How To Scrape YouTube Videos Using Python - ProxyScrape

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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:

  • Searching for and watching videos
  • Creating a personal Youtube channel
  • Uploading videos to your channel
  • Subscribing other channels and users
  • Liking and sharing other Youtube videos
  • Creating playlists to organize videos together

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.

Why Scrape Videos On Youtube?

Below mentioned are two main reasons for scraping Youtube data.

  • Video Performance Data –  When you post informational videos for a brand, it is important to track how your audience responds to them. Scraping the page for a specific video will help you receive the number of views, likes, dislikes, comments, channel subscribers, and more. You need to keep in mind the ration of each of these metrics. For instance, a video can have millions of views and have more dislikes than likes. The number of views is not indicative of a well-liked or high-quality video. Instead, the ratio of views to likes/dislikes can be a form of sentiment analysis.
  • Channel Data – When scraping the page for a Youtube channel, you’ll get data related to the playlists, number of videos, subscribers, and more. Further, it is useful and informative to scrape the pages of competing channels to understand better whether your channel is at the same level of influence compared to them. 
  • Achieve Automation – Robust web scrapers automatically allow you to extract data from Youtube. It saves your time as you can collect data at greater volume than a single human can ever hope to achieve. 
  • Business Intelligence and Insights – You can get a better picture of your competitors’ activity by downloading, cleaning, and analyzing data at significant volume, leading to better business decision-making.

Scraping Youtube Videos Using Python

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.

Setting Up The Python Environment

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.

  • You have to visit
  • You have to select the compatible driver for your Chrome version.
  • You need to check the Chrome version you are using by clicking on the three vertical dots in the top right corner.
  • Then, you have to go to Help -> About Google Chrome.

Step 3 – You need to move the driver file to a PATH.

You have to go to the downloads directory and do the following.

  • Unzip the file.
  • Move it to usr/local/bin PATH.
$ cd Downloads
$ unzip
$ mv chromedriver /usr/local/bin/

Scraping Youtube Videos

We will scrape the video ID, title, and description of a particular category from Youtube. The categories we can scrape are as:

  • Science
  • Food
  • Travel
  • Manufacturing etc.

Import Libraries

You need to import the necessary libraries like Pandas and Selenium.

from selenium import webdriver 
import pandas as pd 
from import By 
from import WebDriverWait 
from import expected_conditions as EC

Setting Up the Driver

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() 

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.

  • Right-click on the page.
  • Select the “Inspect” element.

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:


Create a DataFrame

You need to create a dataframe with the below four columns.

  • link
  • title
  • description
  • category

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:
            v_id = x.strip('')
            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(
            df.loc[len(df)] = [v_id, v_title, v_description, v_category]


  • wait ignores instances of NotFoundException encountered by default in the “until” condition. 
  • The parameters of the wait function are:
    • driver – It is the WebDriver instance to be passed to the expected conditions.
    • timeOutInSeconds – It is the timeout when the expectation is called.
  • v_category is used for storing video category_name.
  • We applied the for loop for the list of links created above.
  • driver.get(x) performs the below functions:
    •  traverses through all the links one-by-one
    • opens them in the browser to fetch the details
  • v_id is used for storing the striped video ID from the link.
  • v_title stores the video title fetched by using CSS_SELECTOR
  • Likewise, v_description stores the video description by using CSS_SELECTOR

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)

Using a Proxy to Scrape Youtube Videos

You can use Youtube proxies for the following tasks:

  • Scraping – You can collect video titles, comments, and any information properly by using a proxy. You can also use a proxy to scrape Youtube videos that are within the Creative Commons domain. Therefore, you can add videos to your website without using Youtube as the official player.
  • Unblocking Youtube – Many companies try to hide their content from the public for political or other reasons. With the help of the proxies, you can upload and watch Youtube content from a location where your access is restricted. Proxies help you access Youtube videos that your school or workplace has blocked.

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.

Why Use Proxies For Scraping Youtube?

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:

  • Making business decisions
  • Marketing decisions
  • Social research and studies

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.

Conclusion on Youtube Video Scraping Using Python

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.