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Web scraping in 5 minutes :). - Towards Data Science

Web scraping in 5 minutes :). – Towards Data Science

To naive people like me, doing some web scraping it’s the most similar thing we’re going to find to feel world-level hackers, extracting some secret information from the government about it if you want, but web scraping can give you a dataset when you had nothing at all to work with. And of course, for most of the cases -we are going to talk about exceptions later…surprisingly, not everybody wants us sneaking around their webpage-, we can face the web-scraping-challenge by using just a few Python public libraries:‘requests’ calling ‘import requests’‘BeautifulSoup’ calling ‘from bs4 import BeautifulSoup’We may also want to use also ‘tqdm_notebook’ tool calling ‘from tqdm import tqdm_notebook’ in case we need to iterate through a site with several pages. For example, suppose we’re trying to extract some information from all the job posts in after searching ‘data scientist’. We’ll probably have several result pages. The ‘bs4’ library will allow us to go all over them in a very simple way. We could do this also using just a simple for loop, but the ‘tqdm_notebook’ tool provides visualization about the evolution of the process, what meshes quite nicely if we’re scraping hundreds or thousands of pages from a site in one runBut anyway, let’s go step by step. For this article, we’ll be scraping Penguin’s list of the 100 must-read classic books. And we’ll try to get only the title and the author for each one of them. This simple notebook and others are available in my GitHub profile (including the entire project about scraping indeed and running a classification model) we’re scraping a website, basically we’re making a request from Python and parsing through the HTML that is returned from each page. If you are wondering: what the hell is HTML? Or if you’re thinking: crap, I’m none web designer, I cannot deal with this! Don’t worry, you don’t need to know anything more than how to get the webpage HTML code and find whatever you are looking for. Remember, for the sake of this example, we’ll be looking for the title and author of each book:We can access the webpage HTML code by doing:Right-click > InspectOr either in Mac pressing Command+shift+cA window should be opened to the right of your screen. Be sure to be standing on the ‘Elements’ tab in the upper tab of the window:Just after opening this window, if you stand over any element of the webpage, the HTML code will move by its own until that specific section of the code. Look how if I move my pointer above the first book title (‘1. The Great Gatsby by F. Scott Fitzgerald’), the HTML highlights the elements belonging to that part of the webpage:In this case, the text seems to be contained inside ‘

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’. The

tag is nothing more than a container unit that encapsulates other page elements and divides the HTML document into sections. We may find our data under this or another tag. For example, the

  • tag is used to represent an item in a list, and the

    tag represents a level 2 heading in an HTML document (HTML includes 6 levels of headings, which are ranked by importance) is important to be understood because we’ll have to specify the tag under which our data is in order to be scrapped. But first, we’ll have to instantiate a ‘requests’ object. This will be a get, with the URL and optional parameters you’d like passed through the = ‘’r = (url)Now we made the request, we’ll have to create the BeautifulSoup object to pass our request text through its HTML parser:soup = BeautifulSoup(, ’’)In this case, we didn’t have any special parameter in our URL. But let’s go back to our previous example of scraping In that scenario, as we mentioned before, we’d have to use tqdm_notebook. This tool works as a for loop iterating through the different pages:for start in tqdm_notebook(range(0, 1000 10)): url = “}”(start) r = (url) soup = BeautifulSoup(, ’’)Pay attention to how we’re specifying to go from page 0 to 100, jumping from 10 to 10, and then inserting the ‘start’ parameter into the url by using start={}”(start). Amazing! We already have our HTML code in a nice format. Now let’s obtain our data! There are several ways of doing this, but for me, the neatest way is to write a simple function to be executed just after creating our BeautifulSoup eviously we saw that our data (the title + author info) was in the following location: ‘

