In this simple tutorial, I show you how to scrape a simple website with python and beautiful soup 4. Mar 14, 2020 This is the second article of my web scraping guide. In the first article, I showed you how you can find, extract, and clean the data from one single web page on IMDb. In this article, you’ll learn how to scrape multiple web pages — a list that’s 20 pages and 1,000 movies total — with a Python web scraper.

In the last tutorial we learned how to leverage the Scrapy framework to solve common web scraping problems.Today we are going to take a look at Selenium (with Python ❤️ ) in a step-by-step tutorial.

Selenium refers to a number of different open-source projects used for browser automation. It supports bindings for all major programming languages, including our favorite language: Python.

The Selenium API uses the WebDriver protocol to control a web browser, like Chrome, Firefox or Safari. The browser can run either localy or remotely.

Dec 05, 2017 In this tutorial, we will talk about Python web scraping and how to scrape web pages using multiple libraries such as Beautiful Soup, Selenium, and some other magic tools like PhantomJS. You’ll learn how to scrape static web pages, dynamic pages (Ajax loaded content), iframes, get specific HTML elements, how to handle cookies, and much more. Scraping Multiple Pages: mbadatanut: 1: 1,669: May-08-2020, 02:30 AM Last Post: Larz60+ scraping from a website that hides source code: PIWIProtein: 1: 553: Mar-27-2020, 05:08 PM Last Post: Larz60+ Scraping not moving to the next pages in a website: jithin123: 0: 542: Mar-23-2020, 06:10 PM Last Post: jithin123: Scraping from multiple URLS to. Jan 05, 2021 Advanced web scraping with python: Selenium Selenium comes with several options for performing advanced web scraping with ease. For example, let’s see how you can set it up to use proxies, execute JavaScript, and use a headless browser version.

At the beginning of the project (almost 20 years ago!) it was mostly used for cross-browser, end-to-end testing (acceptance tests).

Now it is still used for testing, but it is also used as a general browser automation platform. And of course, it us used for web scraping!

Selenium is useful when you have to perform an action on a website such as:

• Clicking on buttons
• Filling forms
• Scrolling
• Taking a screenshot

It is also useful for executing Javascript code. Let's say that you want to scrape a Single Page Application. Plus you haven't found an easy way to directly call the underlying APIs. In this case, Selenium might be what you need.

Installation

We will use Chrome in our example, so make sure you have it installed on your local machine:

• selenium package

To install the Selenium package, as always, I recommend that you create a virtual environment (for example using virtualenv) and then:

Quickstart

Once you have downloaded both Chrome and Chromedriver and installed the Selenium package, you should be ready to start the browser:

This will launch Chrome in headfull mode (like regular Chrome, which is controlled by your Python code).You should see a message stating that the browser is controlled by automated software.

To run Chrome in headless mode (without any graphical user interface), you can run it on a server. See the following example:

The driver.page_source will return the full page HTML code.

Here are two other interesting WebDriver properties:

• driver.title gets the page's title
• driver.current_url gets the current URL (this can be useful when there are redirections on the website and you need the final URL)

Locating Elements

Locating data on a website is one of the main use cases for Selenium, either for a test suite (making sure that a specific element is present/absent on the page) or to extract data and save it for further analysis (web scraping).

There are many methods available in the Selenium API to select elements on the page. You can use:

• Tag name
• Class name
• IDs
• XPath
• CSS selectors

We recently published an article explaining XPath. Don't hesitate to take a look if you aren't familiar with XPath.

As usual, the easiest way to locate an element is to open your Chrome dev tools and inspect the element that you need.A cool shortcut for this is to highlight the element you want with your mouse and then press Ctrl + Shift + C or on macOS Cmd + Shift + C instead of having to right click + inspect each time:

find_element

There are many ways to locate an element in selenium.Let's say that we want to locate the h1 tag in this HTML:

All these methods also have find_elements (note the plural) to return a list of elements.

For example, to get all anchors on a page, use the following:

Some elements aren't easily accessible with an ID or a simple class, and that's when you need an XPath expression. You also might have multiple elements with the same class (the ID is supposed to be unique).

XPath is my favorite way of locating elements on a web page. It's a powerful way to extract any element on a page, based on it's absolute position on the DOM, or relative to another element.

WebElement

A WebElement is a Selenium object representing an HTML element.

There are many actions that you can perform on those HTML elements, here are the most useful:

• Accessing the text of the element with the property element.text
• Clicking on the element with element.click()
• Accessing an attribute with element.get_attribute('class')
• Sending text to an input with: element.send_keys('mypassword')

There are some other interesting methods like is_displayed(). This returns True if an element is visible to the user.

It can be interesting to avoid honeypots (like filling hidden inputs).

