Scraping Videos Using Python

How to Scrape Videos using Python ? – GeeksforGeeks

Prerequisite:requestsBeautifulSoupIn this article, we will discuss web scraping of videos using python. For web scraping, we will use requests and BeautifulSoup Module in Python. The requests library is an integral part of Python for making HTTP requests to a specified URL. Whether it be REST APIs or Web Scrapping, requests are must be learned for proceeding further with these technologies. When one makes a request to a URI, it returns a response. Python requests provide inbuilt functionalities for managing both the request and install requestsBeautiful Soup is a Python library designed for quick turnaround projects like install bs4Let’s Understand Step by step implementation:Import Required ModulePython3import requests from bs4 import BeautifulSoupParse HTML ContentPython3Web_url = “Enter WEB URL”r = (Web_url) soup = BeautifulSoup(ntent, ‘html5lib’)Count How many videos are there on the web page. In HTML For displaying video, we use video thon3video_tags = ndAll(‘video’) print(“Total “, len(video_tags), “videos found”)Iterate through all video tags and fetch video URLPython3for video_tag in video_tags: video_url = (“a”)[‘href’] print(video_url)Below is the Implementation:Python3import requestsfrom bs4 import BeautifulSoupr = (Web_url)soup = BeautifulSoup(ntent, ”)video_tags = ndAll(‘video’)print(“Total “, len(video_tags), “videos found”)if len(video_tags)! = 0: for video_tag in video_tags: video_url = (“a”)[‘href’] print(video_url)else: print(“no videos found”)Output:Total 1 videos found
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How To Scrape Youtube Video Views With Python | Honchō

How To Scrape Youtube Video Views With Python | Honchō

Jack Minot – 20 May 2021 – SEO
Getting Views From Youtube Videos
This guide will show you how to scrape video views with Python with a primary focus on either scraping a single video or a number of videos. While this is fairly easy to do thanks to the python file we’ll be using, there are few parts that may require a bit more slightly more knowledge.
We are using an existing Python file created by Python Engineer. I’d highly recommend the Youtube tutorial which covers this file in greater detail. All credit to him for creating this Python file and making all our lives much easier! Link to the Youtube Playlist.
Overview of the process
You will need an API key from
You will also need both the Video ID and Channel ID ideally in a list or Data Frame enabling us to iterate over the values
Installation of pandas
The following Python file from Python Engineer:
Prerequisites and set-up used
Python Version: 3. 9. 4
IDE: Visual Studio Code
Libraries: Pandas, TQDM & yt_stats
Google Cloud: Youtube Data API V3
OS: Big Sur – 11. 3. 1
How to Get Video & Channel IDs
The channel and Video IDs can be identified by the URL from Youtube.
The channel URL is, with the string “marksandspencertv” being the Channel ID.
The Video ID for M&S Christmas’s TV Ad from 2017 is, with the “KfaSxIkLslE” being the Video ID.
Step 1: Adding your Channel & Video ID to a Dataframe
In my example, I’ll be using a data frame from an Excel File, with the 3 columns, the first being a Channel ID and the 2nd being a Video ID. For demonstration purposes, I have labelled the column names “Channel ID” and “Video ID”.
File Name:
Column 1: “Title”
Column 2: “Video ID”
Column 3: “Channel ID”
Step 2: Install & Import Files
Save the file in the directory of your script
Import Pandas, yt_stats and tqdm
import pandas
from yt_stats import YTstats
from tqdm import tqdm
Step 3: Add your API Key
Add your API key to the API_KEY Object.
API_KEY = ‘#####Your API KEY#####’
Step 4: Read your Excel/CSV file and create an empty list
Read “” and add to the “df” object. Then we’ll create an empty list where we’ll append the view data, later on.
df = ad_excel(“”)
Views =[]
Step 5: Create our script which will retrieve the view data.
The following script will use the Channel ID and Video ID to locate the video, then using the Youtube Data API V3 will retrieve the video “Statistics”, from that taking the “viewCount”.
It’s worth noting that depending on your Data Frame headings you may need to alter the script for the correct references.
for x, y in tqdm(zip(df[“Video ID”], df[“Channel ID”])):
channel_id = y
video_id = x
part = ‘statistics’
yt = YTstats(API_KEY, channel_id)
a = yt. _get_single_video_data(video_id, part)
Step 6: Retrieving our data
The data will now be in the view list. Using Pandas we can add that to our existing data frame and you can output the file to a CSV, Excel or JSON file.
df[“views”] = Views
Output & notes
Once saved to excel you should something matching the following.
The Youtube Data API V3 has a quota of 5, 000 queries a day. If you are planning a large scale project, you’ll have to look at paid options.
More information on the Youtube Data API V3 can be found here:
Full Code:
import pandas as pd
API_KEY = ‘#####API KEY#####’
Is Web Scraping Illegal? Depends on What the Meaning of the Word Is

