youtube scrape python seo

I had a project this week that tasked my team with optimizing YouTube tags for a couple hundred videos. We could do it manually but thought this was a nice chance to use Python. Our idea was we could scrape YouTube video page information, put it into a spreadsheet for easier organization and identification of optimization needs in terms of tagging. It turns out to be easier than I thought outside of keeping Google’s blocking tendencies at bay.

In this Python SEO tutorial, I’ll show you how to scrape the title, views, description, and tags for a video. The bonus is we’ll detect if any tags are used in the description.

Requirements and Assumptions

  • Python 3 is installed and basic Python syntax understood
  • Access to a Linux installation (I recommend Ubuntu) or Google Colab.
  • Video URL list CSV file with a URL column labeled “urls”
    • I was able to quickly scrape all the videos in a channel via a Firefox/Chrome extension called LinkGopher.

Import Modules

  • pandas: for importing the URL CSV file, storing and exporting results
  • fake_useragent : generates user_agents to evade Google blocking
  • bs4: an HTML parser
  • requests: makes the HTTP call
  • time: delaying the script to evade Google blocking
  • re: for regex matching
  • numpy: for randomization function

Before we begin, remember to watch the indents of anything you copy here as sometimes the code snippets don’t copy perfectly.  First, we need to install fake_useragent. If you are using Google Colab put an exclamation market at the start.

pip3 install fake_useragent

Let’s first import the modules needed for this script expressed above.

from fake_useragent import UserAgent
from bs4 import BeautifulSoup
import requests
import pandas as pd
import time
import re
import numpy as np

Create Scraping Function

We’re going to start with creating a function that handles the scraping of each video page. We will be sending in the URL data, user agent object, and the crawl delay number. We now set the user agent in the header using the random function for the user agent module. This will randomize the user agent details like OS and browser. Next, we set the random delay time which we calculate later. A random script delay is essential for evading Google’s blocking tendencies.

def get_youtube_info(url,ua,crawl_delay):
  header = {"user-agent": ua.random}

Now we make the HTTP request using the url and header variables we set above. Then we create a BeautifulSoup object using the content from the video page. We then use the find_all() function of BeautifulSoup to put all the open graph meta tags with the property “og:video:tag” into a Python list. Then we use the find() function to get the text within the title tag.

request = requests.get(url,headers=header,verify=True)
soup = BeautifulSoup(request.content,"html.parser")
tags = soup.find_all("meta",property="og:video:tag")
titles = soup.find("title").text

Now we will attempt to get the video description content. YouTube pages are mostly rendered via Javascript, so you can’t easily search for the tags that end up rendered. When you make an HTTP request via Python the result is not rendered. It’s just the raw code. So to get the description contained in the Javascript we’ll need to use a little RegEx and afterward do a little cleanup. I also found that some videos didn’t have a description at all and ended up creating an error. So for that, we use a try/exception.

  getdesc ='description\":{\"simpleText\":\".*\"',request.text)
  desc =
  desc = desc.replace('description":{"simpleText":"','')
  desc = desc.replace('"','')
  desc = desc.replace('\n','')
  desc = "n/a"

Next, we do a little more RegEx to find the date and view data. Then we return the data we scraped back to the main script.

getdate ='[a-zA-z]{3}\s[0-9]{1,2},\s[0-9]{4}',request.text)
vid_date =

getviews ='[0-9,]+\sviews',request.text)
vid_views =

return tags,titles,vid_date,desc,views

Create Tag Match Function

The next phase is comparing the tag list to the description content and seeing what tags are in that content. One could imagine if there aren’t many matches that is an opportunity to fix. For this, we create a new function that accepts the description text and the tag list. At this point, our tag list is a comma delineated list and you’ll find out why later on, but for this function, we need to turn it back into a Python list, so we use split(). Then we loop through each tag in the list and use the string function find() which looks for the tag in the description. If the result is not “-1”, then we know there was a match and we add it to the matches list variable.

def tag_matches(desc, vid_tag_list):
  vid_tag_list = vid_tag_list.split(',')
  matches = ""

  for x in vid_tag_list:
    if (desc.find(x) != -1):
      matches += x + ","

  return matches

Import Data, Set Up Variables and Call Functions

Above we created the two main functions of the script. Now we actually start at the beginning. We first load the videos CSV containing the video URLs and convert the column into a list so we can easily loop through them.

df = pd.read_csv("videos.csv")
urls_list = df['urls'].to_list()

Next, we create our user agent function that we access and randomize in the YouTube scraper function above. Then we create our delay list. This list comprehension creates a list containing numbers ordered from 10 to 22, incrementing by 1 digit. Later on, we’ll use Numpy to randomly select from that list. I’ve found this ranged list to evade Google pretty well. Choose a digit less than 10 and you start getting blocked more often. Lastly, we create our empty dataframe which we’ll eventually store our scraped data.

 ua = UserAgent()
delays = [*range(10,22,1)]

df2 = pd.DataFrame(columns = ['URL', 'Title', 'Date', 'Views','Tags', 'Tag Matches in Desc'])

Now we want to loop through our URL list and start processing the pages and content. crawl_delay is set for each URL by using the np.random.choice() function to randomly select a value from the list. One URL might have a delay of 13, the next it may be 19. This denies any pattern Google could detect and looks more like a human.

for x in urls_list:
  crawl_delay = np.random.choice(delays)
  vid_tags,title,vid_date,desc,views = get_youtube_info(x,ua,crawl_delay)

  vid_tag_list = ""
  for i in vid_tags:
    vid_tag_list += i['content'] + ", "

Below is where we call the function to evaluate the tag to description matching. Then we clean up the page titles as YouTube adds their brand and that is unnecessary to include in our data. Once we’re done scraping and tag matching we fill out a dictionary and append it to the empty dataframe we added earlier. Lastly, we export the dataframe as a CSV for you to do further analysis.

  matches = tag_matches(desc,vid_tag_list)
  title = title.replace(' - YouTube','')

  dict1 = {'URL':x,'Title':title,'Date':vid_date,'Views':views,'Tags':vid_tag_list,'Tag Matches in Desc':matches}
  df2 = df2.append(dict1, ignore_index=True)



Note this dataframe example below is from my work’s YouTube channel with 2 videos 🙂

dataframe python


So there you have it! You can now scrape YouTube pages to help with your optimization efforts. With this framework, you can extend it in so many interesting directions. Now get out there and try it out! Follow me on Twitter and let me know your applications and ideas!

Greg Bernhardt
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