Extract Data From Website Using Python

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A Beginner's Guide to learn web scraping with python! - Edureka

A Beginner’s Guide to learn web scraping with python! – Edureka

Last updated on Sep 24, 2021 641. 9K Views Tech Enthusiast in Blockchain, Hadoop, Python, Cyber-Security, Ethical Hacking. Interested in anything… Tech Enthusiast in Blockchain, Hadoop, Python, Cyber-Security, Ethical Hacking. Interested in anything and everything about Computers. 1 / 2 Blog from Web Scraping Web Scraping with PythonImagine you have to pull a large amount of data from websites and you want to do it as quickly as possible. How would you do it without manually going to each website and getting the data? Well, “Web Scraping” is the answer. Web Scraping just makes this job easier and faster. In this article on Web Scraping with Python, you will learn about web scraping in brief and see how to extract data from a website with a demonstration. I will be covering the following topics: Why is Web Scraping Used? What Is Web Scraping? Is Web Scraping Legal? Why is Python Good For Web Scraping? How Do You Scrape Data From A Website? Libraries used for Web Scraping Web Scraping Example: Scraping Flipkart Website Why is Web Scraping Used? Web scraping is used to collect large information from websites. But why does someone have to collect such large data from websites? To know about this, let’s look at the applications of web scraping: Price Comparison: Services such as ParseHub use web scraping to collect data from online shopping websites and use it to compare the prices of products. Email address gathering: Many companies that use email as a medium for marketing, use web scraping to collect email ID and then send bulk emails. Social Media Scraping: Web scraping is used to collect data from Social Media websites such as Twitter to find out what’s trending. Research and Development: Web scraping is used to collect a large set of data (Statistics, General Information, Temperature, etc. ) from websites, which are analyzed and used to carry out Surveys or for R&D. Job listings: Details regarding job openings, interviews are collected from different websites and then listed in one place so that it is easily accessible to the is Web Scraping? Web scraping is an automated method used to extract large amounts of data from websites. The data on the websites are unstructured. Web scraping helps collect these unstructured data and store it in a structured form. There are different ways to scrape websites such as online Services, APIs or writing your own code. In this article, we’ll see how to implement web scraping with python. Is Web Scraping Legal? Talking about whether web scraping is legal or not, some websites allow web scraping and some don’t. To know whether a website allows web scraping or not, you can look at the website’s “” file. You can find this file by appending “/” to the URL that you want to scrape. For this example, I am scraping Flipkart website. So, to see the “” file, the URL is in-depth Knowledge of Python along with its Diverse Applications Why is Python Good for Web Scraping? Here is the list of features of Python which makes it more suitable for web scraping. Ease of Use: Python is simple to code. You do not have to add semi-colons “;” or curly-braces “{}” anywhere. This makes it less messy and easy to use. Large Collection of Libraries: Python has a huge collection of libraries such as Numpy, Matlplotlib, Pandas etc., which provides methods and services for various purposes. Hence, it is suitable for web scraping and for further manipulation of extracted data. Dynamically typed: In Python, you don’t have to define datatypes for variables, you can directly use the variables wherever required. This saves time and makes your job faster. Easily Understandable Syntax: Python syntax is easily understandable mainly because reading a Python code is very similar to reading a statement in English. It is expressive and easily readable, and the indentation used in Python also helps the user to differentiate between different scope/blocks in the code. Small code, large task: Web scraping is used to save time. But what’s the use if you spend more time writing the code? Well, you don’t have to. In Python, you can write small codes to do large tasks. Hence, you save time even while writing the code. Community: What if you get stuck while writing the code? You don’t have to worry. Python community has one of the biggest and most active communities, where you can seek help Do You Scrape Data From A Website? When you run the code for web scraping, a request is sent to the URL that you have mentioned. As a response to the request, the server sends the data and allows you to read the HTML or XML page. The code then, parses the HTML or XML page, finds the data and extracts it. To extract data using web scraping with python, you need to follow these basic steps: Find the URL that you want to scrape Inspecting the Page Find the data you want to extract Write the code Run the code and extract the data Store the data in the required format Now let us see how to extract data from the Flipkart website using Python, Deep Learning, NLP, Artificial Intelligence, Machine Learning with these AI and ML courses a PG Diploma certification program by NIT braries used for Web Scraping As we know, Python is has various applications and there are different libraries for different purposes. In our further demonstration, we will be using the following libraries: Selenium: Selenium is a web testing library. It is used to automate browser activities. BeautifulSoup: Beautiful Soup is a Python package for parsing HTML and XML documents. It creates parse trees that is helpful to extract the data easily. Pandas: Pandas is a library used for data manipulation and analysis. It is used to extract the data and store it in the desired format. Subscribe to our YouTube channel to get new updates..! Web Scraping Example: Scraping Flipkart WebsitePre-requisites: Python 2. x or Python 3. x with Selenium, BeautifulSoup, pandas libraries installed Google-chrome browser Ubuntu Operating SystemLet’s get started! Step 1: Find the URL that you want to scrapeFor this example, we are going scrape Flipkart website to extract the Price, Name, and Rating of Laptops. The URL for this page is 2: Inspecting the PageThe data is usually nested in tags. So, we inspect the page to see, under which tag the data we want to scrape is nested. To inspect the page, just right click on the element and click on “Inspect” you click on the “Inspect” tab, you will see a “Browser Inspector Box” 3: Find the data you want to extractLet’s extract the Price, Name, and Rating which is in the “div” tag respectively. Learn Python in 42 hours! Step 4: Write the codeFirst, let’s create a Python file. To do this, open the terminal in Ubuntu and type gedit with extension. I am going to name my file “web-s”. Here’s the command:gedit, let’s write our code in this file. First, let us import all the necessary libraries:from selenium import webdriver
from BeautifulSoup import BeautifulSoup
import pandas as pdTo configure webdriver to use Chrome browser, we have to set the path to chromedriverdriver = (“/usr/lib/chromium-browser/chromedriver”)Refer the below code to open the URL: products=[] #List to store name of the product
prices=[] #List to store price of the product
ratings=[] #List to store rating of the product
(“)
Now that we have written the code to open the URL, it’s time to extract the data from the website. As mentioned earlier, the data we want to extract is nested in

