We can build a news aggerator web app by scrapping the news websites and serving those scrapped news via Django on web or in any app.
In this article, i will explain step by step guide on how to implement everything. Let's start by understand what a news aggregator is and why should we build it.
What is news aggregator ?
A news aggregator is a system that takes news from several resources and puts them all together. A good example of news aggregator are JioNews and Google News.
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Why build a news aggregator ?
There are hundreds of news websites, they do cover news on serveral broad topics, out of which only a few of them are of our interest. A news aggregator can be a tool to save a lot of time and with some modifications and filteration we can fine tune it to show only news of our interest.
A news aggregator can be an useful tool to get information within short time.
Plan
We'll build our news aggeragator in 3 parts. These are following:
- We'll research on html source code of news sites and build a website scrapper for each
- Then, We'll setup our django server
- Finally, we'll integrate everything altogether
So, let's start with first step.
Building the website scrapper
Before we start building scrapper, let's get the required packages first. You can install them from command prompt by these commads.
This will install the required packages.
We are going to use timesofindia and hindustantimes as our news sources. We'll Get content from these two websites and integrate into our news aggregator.
Let's start by times of india... We'll take news from berief section of times of india. Here, we can see that news heading comes in h2 tag.
So we'll grab this tag. Here is how our scrapper will look like.
This we'll get all the news headings from times of india.
Now, let's move to Hindustan times. We'll scrap india section of their website. Here we can see that, news is coming in a div with headingfour class.
Let's write a scrapper for this div.
Now we have the news that, we want to display in our web app. We can start building our web app.
Building Django web app
To build web app with django, we need to install django on our system. You can install Django from following command.
After installation of django, we can start building our web app. I'll call my app HackersFriend News Aggregator, you can give name of your app as per your choice, it doesn't matter. We will create the project from this command.
After that your directory structure should look like this.
Once we have manage.py file. We'll create app, in which our web app will live. Django, has convetion of keeping everything in seperate app, Inside a project. A project can have multiple apps.
So move into the project folder and create the app. This is the command to create app. I am calling the app news. You can give name of your choice.
After that your directory should look like this.
Now, we'll add this news app to settings.py file in INSTALLED_APPS. So that, Django takes this app into consideration. Here is how your settings.py should look like after adding the news app:
Now, let's create a template for home page.
Go to news directory > create a directory with name templates > create a news directory inside templates directory and then create a index.html file inside this directory.
We'll use bootstrap 4, so include all the css links and js file links into page index.html. Also, we are going to pass two variables namely toi_news and ht_news from our views.py file to this template with news of times of india and hindustan times respectively and we'll loop through them and print the news. Here is how your index.html file should look like.
Now, we can create views.py file.
Inside views.py file we will create news scrapper of both news sites.
Here is how our views.py file looks.
Once, we are done with template and views creation, we can add this view to our urls.py file to server the view.
Move to HackersFriend_NewsAggregator diectory and open urls.py file and there you need to import news view and add this view to url.
Here is how urls.py looks after adding.
After that, we are done. Now you can run your web app from command window. Use this command to run the app.
after that, you can open 127.0.0.1:8000 and you should see the news aggregator app's homepage.
That's certainely not the most beautifule news app on the internet, but you get the idea how we can build a news aggregator.
You can add a lot of features on top of it. Like showing news on certain topic, aggregating from several websites etc.
Here is github repo for all the codes: https://github.com/hackers-friend/HackersFriend-NewsAggregator
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. 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.
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.
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
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:
Web Scraping In Django Unchained
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:
Web Scraping In Django Python
- 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'.
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:
Django Web App
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.
Website Scrape Django
In this tutorial, we have discussed all basic concepts of web scrapping and described the sample scrapping from the leading online ecommerce site flipkart.