Web scrap is a concept that refers to the method by which large quantities of a web data set are extracted and processed utilizing a computer or algorithms. Discharging network data is a good ability if you are a data analyst, programmer, or somebody who evaluates large amounts of data. Suppose that you find the information you download directly on the Internet. Web scrapping is a sort of data scrapping known as online data extraction to collect data from online sites. WebScrapping applications can locate WWW via HTTP or the internet browser. The WebScrapping process is to find and store information on a webpage. We can use Python-based scrapping for extracting data in a useful format that can be accessed from the internet. If a web scraping script is running, the application is sent to a particular URL. In response to our message, the server sends information, which allows us to access both HTML and the XML section. Therefore, code scans, locate, and extracts data from both HTML and XML sections. Most residents cut/paste website data physically. However, it would not be possible to do so on massive websites with thousands of pages. In this situation, web scraping is helpful.
Web scraping appears to be an efficient and timely way to extract data from WWW. Web scraping lets us download any database content to our machine, no matter how large these datasets are.
We must take such easy actions to analyze information utilizing site scraping through Python:
1. Locate a URL we intend to scrape.
2. Examining Page
3. Locate the information you intend to collect.
4. Make code.
5. Execute code to get results.
6. Save data within an appropriate format.
So how is Python so Strong Web Scraping Language?
Python is a powerful OOP language that we can take advantage of in a variety of ways. The programming language is a general one. The Python code is easy to read and understand. In the automated system, it is too much important. To render functions understandable, it employs a basic syntax. Stateless operations of Python also prevent any interruptions.
The following consists of some Python characteristics that make Python more appropriate in web scraping.
- Python is an easy language to program in. Its syntax is so simple, which makes it far easier to get and less messy.
- Library Sets: Python has vast Library resources that contain methodologies for a variety of purposes. As a result, it is perfect for database crawling and more manipulation with data.
- Adaptively coded: We would not have to specify data types with variables throughout Python. We can only use them anywhere they are needed. It saves time or speeds up our work.
- Python is easy to understand because it is very similar to writing a statement in the English language to understand a Python file. Python’s incision helps a user distinguish among various scopes/blocks throughout code, making it descriptive and readable.
- Light code, great work: Web scraping is a time-saving technology. Python can write simple code to perform huge tasks. It allows us to keep time even when coding.
- It’s Position: How if you run into difficulties when coding? We do not have to be concerned. Python ecosystem is the largest or most popular in the world.
Since we all understand, Python seems to have a wide range of programs and libraries of multiple uses. Following libraries are included in our next protest:
Selenium: Selenium is an open-source web testing framework. It is a program that automates browser functions. Selenium is a web-based platform that is fully accessible. Selenium is used in conjunction with Python for analysis. It is much less wordy than any computer language and seems to be very simple to utilize. Python APIs allow us to use Selenium to bind to a browser. Significant variations with browser architecture, Selenium may submit python Language command with various browsers. Selenium is Python-compatible. So, it could be used like Selenium WebDriver within Python besides training. Then there is a question of whether
Python is also used for Selenium.
The following are among the main reason:
- It is simple to program and interpret.
- When comparing two programming languages, it executed very quickly.
- Python Provides a colorful typing environment for users.
- Python is a programming language that a large proportion of developers are familiar with it.
- API for Python makes it possible to bind to a browser via Selenium. Python-Selenium linking provides a convenient API for writing practice tests using its WebDriver in such a thoughtful manner.
- Python API allows us to use Selenium to link to the browser. Selenium may quickly submit basic Python instructions to a different browser, regardless of their differences.
Beautiful Soup: Beautiful Soup is a Python service that allows you to parse HTML and XML files. It can generate parse trees, which are useful for extracting data. BeautifulSoup is a Python library for extracting data from scripting languages. We come across few websites which show data related to our studies, like dates or addresses, but do not allow us to download data directly. BeautifulSoup manages to extract data from a website, strip away Html, to save data. It is a web scraping app that allows us to clean up or parse documents you have downloaded from WWW.