tags. So, I will find the div tags with those respective class-names, extract the data and store the data in a variable. Refer the code below:content = ge_source
soup = BeautifulSoup(content)
for a in ndAll(‘a’, href=True, attrs={‘class’:’_31qSD5′}):
(‘div’, attrs={‘class’:’_3wU53n’})
(‘div’, attrs={‘class’:’_1vC4OE _2rQ-NK’})
(‘div’, attrs={‘class’:’hGSR34 _2beYZw’})
()
Step 5: Run the code and extract the dataTo run the code, use the below command: python 6: Store the data in a required formatAfter extracting the data, you might want to store it in a format. This format varies depending on your requirement. For this example, we will store the extracted data in a CSV (Comma Separated Value) format. To do this, I will add the following lines to my code:df = Frame({‘Product Name’:products, ‘Price’:prices, ‘Rating’:ratings})
_csv(”, index=False, encoding=’utf-8′)Now, I’ll run the whole code again. A file name “” is created and this file contains the extracted data. I hope you guys enjoyed this article on “Web Scraping with Python”. I hope this blog was informative and has added value to your knowledge. Now go ahead and try Web Scraping. Experiment with different modules and applications of Python. If you wish to know about Web Scraping With Python on Windows platform, then the below video will help you understand how to do Scraping With Python | Python Tutorial | Web Scraping Tutorial | EdurekaThis Edureka live session on “WebScraping using Python” will help you understand the fundamentals of scraping along with a demo to scrape some details from a question regarding “web scraping with Python”? You can ask it on edureka! Forum and we will get back to you at the earliest or you can join our Python Training in Hobart get in-depth knowledge on Python Programming language along with its various applications, you can enroll here for live online Python training with 24/7 support and lifetime access.
Downloading files from web using Python? - Tutorialspoint