Is Web Scraping Illegal? Depends on What the Meaning of the Word Is

Depending on who you ask, web scraping can be loved or hated.
Web scraping has existed for a long time and, in its good form, it’s a key underpinning of the internet. “Good bots” enable, for example, search engines to index web content, price comparison services to save consumers money, and market researchers to gauge sentiment on social media.
“Bad bots, ” however, fetch content from a website with the intent of using it for purposes outside the site owner’s control. Bad bots make up 20 percent of all web traffic and are used to conduct a variety of harmful activities, such as denial of service attacks, competitive data mining, online fraud, account hijacking, data theft, stealing of intellectual property, unauthorized vulnerability scans, spam and digital ad fraud.
So, is it Illegal to Scrape a Website?
So is it legal or illegal? Web scraping and crawling aren’t illegal by themselves. After all, you could scrape or crawl your own website, without a hitch.
Startups love it because it’s a cheap and powerful way to gather data without the need for partnerships. Big companies use web scrapers for their own gain but also don’t want others to use bots against them.
The general opinion on the matter does not seem to matter anymore because in the past 12 months it has become very clear that the federal court system is cracking down more than ever.
Let’s take a look back. Web scraping started in a legal grey area where the use of bots to scrape a website was simply a nuisance. Not much could be done about the practice until in 2000 eBay filed a preliminary injunction against Bidder’s Edge. In the injunction eBay claimed that the use of bots on the site, against the will of the company violated Trespass to Chattels law.
The court granted the injunction because users had to opt in and agree to the terms of service on the site and that a large number of bots could be disruptive to eBay’s computer systems. The lawsuit was settled out of court so it all never came to a head but the legal precedent was set.
In 2001 however, a travel agency sued a competitor who had “scraped” its prices from its Web site to help the rival set its own prices. The judge ruled that the fact that this scraping was not welcomed by the site’s owner was not sufficient to make it “unauthorized access” for the purpose of federal hacking laws.
Two years later the legal standing for eBay v Bidder’s Edge was implicitly overruled in the “Intel v. Hamidi”, a case interpreting California’s common law trespass to chattels. It was the wild west once again. Over the next several years the courts ruled time and time again that simply putting “do not scrape us” in your website terms of service was not enough to warrant a legally binding agreement. For you to enforce that term, a user must explicitly agree or consent to the terms. This left the field wide open for scrapers to do as they wish.
Fast forward a few years and you start seeing a shift in opinion. In 2009 Facebook won one of the first copyright suits against a web scraper. This laid the groundwork for numerous lawsuits that tie any web scraping with a direct copyright violation and very clear monetary damages. The most recent case being AP v Meltwater where the courts stripped what is referred to as fair use on the internet.
Previously, for academic, personal, or information aggregation people could rely on fair use and use web scrapers. The court now gutted the fair use clause that companies had used to defend web scraping. The court determined that even small percentages, sometimes as little as 4. 5% of the content, are significant enough to not fall under fair use. The only caveat the court made was based on the simple fact that this data was available for purchase. Had it not been, it is unclear how they would have ruled. Then a few months back the gauntlet was dropped.
Andrew Auernheimer was convicted of hacking based on the act of web scraping. Although the data was unprotected and publically available via AT&T’s website, the fact that he wrote web scrapers to harvest that data in mass amounted to “brute force attack”. He did not have to consent to terms of service to deploy his bots and conduct the web scraping. The data was not available for purchase. It wasn’t behind a login. He did not even financially gain from the aggregation of the data. Most importantly, it was buggy programing by AT&T that exposed this information in the first place. Yet Andrew was at fault. This isn’t just a civil suit anymore. This charge is a felony violation that is on par with hacking or denial of service attacks and carries up to a 15-year sentence for each charge.
In 2016, Congress passed its first legislation specifically to target bad bots — the Better Online Ticket Sales (BOTS) Act, which bans the use of software that circumvents security measures on ticket seller websites. Automated ticket scalping bots use several techniques to do their dirty work including web scraping that incorporates advanced business logic to identify scalping opportunities, input purchase details into shopping carts, and even resell inventory on secondary markets.