Honeypots are mechanisms used by website owners to detect bots. For example, if an HTML input has the attribute type=hidden like this:

This input value is supposed to be blank. If a bot is visiting a page and fills all of the inputs on a form with random value, it will also fill the hidden input. A legitimate user would never fill the hidden input value, because it is not rendered by the browser.

That's a classic honeypot.

Full example

Here is a full example using Selenium API methods we just covered.

We are going to log into Hacker News:

In our example, authenticating to Hacker News is not really useful on its own. However, you could imagine creating a bot to automatically post a link to your latest blog post.

In order to authenticate we need to:

• Go to the login page using driver.get()
• Select the username input using driver.find_element_by_* and then element.send_keys() to send text to the input
• Follow the same process with the password input
• Click on the login button using element.click()

Should be easy right? Let's see the code:

Easy, right? Now there is one important thing that is missing here. How do we know if we are logged in?

We could try a couple of things:

• Check for an error message (like “Wrong password”)
• Check for one element on the page that is only displayed once logged in.

So, we're going to check for the logout button. The logout button has the ID “logout” (easy)!

We can't just check if the element is None because all of the find_element_by_* raise an exception if the element is not found in the DOM.So we have to use a try/except block and catch the NoSuchElementException exception:

Taking a screenshot

We could easily take a screenshot using:

Note that a lot of things can go wrong when you take a screenshot with Selenium. First, you have to make sure that the window size is set correctly.Then, you need to make sure that every asynchronous HTTP call made by the frontend Javascript code has finished, and that the page is fully rendered.

In our Hacker News case it's simple and we don't have to worry about these issues.

Waiting for an element to be present

Dealing with a website that uses lots of Javascript to render its content can be tricky. These days, more and more sites are using frameworks like Angular, React and Vue.js for their front-end. These front-end frameworks are complicated to deal with because they fire a lot of AJAX calls.

If we had to worry about an asynchronous HTTP call (or many) to an API, there are two ways to solve this:

• Use a time.sleep(ARBITRARY_TIME) before taking the screenshot.
• Use a WebDriverWait object.

If you use a time.sleep() you will probably use an arbitrary value. The problem is, you're either waiting for too long or not enough.Also the website can load slowly on your local wifi internet connection, but will be 10 times faster on your cloud server.With the WebDriverWait method you will wait the exact amount of time necessary for your element/data to be loaded.

This will wait five seconds for an element located by the ID “mySuperId” to be loaded.There are many other interesting expected conditions like:

• element_to_be_clickable
• text_to_be_present_in_element
• element_to_be_clickable

You can find more information about this in the Selenium documentation

Executing Javascript

Sometimes, you may need to execute some Javascript on the page. For example, let's say you want to take a screenshot of some information, but you first need to scroll a bit to see it.You can easily do this with Selenium:

Conclusion

I hope you enjoyed this blog post! You should now have a good understanding of how the Selenium API works in Python. If you want to know more about how to scrape the web with Python don't hesitate to take a look at our general Python web scraping guide.

Selenium is often necessary to extract data from websites using lots of Javascript. The problem is that running lots of Selenium/Headless Chrome instances at scale is hard. This is one of the things we solve with ScrapingBee, our web scraping API

Selenium is also an excellent tool to automate almost anything on the web.

If you perform repetitive tasks like filling forms or checking information behind a login form where the website doesn't have an API, it's maybe* a good idea to automate it with Selenium,just don't forget this xkcd:

Web Scraping Multiple Websites Python 3.7

What is Web Scraping?

Web Scraping is a technique to extract a large amount of data from several websites. The term 'scraping' refers to obtaining the information from another source (webpages) and saving it into a local file. For example: Suppose you are working on a project called 'Phone comparing website,' where you require the price of mobile phones, ratings, and model names to make comparisons between the different mobile phones. If you collect these details by checking various sites, it will take much time. In that case, web scrapping plays an important role where by writing a few lines of code you can get the desired results.

Web Scrapping extracts the data from websites in the unstructured format. It helps to collect these unstructured data and convert it in a structured form.

Startups prefer web scrapping because it is a cheap and effective way to get a large amount of data without any partnership with the data selling company.

Is Web Scrapping legal?

Here the question arises whether the web scrapping is legal or not. The answer is that some sites allow it when used legally. Web scraping is just a tool you can use it in the right way or wrong way.

Web scrapping is illegal if someone tries to scrap the nonpublic data. Nonpublic data is not reachable to everyone; if you try to extract such data then it is a violation of the legal term.

There are several tools available to scrap data from websites, such as:

• Scrapping-bot
• Scrapper API
• Octoparse
• Import.io
• Webhose.io
• Dexi.io
• Outwit
• Diffbot
• Content Grabber
• Mozenda
• Web Scrapper Chrome Extension

Why Web Scrapping?