Downloading files from web using Python? – Tutorialspoint

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Python provides different modules like urllib, requests etc to download files from the web. I am going to use the request library of python to efficiently download files from the ’s start a look at step by step procedure to download files using URLs using request library−1. Import moduleimport requests2. Get the link or urlurl = ”
r = (url, allow_redirects=True)3. Save the content with (”, ‘wb’)(ntent)save the file as requests
url = ”
r = (url, allow_redirects=True)
open(”, ‘wb’)(ntent)ResultWe can see the file is downloaded(icon) in our current working we may need to download different kind of files like image, text, video etc from the web. So let’s first get the type of data the url is linking to−>>> r = (url, allow_redirects=True)
>>> print(r. (‘content-type’))
image/pngHowever, there is a smarter way, which involved just fetching the headers of a url before actually downloading it. This allows us to skip downloading files which weren’t meant to be downloaded. >>> print(is_downloadable(”))
False
>>> print(is_downloadable(”))
TrueTo restrict the download by file size, we can get the filezie from the content-length header and then do as per our ntentLength = (‘content-length’, None)
if contentLength and contentLength > 2e8: # 200 mb approx
return FalseGet filename from an URLTo get the filename, we can parse the url. Below is a sample routine which fetches the last string after backslash(/) ”
if (‘/’):
print((‘/’, 1)[1]Above will give the filename of the url. However, there are many cases where filename information is not present in the url for example –. In such a case, we need to get the Content-Disposition header, which contains the filename requests
import re
def getFilename_fromCd(cd):
“””
Get filename from content-disposition
if not cd:
return None
fname = ndall(‘filename=(. +)’, cd)
if len(fname) == 0:
return fname[0]
filename = getFilename_fromCd(r. (‘content-disposition’))
open(filename, ‘wb’)(ntent)The above url-parsing code in conjunction with above program will give you filename from Content-Disposition header most of the time.
Published on 02-May-2019 12:00:00
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Pulling Data from the Web: How to Get Data from a Website | Import.io

