- Web Scraping Python Django
- Web Scraping In Django Unchained
- Web Scraping In Django Using
- Web Scraping In Django Project
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.
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In this article, i will explain step by step guide on how to implement everything. Intego antivirus vs norton. 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.
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
Have you ever thought of all the possibilities web scraping provides and how many benefits it can unlock for your business? Surely, you have!
But at the same time there were a lot of thoughts about the hurdles appearing – possible blocking, the system being sophisticated, difficulties in getting JS/AJAX data, scaling up challenges, maintaining, requiring above-the-average skill. And even if you don't give up and keep working, your efforts can be completely derailed by the structure changes in the website. Don't worry about that! There's a simple Beginners Guide to web scraping. We did our best to put it together so even if you don't have a technical background or lack relevant experience, you can still use it as a handbook. So you can get all the advantages web scraping provides and implement the juicy features into your business.
Let's get started!
What is web scraping?
In short, web scraping allows you to extract data from the websites, so it can be saved in a file on your machine, so it can be accessed on a spreadsheet later on.
Usually you can only view the downloaded web page but not extract data. Yes, it is possible to copy some parts of it manually but this way is too time-consuming and not scalable. Web scraping extracts reliable data from the picked pages, so the process becomes completely automated. The received data can be used for business intelligence later on.
In other words, one can work with any kind of data, as far web scraping works perfectly fine with vast quantities of data, as well as different data types.
Images, text, emails, even phone numbers – all will be extracted up to your business' needs. For some projects specific data can be needed, for example, financial data, real estate data, reviews, price or competitor data whatever. Using web scraping tools it is fast and easy to extract it as well. But the best thing is that at the end you get the extracted data in a format of your choice. It can be plain text, JSON or CSV.
How does web scraping work?
Surely, there are lots of ways to extract data, but here there's the easiest and the most reliable one. Here's how it works.
1. Request-response
The first simple step in any web scraping program (also called a 'scraper') is to request the target website for the contents of a specific URL.
In return, the scraper gets the requested information in HTML format. Remember, HTML is the file type used to display all the textual information on a webpage.
2. Parse and extract
HTML is a markup language, having a simple and clear structure. Parsing applies to any computer language, taking the code as bunches of text. It produces a structure in memory, which the computer can understand and work with.
Sounds too difficult? Wait a second. To make it simple we can say that HTML parsing takes HTML code, expects it and extracts the relevant information – title, paragraphs, headings. Links and formatting like bold text.
Docker build -t metabase/druid:0.17.0. The build logic ingests the data in rows.jsonby executing the ingestion spec task task.json. This is done in the script ingest.sh; tweak as needed. Why ingest data as part of the build process? Metabase druid. Super impressed with @metabase! We are using it internally for a dashboard and it really offers a great combination of ease of use, flexibility, and speed. Paavo Niskala (@Paavi) December 17, 2019. @metabase is the most impressive piece of software I've used in a long time. If you have data you want to understand give it a try. Druid Druid is a distributed, column-oriented, real-time analytics data store that is commonly used to power exploratory dashboards in multi-tenant environments. Druid excels as a data warehousing solution for fast aggregate queries on petabyte sized data sets. Druid metabase.com Source Code Changelog Simple Dashboarding and GUI Query tool, Nightly Emails and Slack Integration w/ PostgreSQL, MySQL, Redshift and other DBs.
So all you need is a regular expression, defining the regular language, so a regular expression engine can generate a parser for this specific language. Thus pattern matching becomes possible, as well as text extraction.
3. Download data
The last step - downloading and saving the data in the format of your choice (CSV, JSON or in a database). After it becomes accessible, it can be retrieved, implemented in other programs.
In other words, scrapping allows you not just to extract data, but to store it into a central local database or spreadsheet and use it later when you need.
Advanced techniques for web scraping using python
Today computer vision technologies, as well as machine learning is used to distinguish and scrape data from the images, similar to the way a human being could do.
All it works quite straightforward. A machine learning system has its own classifications to which it assigns a so-called confidence score. Single double forklift. It is a measure of the statistical likelihood. So if the classification is considered to be correct, it means it is close to the patterns discerned in the training data
In case the confidence score is too low, the system initiates a new search query to pick the bunch of text which will most likely contain the previously requested data.
After the system makes an attempt to scrap the relevant data from the text considered to be new and reconciles the received result with the data in the initial scraping. In case the confidence score is still too low it processes further on, working on the next pulled text.
What is web scraping used for?
There are numerous ways how web scraping python can be used, basically it can be implemented in every known domain. But let's have a closer look at some areas where web scraping is considered to be the most efficient.
Price monitoring
Competitive pricing is the main strategy for e-commerce businesses. The only way to succeed here is to keep a constant track of the competitors and their pricing strategy. Parsed data can help to define your own pricing strategy. It is much faster than manual comparing and analysis. When it comes to price monitoring web scraping can be surprisingly efficient.