To counteract this type of activity, the BOTS Act:
Prohibits the circumvention of a security measure used to enforce ticket purchasing limits for an event with an attendance capacity of greater than 200 persons.
Prohibits the sale of an event ticket obtained through such a circumvention violation if the seller participated in, had the ability to control, or should have known about it.
Treats violations as unfair or deceptive acts under the Federal Trade Commission Act. The bill provides authority to the FTC and states to enforce against such violations.
In other words, if you’re a venue, organization or ticketing software platform, it is still on you to defend against this fraudulent activity during your major onsales.
The UK seems to have followed the US with its Digital Economy Act 2017 which achieved Royal Assent in April. The Act seeks to protect consumers in a number of ways in an increasingly digital society, including by “cracking down on ticket touts by making it a criminal offence for those that misuse bot technology to sweep up tickets and sell them at inflated prices in the secondary market. ”
In the summer of 2017, LinkedIn sued hiQ Labs, a San Francisco-based startup. hiQ was scraping publicly available LinkedIn profiles to offer clients, according to its website, “a crystal ball that helps you determine skills gaps or turnover risks months ahead of time. ”
You might find it unsettling to think that your public LinkedIn profile could be used against you by your employer.
Yet a judge on Aug. 14, 2017 decided this is okay. Judge Edward Chen of the U. S. District Court in San Francisco agreed with hiQ’s claim in a lawsuit that Microsoft-owned LinkedIn violated antitrust laws when it blocked the startup from accessing such data. He ordered LinkedIn to remove the barriers within 24 hours. LinkedIn has filed to appeal.
The ruling contradicts previous decisions clamping down on web scraping. And it opens a Pandora’s box of questions about social media user privacy and the right of businesses to protect themselves from data hijacking.
There’s also the matter of fairness. LinkedIn spent years creating something of real value. Why should it have to hand it over to the likes of hiQ — paying for the servers and bandwidth to host all that bot traffic on top of their own human users, just so hiQ can ride LinkedIn’s coattails?
I am in the business of blocking bots. Chen’s ruling has sent a chill through those of us in the cybersecurity industry devoted to fighting web-scraping bots.
I think there is a legitimate need for some companies to be able to prevent unwanted web scrapers from accessing their site.
In October of 2017, and as reported by Bloomberg, Ticketmaster sued Prestige Entertainment, claiming it used computer programs to illegally buy as many as 40 percent of the available seats for performances of “Hamilton” in New York and the majority of the tickets Ticketmaster had available for the Mayweather v. Pacquiao fight in Las Vegas two years ago.
Prestige continued to use the illegal bots even after it paid a $3. 35 million to settle New York Attorney General Eric Schneiderman’s probe into the ticket resale industry.
Under that deal, Prestige promised to abstain from using bots, Ticketmaster said in the complaint. Ticketmaster asked for unspecified compensatory and punitive damages and a court order to stop Prestige from using bots.
Are the existing laws too antiquated to deal with the problem? Should new legislation be introduced to provide more clarity? Most sites don’t have any web scraping protections in place. Do the companies have some burden to prevent web scraping?
As the courts try to further decide the legality of scraping, companies are still having their data stolen and the business logic of their websites abused. Instead of looking to the law to eventually solve this technology problem, it’s time to start solving it with anti-bot and anti-scraping technology today.
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Frequently Asked Questions about scraping videos using python

How do you scrape a YouTube video in Python?

How To Scrape Youtube Video Views With PythonOverview of the process.Step 1: Adding your Channel & Video ID to a Dataframe.Step 2: Install & Import Files.Step 3: Add your API Key.Step 4: Read your Excel/CSV file and create an empty list.Step 5: Create our script which will retrieve the view data.More items…•May 20, 2021

Is Python scraping legal?

Web scraping and crawling aren’t illegal by themselves. After all, you could scrape or crawl your own website, without a hitch. … Big companies use web scrapers for their own gain but also don’t want others to use bots against them.

How do I download a video from a website using python?

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