Pulling Data from the Web: How to Get Data from a Website | Import.io

The value of web data is increasing in every industry from retail competitive price monitoring to alternative data for investment research. Getting that data from a website is vital to the success of your business. As the trusted research firm, Gartner, stated in their blog:
“Your company’s biggest database isn’t your transaction, CRM, ERP or other internal database. Rather it’s the Web itself…Treat the Internet itself as your organization’s largest data source. ”
In fact, the internet is the largest source of business data on earth and it’s growing by the minute. The infograph below from Domo shows how much web data is created every minute from just a few websites out of a billion.
Source Domo
It’s clear the need for web data integration is greater than ever. This article will walk you through a simple process of pulling data from a webpage using data extraction software. First, let’s look at other uses of web data in business.
How do businesses use data from a website?
Competitive price comparison and alternative data for equity research are two popular uses of website data, but there are others less obvious.
Here are a few examples:
Teaching Movie Studios how to spot a hit manuscript
For StoryFit, data is the fuel that powers its predictive analytic engines. StoryFit’s artificial intelligence and machine learning algorithms are trained using vast amounts of data culled from a variety of sources, including extractors. This data contributes to StoryFit’s core NLP-focused AI to train machine learning models to determine what makes a hit movie.
Predicative Shipping Logistics
ClearMetal is a Predictive Logistics company using data science to unlock unprecedented efficiencies for global trade. They are using web data to mine all container and shipping information in the world then feed predictions back to companies that run terminals.
Market Intelligence
XiKO provides market intelligence around what consumers say online about brands and products. This information allows marketers to increase the efficacy of their programs and advertising. The key to XiKO’s success lies in its ability to apply linguistic modeling to vast amounts of data collected from websites.
Data-driven Marketing
Virtuance uses web data to review listing information from real estate sites to determine which listings need professional marketing and photography. From this data, Virtuance determines who needs their marketing services and develops success metrics based on the aggregated data.
Now that you have some examples of what companies are doing with web data, below are the steps that will show you how to pull data from a website.
Steps to get data from a website
Websites are built for human consumption, not machine. So it’s not always easy to get web data into a spreadsheet for analysis or machine learning. Copying and pasting information from websites is time-consuming, error-prone and not feasible.
Web scraping is a way to get data from a website by sending a query to the requested page, then combing through the HTML for specific items and organizing the data. If you don’t have an engineer on hand, provides a no-coding, point and click web data extraction platform that makes it easy to get web data.
Here’s a quick tutorial on how it works:
Step 1. First, find the page where your data is located. For instance, a product page on
Step 1. First, find the page where your data is located.
Step 2. Copy and paste the URL from that page into, to create an extractor that will attempt to get the right data.
Step 2. Copy and paste the URL from that page into
Step 3. Click Go and will query the page and use machine learning to try to determine what data you want.
Step 4. Once it’s done, you can decide if the extracted data is what you need. In this case, we want to extract the images as well as the product names and prices into columns. We trained the extractor by clicking on the top three items in each column, which then outlines all items belonging to that column in green.
Step 4. Once it’s done, you can decide if the extracted data is what you need.
Step 5. then populates the rest of the column for the product names and prices.
Step 6. Next, click on Extract data from website.
Step 7. has detected that the product listing data spans more than one page, so you can add as many pages as needed to ensure that you get every product in this category into your spreadsheet.
Step 8. Now, you can download the images, product names, and prices.
Step 9. First, download the product name and price into an Excel spreadsheet.
Step 10. Next, download the images as files to use to populate your own website or marketplace.
What else can you do with web scraping?
This is a very simple look at getting a basic list page of data into a spreadsheet and the images into a Zip folder of image files.
There’s much more you can do, such as:
Link this listing page to data contained on the detail pages for each product.
Schedule a change report to run daily to track when prices change or items are removed or added to the category.
Compare product prices on Amazon to other online retailers, such as Walmart, Target, etc.
Visualize the data in charts and graphs using Insights.
Feed this data into your internal processes or analysis tools via the APIs.
Web scraping is a powerful, automated way to get data from a website. If your data needs are massive or your websites trickier, offers data as a service and we will get your web data for you.
No matter what or how much web data you need, can help. We offer the world’s only web data integration platform which not only extracts data from a website, it identifies, prepares, integrates, and consumes it. This platform can meet an organization’s consumption needs for business applications, analytics, and other processes. You can start by talking to a data expert to determine the best solution for your data needs, or you can give the platform a try yourself. Sign up for a free seven day trial, or we’ll handle all the work for you.

Frequently Asked Questions about extract data from website using python

How do I download data from a website using python?

Downloading files from web using Python?Import module. import requests.Get the link or url. url = ‘https://www.facebook.com/favicon.ico’ r = requests.get(url, allow_redirects=True)Save the content with name. open(‘facebook.ico’, ‘wb’).write(r.content) … Get filename from an URL. To get the filename, we can parse the url.May 2, 2019

How can I extract data from a website?

Steps to get data from a websiteFirst, find the page where your data is located. … Copy and paste the URL from that page into Import.io, to create an extractor that will attempt to get the right data. … Click Go and Import.io will query the page and use machine learning to try to determine what data you want.More items…•Aug 9, 2018

Which of these methods is used to extract a webpage in python?

In simple words, BeautifulSoup is a Python library for pulling data out of HTML and XML files. It can be used with requests, because it needs an input (document or url) to create a soup object asit cannot fetch a web page by itself. You can use the following Python script to gather the title of web page and hyperlinks.

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