As we have discussed above, web scrapping is used to extract the data from websites. But we should know how to use that raw data. That raw data can be used in various fields. Let's have a look at the usage of web scrapping:

• Dynamic Price Monitoring

It is widely used to collect data from several online shopping sites and compare the prices of products and make profitable pricing decisions. Price monitoring using web scrapped data gives the ability to the companies to know the market condition and facilitate dynamic pricing. It ensures the companies they always outrank others.

• Market Research

eb Scrapping is perfectly appropriate for market trend analysis. It is gaining insights into a particular market. The large organization requires a great deal of data, and web scrapping provides the data with a guaranteed level of reliability and accuracy.

• Email Gathering

Many companies use personals e-mail data for email marketing. They can target the specific audience for their marketing.

• News and Content Monitoring

A single news cycle can create an outstanding effect or a genuine threat to your business. Schoolbuslicensesmallanne 28 online, free games. If your company depends on the news analysis of an organization, it frequently appears in the news. So web scraping provides the ultimate solution to monitoring and parsing the most critical stories. News articles and social media platform can directly influence the stock market.

• Social Media Scrapping

Web Scrapping plays an essential role in extracting data from social media websites such as Twitter, Facebook, and Instagram, to find the trending topics.

• Research and Development

The large set of data such as general information, statistics, and temperature is scrapped from websites, which is analyzed and used to carry out surveys or research and development.

Why use Python for Web Scrapping?

There are other popular programming languages, but why we choose the Python over other programming languages for web scraping? Below we are describing a list of Python's features that make the most useful programming language for web scrapping.

• Dynamically Typed

In Python, we don't need to define data types for variables; we can directly use the variable wherever it requires. It saves time and makes a task faster. Python defines its classes to identify the data type of variable.

• Vast collection of libraries

Python comes with an extensive range of libraries such as NumPy, Matplotlib, Pandas, Scipy, etc., that provide flexibility to work with various purposes. It is suited for almost every emerging field and also for web scrapping for extracting data and do manipulation.

• Less Code

The purpose of the web scrapping is to save time. But what if you spend more time in writing the code? That's why we use Python, as it can perform a task in a few lines of code.

• Open-Source Community

Python is open-source, which means it is freely available for everyone. It has one of the biggest communities across the world where you can seek help if you get stuck anywhere in Python code.

The basics of web scraping

The web scrapping consists of two parts: a web crawler and a web scraper. In simple words, the web crawler is a horse, and the scrapper is the chariot. The crawler leads the scrapper and extracts the requested data. Let's understand about these two components of web scrapping:

• The crawler

A web crawler is generally called a 'spider.' It is an artificial intelligence technology that browses the internet to index and searches for the content by given links. It searches for the relevant information asked by the programmer.

The scrapper

A web scraper is a dedicated tool that is designed to extract the data from several websites quickly and effectively. Web scrappers vary widely in design and complexity, depending on the projects.

How does Web Scrapping work?

These are the following steps to perform web scraping. Let's understand the working of web scraping.

Step -1: Find the URL that you want to scrape

First, you should understand the requirement of data according to your project. A webpage or website contains a large amount of information. That's why scrap only relevant information. In simple words, the developer should be familiar with the data requirement.

Step - 2: Inspecting the Page

The data is extracted in raw HTML format, which must be carefully parsed and reduce the noise from the raw data. In some cases, data can be simple as name and address or as complex as high dimensional weather and stock market data.

Step - 3: Write the code

Write a code to extract the information, provide relevant information, and run the code.

Step - 4: Store the data in the file

Store that information in required csv, xml, JSON file format.

Getting Started with Web Scrapping

Python has a vast collection of libraries and also provides a very useful library for web scrapping. Let's understand the required library for Python.

Manual for mac 3200 chainsaw. Library used for web scrapping

• Selenium- Selenium is an open-source automated testing library. It is used to check browser activities. To install this library, type the following command in your terminal.

Note - It is good to use the PyCharm IDE.

• Pandas

Pandas library is used for data manipulation and analysis. It is used to extract the data and store it in the desired format.

• BeautifulSoup

BeautifulSoup is a Python library that is used to pull data of HTML and XML files. It is mainly designed for web scrapping. It works with the parser to provide a natural way of navigating, searching, and modifying the parse tree. The latest version of BeautifulSoup is 4.8.1.

Let's understand the BeautifulSoup library in detail.

