Behind Google, YouTube is the second most popular engine in the world. It is a video-sharing service where users can watch, share, like, comment, and upload videos. It is home to vloggers, informative content, educational videos, and lots of other data. Some of the main functions of Youtube are:
With the help of web scraping, you can extract data from Youtube and benefit your organization by yielding valuable insights from that data. When you learn how to extract data from Youtube, it is important to know what type of data you want to extract. For instance, if you want to know peoples’ responses to your work, you can scrape the comments section for user sentiment analysis. Similarly, if you want to track the success of a video, you can scrape video performance data.
Before we learn how to scrape Youtube videos, let’s learn why we need to scrape them.
Below mentioned are two main reasons for scraping Youtube data.
Let’s see how to extract Youtube video data using Selenium and Python. Selenium is a popular tool to automate web browsers. You can easily program a Python script for automating a web browser using Selenium.
Selenium requires a driver to interface with your chosen browser. For instance, Chrome requires a ChromeDriver that needs to be installed before you start scraping.
Step 1 – You need to open your terminal and install Selenium by using the command below.
$ pip install selenium
Step 2 – You need to download the Chrome WebDriver by following the below steps.
Step 3 – You need to move the driver file to a PATH.
You have to go to the downloads directory and do the following.
$ cd Downloads $ unzip chromedriver_linux64.zip $ mv chromedriver /usr/local/bin/
We will scrape the video ID, title, and description of a particular category from Youtube. The categories we can scrape are as:
You need to import the necessary libraries like Pandas and Selenium.
from selenium import webdriver import pandas as pd from selenium.webdriver.common.by import By from selenium.webdriver.support.ui import WebDriverWait from selenium.webdriver.support import expected_conditions as EC
You have to open Youtube in your browser. Type in the category you want to search videos for and set the filter to “videos.” You will get the videos related to your search. Now, you have to copy the URL.
You need to set up the driver to fetch the content of the URL from Youtube.
driver = webdriver.Chrome() driver.get("YOUR_LINK_HERE")
Now, paste the link into driver.get(“YOUR_LINK_HERE”) function. Run the cell, and a new browser window will open for that link. You need to fetch the video links present on that particular page. You can create a list to store those links. Afterward, you have to go to the browser window and do the following.
You have to search for the anchor tag with id = “video-title.” Right-click on it -> Copy -> XPath. The XPath will look something like this:
You can use the below code to fetch the “href” attribute of the anchor tag you searched for.
user_data = driver.find_elements_by_xpath('//*[@id="video-title"]') links =  for i in user_data: links.append(i.get_attribute('href')) print(len(links))
You need to create a dataframe with the below four columns.
You can store the details of the videos for different categories in these columns.
df = pd.DataFrame(columns = ['link', 'title', 'description', 'category'])
You are set to scrape the Youtube video details using the Python’s below code.
wait = WebDriverWait(driver, 10) v_category = "CATEGORY_NAME" for x in links: driver.get(x) v_id = x.strip('https://www.youtube.com/watch?v=') v_title = wait.until(EC.presence_of_element_located( (By.CSS_SELECTOR,"h1.title yt-formatted-string"))).text v_description = wait.until(EC.presence_of_element_located( (By.CSS_SELECTOR,"div#description yt-formatted-string"))).text df.loc[len(df)] = [v_id, v_title, v_description, v_category]
We will follow the same steps for the remaining categories. We will have four different dataframes, and we will merge them into a single dataframe. This way, our final dataframe will contain the desired details of the videos from all categories mentioned above.
frames = [df_travel, df_science, df_food, df_manufacturing] df_copy = pd.concat(frames, axis=0, join='outer', join_axes=None, ignore_index=True, keys=None, levels=None, names=None, verify_integrity=False, copy=True)
You can use Youtube proxies for the following tasks:
Residential proxies are the best proxies for Youtube as compared to datacenter proxies. It is because the datacenter proxies get easily detected, and you have to face a lot of Captchas while using them. So, to avoid IP blocking and Captchas, residential proxies are best suited for Youtube automation.
You know Youtube is filled with billions of pieces of valuable data. You can analyze this data and use it to do many things, such as:
You need proxies when scraping Youtube. It is because Youtube employs advanced cybersecurity techniques that detect when you try to purchase multiple items from a single IP address. For circumventing detection, you need to reroute your internet traffic through several different proxy servers. This way, it will look like the network traffic is coming from different computers.
Proxies also act as a shield for marketers using Youtube bots to increase a video’s view count to manipulate the Youtube ranking algorithm and claim revenue from ads.
For organizations and Youtube creators running their accounts, Youtube houses many useful data that can be scraped for analysis. Youtube scrapers extract data related to views, likes/dislikes, comments, and more, making it easier to make better business decisions. You can scrape Youtube videos using Selenium and Python and save a lot of time. The use of proxies is important because your account can get blocked if Youtube detects multiple requests from a single IP address. The best proxies for Youtube are residential proxies as they are super fast and can not be detected easily.
Hope you got an understanding of how to scrape Youtube videos using Python.