    ’. So what we’ll have to do is dismantle that code sequence, in order to find all the ‘cmp-text text’ elements in the code. For that, we’ll use our BeautifulSoup object:for book in nd_all(‘div’, attrs={‘class’:’cmp-text text’})In this way, our soup object will go through all the code, finding all the ‘cmp-text text’ elements and giving us the content of each one of them. A good idea to understand what we’re getting on each iteration is to print the text calling ‘’. In this case, we’d see the following:1. Scott FitzgeraldThe greatest, most scathing dissection of the hollowness at the heart of the American dream. Hypnotic, tragic, both of its time and completely T, TwitterWhat we can see here, is that the text under our ‘cmp-text text’ element contains the title + author, but also the brief description of the book. We can solve this in several ways. For the sake of this example, I used regex calling ‘import re’. Regex is a vast topic by its own, but in this case, I used a very simple tool that always comes in handy and you can learn ttern = mpile(r’(? <=. ). +(? =\n)’)Using our ‘re’ object, we can create a pattern using the regex lookahead and lookbehind tools. Specifying (? <=behind_text) we can look for whatever follows our ‘match_text’. And with (? =ahead_text) we are indicating for anything followed by ‘match_text’, ’. Once regex found our code, we can tell it to give us anything that’s a number, only letters, special characters, some specific words or letter, or even a combination of all this. In our example, the ‘. +’ expression in the middle basically means ‘bring me anything’. So we’ll find everything in between ‘. ‘ (the point and the space after the book’s position in the list) and a ‘\n’, that’s a jump to the next we only have to pass our ‘re’ object the pattern, and the text:appender = ndall(pattern, text)This will give us a list, for example, with the following content:[‘The Great Gatsby by F. Scott Fitzgerald’]Easy peasy to solve calling the text in the list and splitting it at ‘ by ‘, to after store title and author in different lists. Let’s put everything together now in a function as we said before:def extract_books_from_result(soup): returner = {‘books’: [], ‘authors’: []} for book in nd_all(‘div’, attrs={‘class’:’cmp-text text’}): text = pattern = mpile(r’(? <=. +(? =\n)’) appender = ndall(pattern, text)[0](‘ by’) # Including a condition just to avoid anything that’s not a book # Each book should consist in a list with two elements: # [0] = title and [1] = author if len(appender) > 1: returner[‘books’](appender[0]) returner[‘authors’](appender[1]) returner_df = Frame(returner, columns=[‘books’, ’authors’]) return returner_dfAnd finally, let’s run all together:url = ‘’r = (url)soup = BeautifulSoup(, ’’)results = extract_books_from_result(soup)Congratulations! You’ve done your first scraping and you have all the data in a DataFrame. Now its up to you what you’ll we do with that;). Unfortunately, web scraping is not always that easy, since there are several web sites that don’t want us sneaking around. In some cases, they will have a very intricated HTML to avoid rookies, but in many, they will be blocking good people like us trying to obtain some decent data. There are several ways of not being detected, but sadly, this article has gone away from me and my promise of learning web scraping in five minutes. But don’t worry, I’ll be writing more about this in the near future. So stay tuned;)Finally, remember: this simple notebook and others are available in my GitHub profile (including the entire project about scraping indeed and running a classification model). Also, don’t forget to check out my last article about 6 amateur mistakes I’ve made working with train-test splits and more in my writer profile in Towards Data Science. And if you liked this article, don’t forget to follow me, and if you want to receive my latest articles directly on your email, just subscribe to my newsletter:)Thanks for reading!
    Is Web Scraping Easy? How to Scrape Data without Coding Skills

    Is Web Scraping Easy? How to Scrape Data without Coding Skills

    Mastering web scraping can be incredibly all, web scraping will give you instant access to valuable datasets such as competitor product details, stock prices, market data, you name it! However, web scraping might seem intimidating for some people. Specially if you’ve never done any coding in your ever, they are way simpler ways to automate your data gathering process without having to write a single line of is Web Scraping? As you may already know, web scraping refers to the extraction of data from a this can be done manually, most people will use a software tool to run their web scraping jobs. Unfortunately, many of these web scraping tools will still require custom coding from the terested in learning more about web scraping? Read our in-depth guide on web to Scrape Data without Coding SkillsLuckily, there are many web scraping tools that are made with ease-of-use in many in fact, that we have written a guide on what features make the best web scraping tool for your specific use obviously recommend ParseHub, a free and easy-to-use web scraper with the following features:User-friendly UI: ParseHub boasts a super friendly user interface. Load the website you’re looking to scrape data from and simply click on the data you’re looking to with any website: ParseHub works with any website, including modern dynamic sites that some web scrapers cannot scraping: ParseHub only runs on your computer to build your scrape jobs, the actual scraping occurs on the cloud. That means that ParseHub does not eat up at your device’s resources while running large scrape and JSON exports: Export your data as a CSV or JSON file, or take a step further and connect your scrape jobs to a Google Sheets to see it in action? Here’s our video guide on how to use ParseHub to scrape any website on to an excel spreadsheet:What can Web Scraping be used for? Now that you have tested out ParseHub and know how to scrape any website on to an excel spreadsheet, you might be wondering what you could use ParseHub ckily, we have written an in-depth guide on how companies use web scraping to boost their business you’re not yet ready to tackle a complex project, try these simple project ideas to get started with web scraping.
    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.

    Frequently Asked Questions about simple web scraper

    However, web scraping might seem intimidating for some people. Specially if you’ve never done any coding in your life. However, they are way simpler ways to automate your data gathering process without having to write a single line of code.Feb 10, 2020

    Let’s get started!Step 1: Find the URL that you want to scrape. For this example, we are going scrape Flipkart website to extract the Price, Name, and Rating of Laptops. … Step 3: Find the data you want to extract. … Step 4: Write the code. … Step 5: Run the code and extract the data. … Step 6: Store the data in a required format.Sep 24, 2021

    It is perfectly legal if you scrape data from websites for public consumption and use it for analysis. However, it is not legal if you scrape confidential information for profit. For example, scraping private contact information without permission, and sell them to a 3rd party for profit is illegal.Aug 16, 2021

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