selenium pandas
Now that your environment is setup let’s understand the URL structure for eventbrite
The landing page of the platform looks like this: https://www.eventbrite.com/
When we search for all events in a specific region like New york: the state code and name are appended to the URL followed by all-events towards the end: https://www.eventbrite.com/d/ny–new-york/all-events/
If you’re interested in looking for certain kinds of events say fintech, all you have to do is mention the topic of interest towards the end:
https://www.eventbrite.com/d/ny–new-york/fintech/
There are multiple pages of results that you’ll need to scrape data from. For handling dynamic loading of results, we have a page variable to help you keep tabs on the current page number of the result: https://www.eventbrite.com/d/ny–new-york/fintech/?page=2
Eventbrite’s search URL is structured in a way that makes it easy to modify to target specific keywords and locations. The code below demonstrates how to build the dynamic URL in the Python code where searchq is the user input regarding the type of event they want to scrape data for (fintech, business, art etc.) and i is the iterator representing the page number of the platform results.
url = ‘https://www.eventbrite.com/d/ny—new-york/’ + searchq + “/?page=” + str(i+1) + “&lang=en”
Most proxy services, including The Social Proxy, offer integration options with popular web scraping tools. For example, you can set the proxy address and port in tools like Selenium ensuring your requests are routed through the proxy network, thus avoiding CAPTCHAs and IP blocks. Alternatively, you can use the setup in the previous section.
Start by adding the necessary imports.
from selenium import webdriver
from bs4 import BeautifulSoup
import time
import pandas as pd
import re
Set up Selenium with ChromeDriver (or another browser driver) and the URL. For this example, we’ll keep the pages up to 5.
driver = webdriver.Chrome() # Make sure you've installed the driver
text = input().split(";")
num_of_pages = 5
for searchq in text:
for i in range(num_of_pages):
url = 'https://www.eventbrite.com/d/ny--new-york/' + searchq + "/?page=" + str(i+1) + "&lang=en"
# Open the webpage
driver.get(url)
# Give it some time to fully load
time.sleep(5)
If you’re not using a proxy switcher, you can also provide the proxy URL while initializing the driver.
chrome_options = Options()
chrome_options.add_argument(f'--proxy-server={proxy_url}')
driver = webdriver.Chrome(options=chrome_options)
Now you can parse the HTML content of the response using BeautifulSoup. This library will help navigate and search the HTML structure easily.
# Fetch the URL data using requests.get(url),
# store it in a variable, request_result.
request_result = requests.get(url)
# Creating soup from the fetched request
soup = bs4.BeautifulSoup(request_result.text, "html.parser")
all_events = soup.find("ul", {"class":"SearchResultPanelContentEventCardList-module__eventList___2wk-D"})
all_events = all_events.find_all("li")
print(all_events)
print("Name | link | Location | Date time | Type | ")
for event in all_events:
event_name = event.find("h3", {"class": "Typography_root__487rx #3a3247 Typography_body-lg__487rx event-card__clamp-line--two Typography_align-match-parent__487rx"}).text
event_link = event.find('a', {"class": "event-card-link"},href=True).get('href')
event_location = event.find('p', {"class": "Typography_root__487rx #585163 Typography_body-md__487rx event-card__clamp-line--one Typography_align-match-parent__487rx", "style":"--TypographyColor:#585163"}).text
event_date_time = event.find('p', {"class":"Typography_root__487rx #3a3247 Typography_body-md-bold__487rx Typography_align-match-parent__487rx", "style":"--TypographyColor:#3a3247"}).text
On this Eventbrite search page, we’re interested in the listed events. In order to access them, we have to define the tag types and class names of the elements we want to access, so we need the URL of the event to further scrape the event page for details like Organizer name, ticket price, etc. The easiest way to do that is to inspect the page with a Chrome developer tool (press F12).
Eventbrite organizes its event listings across multiple pages, which requires extracting data from more than just the first page. To manage pagination, you can loop through each page by appending a query parameter like ?page= to the URL. This allows you to automatically scrape data from the first five pages by iterating over them with a loop.
For websites like Eventbrite that are heavy with JavaScript in order to load content, you can use Selenium to open each page, wait for the content to load, and then collect the event data. It simulates a real browser, executing JavaScript as a human user would. To ensure that all content is loaded before extracting data, you can use either time.sleep() or more advanced techniques like explicit waits (WebDriverWait) to wait for elements to appear.
Here’s a simple code example:
from selenium import webdriver
from selenium.webdriver.common.by import By
import time
# Set up Selenium WebDriver (e.g., using Chrome)
driver = webdriver.Chrome()
# Start at the first page
url = 'https://www.eventbrite.com/d/ny--new-york/fintech/'
driver.get(url)
# Loop through the first 5 pages
for page in range(1, 6):
driver.get(f"{url}?page={page}")
# Wait for dynamic content to load
time.sleep(3) # Or use explicit wait for more efficiency
# Extract event details (e.g., titles, dates, locations)
events = driver.find_elements(By.CLASS_NAME, 'search-event-card-wrapper')
for event in events:
title = event.find_element(By.CLASS_NAME, 'eds-event-card__formatted-name--is-clamped').text
date = event.find_element(By.CLASS_NAME, 'eds-text-bs--fixed').text
location = event.find_element(By.CLASS_NAME, 'card-text--truncated__one').text
print(f"Title: {title}, Date: {date}, Location: {location}")
Now that you’ve scraped the required data, you can store it in different structured formats:
We’ll use pandas to save the data extracted to a CSV.
# Create Dataframe
data = pd.DataFrame()
data[‘Registration Link’] = eventlinks
data['Title'] = eventname
data['Description'] = eventdescriptions
data['Locations'] = eventlocation
data['Date and Time'] = eventdatetime
data['Organizers'] = eventorganizers
data['Event Type(Free/Paid)'] = eventtype
data['Price of Ticket'] = eventrpice
data.to_csv("eventbrite_scraped_data.csv")
# Close the browser
driver.quit()
Scraping Eventbrite provides access to a wealth of data that can help researchers, businesses, and event planners make strategic decisions. You can gain insights about popular event categories, market trends, and types of events by extracting important event details including locations, organizers, format of the events, dates, locations, and ticket pricing. With this level of detail, businesses can examine event frequency, preferred locations, and pricing strategies—all important considerations when organizing events, forming alliances, or customizing marketing campaigns.
Staying in tune with industry trends is crucial in the fintech space, and event data can help you keep your finger on the pulse. In a global center such as New York, fintech events provide a sneak peek into new trends, networking possibilities, and thought leadership exposure. Companies can track competitors, identify important players in the market, and find trends in event themes and audience participation by scraping this data.
Complete event data equates to actionable intelligence in the eyes of investors, startups, and job seekers. Finding the industry’s key areas or assessing trends that impact future innovations are just a few of the ways that scraping Eventbrite can give you a competitive edge. Having access to these insights can help you skillfully navigate the fintech ecosystem and make well-informed decisions that promote opportunity and growth.
Scraping event data from Eventbrite, particularly for fintech events in New York, provides businesses with useful insights for business intelligence, event comparison, and trend analysis. Leveraging The Social Proxy’s mobile proxies, you can overcome technical restrictions like rate limiting and IP blocking, ensuring a smooth and efficient scraping process.
We’ve covered the basics of scraping Eventbrite listings using Python, BeautifulSoup, and The Social Proxy; extracted desired information; and saved the results to a CSV file, without the disruption of IP blockages and timeouts. Please note, the code provided is an example, and you’ll need to adapt it to your specific scenario as the dynamic HTML elements can change. . You can sign up for a free trial using this link. Happy scraping!
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