Installation of BeautifulSoup

You can install BeautifulSoup by typing the following command:

Installing a parser

BeautifulSoup supports HTML parser and several third-party Python parsers. You can install any of them according to your dependency. The list of BeautifulSoup's parsers is the following:

Parser	Typical usage
Python's html.parser	BeautifulSoup(markup,'html.parser')
lxml's HTML parser	BeautifulSoup(markup,'lxml')
lxml's XML parser	BeautifulSoup(markup,'lxml-xml')
Html5lib	BeautifulSoup(markup,'html5lib')

We recommend you to install html5lib parser because it is much suitable for the newer version of Python, or you can install lxml parser.

Type the following command in your terminal:

BeautifulSoup is used to transform a complex HTML document into a complex tree of Python objects. But there are a few essential types object which are mostly used:

• Tag

A Tag object corresponds to an XML or HTML original document.

Output:

Tag contains lot of attributes and methods, but most important features of a tag are name and attribute.

• Name

Every tag has a name, accessible as .name:

• Attributes

A tag may have any number of attributes. The tag <b id = 'boldest'> has an attribute 'id' whose value is 'boldest'. We can access a tag's attributes by treating the tag as dictionary.

We can add, remove, and modify a tag's attributes. It can be done by using tag as dictionary.

• Multi-valued Attributes

In HTML5, there are some attributes that can have multiple values. The class (consists more than one css) is the most common multivalued attributes. Other attributes are rel, rev, accept-charset, headers, and accesskey.

• NavigableString

A string in BeautifulSoup refers text within a tag. BeautifulSoup uses the NavigableString class to contain these bits of text.

A string is immutable means it can't be edited. But it can be replaced with another string using replace_with().

In some cases, if you want to use a NavigableString outside the BeautifulSoup, the unicode() helps it to turn into normal Python Unicode string.

• BeautifulSoup object

The BeautifulSoup object represents the complete parsed document as a whole. In many cases, we can use it as a Tag object. It means it supports most of the methods described in navigating the tree and searching the tree.

Output:

Web Scrapping Example:

Let's take an example to understand the scrapping practically by extracting the data from the webpage and inspecting the whole page.

First, open your favorite page on Wikipedia and inspect the whole page, and before extracting data from the webpage, you should ensure your requirement. Consider the following code:

Output:

In the following lines of code, we are extracting all headings of a webpage by class name. Here front-end knowledge plays an essential role in inspecting the webpage.

Output:

In the above code, we imported the bs4 and requested the library. In the third line, we created a res object to send a request to the webpage. As you can observe that we have extracted all heading from the webpage.

Webpage of Wikipedia Learning

Let's understand another example; we will make a GET request to the URL and create a parse Tree object (soup) with the use of BeautifulSoup and Python built-in 'html5lib' parser.

Here we will scrap the webpage of given link (https://www.javatpoint.com/). Consider the following code:

The above code will display the all html code of javatpoint homepage.

Using the BeautifulSoup object, i.e. soup, we can collect the required data table. Let's print some interesting information using the soup object:

• Let's print the title of the web page.

Output: It will give an output as follow:

• In the above output, the HTML tag is included with the title. If you want text without tag, you can use the following code:

Output: It will give an output as follow:

• We can get the entire link on the page along with its attributes, such as href, title, and its inner Text. Consider the following code:

Output: It will print all links along with its attributes. Here we display a few of them:

Demo: Scraping Data from Flipkart Website

In this example, we will scrap the mobile phone prices, ratings, and model name from Flipkart, which is one of the popular e-commerce websites. Following are the prerequisites to accomplish this task:

Prerequisites:

• Python 2.x or Python 3.x with Selenium, BeautifulSoup, Pandas libraries installed.
• Google - chrome browser
• Scrapping Parser such as html.parser, xlml, etc.

Step - 1: Find the desired URL to scrap

The initial step is to find the URL that you want to scrap. Here we are extracting mobile phone details from the flipkart. The URL of this page is https://www.flipkart.com/search?q=iphones&otracker=search&otracker1=search&marketplace=FLIPKART&as-show=on&as=off.

Step -2: Inspecting the page

It is necessary to inspect the page carefully because the data is usually contained within the tags. So we need to inspect to select the desired tag. To inspect the page, right-click on the element and click 'inspect'.

Web Scraping Multiple Websites Python Code

Step - 3: Find the data for extracting

Extract the Price, Name, and Rating, which are contained in the 'div' tag, respectively.

Step - 4: Write the Code

Output:

We scrapped the details of the iPhone and saved those details in the CSV file as you can see in the output. In the above code, we put a comment on the few lines of code for testing purpose. You can remove those comments and observe the output.

Web Scraping Multiple Websites Python Tutorial

In this tutorial, we have discussed all basic concepts of web scrapping and described the sample scrapping from the leading online ecommerce site flipkart.

Web Scraping Multiple Websites Python 3