- BeautifulSoup can show us everything about functioning, separating names or links for removing all the texts from HTML elements changing HTML inside a document. BeautifulSoup (bs4) is the only parser, but it is a good version. Selenium has browser automation, and beautiful soup is a parser. You could mix two if you are searching towards HTML and XML components within the end, which bs4 excels at, so you need to browse via repeatedly identical websites, which Selenium excels. And we combine two Beautiful soups and Selenium to get a great outcome. BeautifulSoup developed for quick turnaround tasks such as scrapping or extraction of information through mounted pages. There are three significant features:
- You can use a few standard techniques or Python phrases to navigate, search and modify a parsing tree. It is essentially a toolkit for analyzing and gathering the documents needed.
- Transforms received data in Unicode while exported data in UTF-8 implicitly.
- Sitting on the top of standard Python parsers like lxml and html5lib we can experiment with different parsing methods or with mobility swapping rates if necessary.
It scrapes data through XML and HTML, lxml looks close like Beautiful Soup. lxml is feature-rich and a convenient library of editing XML, HTML through Python language, as per official documentation. Except for Beautiful Soup, though, lxml’s metadata was not as user-friendly.
In this case, we will scrap the data from the website using both the BeautifulSoup and Selenium to create a CSV file.
Step one:
Install bs4 and Selenium:
1 2 3 | pip install bs4 pip install selenium |
Import these Libraries in the script:
1 2 3 4 5 6 | from selenium import webdriver from selenium.webdriver.support.ui import WebDriverWait from bs4 import BeautifulSoup as BS from time import sleep import csv import lxml |
Create driver for the scraping, I have used Chrome:
1 2 3 4 5 6 7 | # Create a Chrome driver and crawl the URL driver = webdriver.Chrome() driver.get('https://www.mcdonalds.com/de/de-de/restaurant-suche.html/l/berlin') # print the title of the driver print(driver.title) # print the current URL of the site print(driver.current_url) |
Print the URL of our Driver:
1 2 3 4 5 6 7 | sleep(10) # give time for all javascripts to be finished running page = driver.page_source soup = BS(page, "lxml") # Find restaurant list content = soup.find('div', class_='uberfinder') Allrestaurant = content.find_all('div', class_='ubsf_sitemap-location-address') |
Scrap the List of Restaurants and print it:
1 2 | #print the List of selected restaurants print(Allrestaurant) |
Make the CSV file of the Scraped data:
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 | # Make CSV file with open('Macdonal.csv', mode='w', newline='') as outputFile: MacdonalCSV = csv.writer(outputFile, delimiter=',', quotechar='"', quoting=csv.QUOTE_MINIMAL) MacdonalCSV.writerow(['Macdonal', 'S', 'Code', 'Area', 'state']) RN = 'McDonalds' C = 'Germany' CT = 'Berlin' for restaurant in Allrestaurant: S = restaurant.text.split(",")[0] ZC = restaurant.text.split(",")[1][1:6] MacdonalCSV.writerow([RN, S, ZC, CT, C]) print(MacdonalCSV) driver.close() print(driver) |
Source file:
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 | from selenium import webdriver from selenium.webdriver.support.ui import WebDriverWait from bs4 import BeautifulSoup as BS from time import sleep import csv import lxml # Create a Chrome driver and crawl the URL driver = webdriver.Chrome() driver.get('https://www.mcdonalds.com/de/de-de/restaurant-suche.html/l/berlin') # print the title of the driver print(driver.title) # print the current URL of the site print(driver.current_url) sleep(10) # give time for all javascripts to be finished running page = driver.page_source soup = BS(page, "lxml") # Find restaurant list content = soup.find('div', class_='uberfinder') Allrestaurant = content.find_all('div', class_='ubsf_sitemap-location-address') #print the List of selected restaurants print(Allrestaurant) # Make CSV file with open('Macdonal.csv', mode='w', newline='') as outputFile: MacdonalCSV = csv.writer(outputFile, delimiter=',', quotechar='"', quoting=csv.QUOTE_MINIMAL) MacdonalCSV.writerow(['Macdonal', 'S', 'Code', 'Area', 'state']) RN = 'McDonalds' C = 'Germany' CT = 'Berlin' for restaurant in Allrestaurant: S = restaurant.text.split(",")[0] ZC = restaurant.text.split(",")[1][1:6] MacdonalCSV.writerow([RN, S, ZC, CT, C]) print(MacdonalCSV) driver.close() print(driver) |
Cover photo credit: medium.com