Lead generation
Web Scraping Python Django
Marketing is essential for any business. For marketing strategy to be successful one needs not just to have the contact details of the parties involved but to reach them. It is the essence of lead generation. And web scraping can improve the process, making it more efficient.
Leads are the very first thing needed for marketing campaign acceleration.
To reach the target audience you most likely need tons of data such as phone numbers, emails etc. And of course to collect it manually over the thousands of websites all over the web is impossible.
Web scraping is here to help! It extracts the data. The process is not just accurate but quick and takes just a fraction of time.
The received data can be easily integrated into your sales tools as far you can pick a format you are comfortable with.
Competitive analysis
Competition has always been the flesh and blood of any business, but today it is critically important to know the competitors well. It allows us to understand their strong and weak points, strategies and evaluate risks in a more efficient way. Of course it is possible only if you possess a lot of relevant data. And web scraping helps here as well.
Any strategy starts with analysis. But how to work with the data spread everywhere? Sometimes it is even impossible to access it manually.
If it is difficult to do manually, use web scraping. So you get the required data and can start working over almost immediately.
A good point here – the faster your scraping tool, the better competitive analysis will be.
Fetching images and product description
When the customer enters any e-commerce website the first thing he sees is the visual content, e.g. pictures. Tons and tons of them. But how to create all this amount of product descriptions and pictures overnight? With web scraping of course!
So, when you come up to the idea of launching a brand new e-commerce website you face a content issue – all these pictures, descriptions and so on.
Old good way of hiring somebody just to copy and paste or write the content from scratch might work but will take forever. Use web scraping instead and see the result.
In other words, web scraping makes your life as an e-commerce website owner much easier, right?
Is data scraping software legal?
Web scraping software is working with data – it is, technically, a process of data extraction. But what if it is protected by law or copyrighted? It is quite natural that one of the first appearing questions is ‘Is it legal?'. The issue is tricky, as far here's no certain opinion on this point even between the layers. Here are a few points to consider:
- Public data can be scrapped without any limits and there will be no restrictions. But if you step into the private data, it might land you in trouble.
- Abusive manner or using personal data for commercial purposes is the best way to end up in violation of CFAA, so avoid it.
- Scrapping copyrighted data is illegal and, well, unethical.
- To stay on the safe side, follow Robots.txt requirements, as well as Terms of Service (ToS).
- Using API for scraping is fine as well.
- Consider the crawl rate as 1 in 10-15 seconds. Otherwise you can be blocked.
- Don't hit servers too often and do not process web scraping in an aggressive manner if you want to be safe.
Challenges in web scraping
Some aspects of web scraping are challenging, though it is relatively simple in general. See below a short list of major challenges you can face:
1. Frequent structure changes
After the scrapper is set up the big game only begins. In other words, setting up the tool is the first step so you can face some unexpected challenges:
All websites keep updating their UI and features. It means that the website structure is changing all the time. As far the crawler keeps in mind the existing structure, any change might upset your plans. The issue will be solved as soon as you change the crawler accordingly.
So to get complete and relevant data you should keep changing your scrapper again and again as soon as structure changes appear.
2. HoneyPot traps
Keep in mind that all the websites with sensitive data take precautions to protect the data in this or that way and they are called HoneyPots. It means that all your web scraping efforts can simply be thwarted and you will be surfing the web in attempts to figure out what's wrong this time.
- HoneyPots are the links, accessible for crawlers, but developed to detect crawlers and prevent them from extracting data.
- They are in most cases the links with CSS style set to display:none. Another way to hide them is to remove them from the visible area or make them the color of background.
- When your crawler gets trapped, the IP becomes flagged or even blocked.
- Deep directory tree is another way to detect a crawler.
- So the number of retrieved pages or limit the traversal depth has to be limited.
3. Anti-scraping technologies
Anti-scrapping technologies evolve as well as web scraping does as far as there's a lot of data that should not be shared, and it is fine. But if you do not keep this in mind you can end up blocked. See below a short list of the most essential points you should know:
- The bigger the website is, the better it protects the data and defines crawlers. For example, LinkedIn, Stubhub and Crunchbase use powerful anti-scraping technologies.
- In case of such websites, bot access is prevented by using dynamic coding algorithms and IP blocking mechanisms implementation.
- It is clear that it is a huge challenge – to avoid blocking, so the solution, working against all the odds, turns out to become a time consuming and pretty expensive project.
4. Data quality
To get the data is just one of the points to achieve. For efficient work the data should be clean and accurate. In other words, if the data is incomplete or there are tons of mistakes, it is of no use. From a business perspective data quality is the main criteria, as far in the end of the day you need data ready to work with.
How can I start web scraping?
We are pretty sure – the question spinning round in your head is something like 'How can I start web scraping and enhance my marketing strategy?'
Coding your own
Web Scraping In Django Unchained
- Prefer DIY-approach? Then go on and code your own scraper.
- Open-source products are an option as well.
- A host is another essential chain in the link. It enables the scraper to run round the clock.
- Robust server infrastructure is a must. However, you will need some kind of storage for the data.
- One of the greatest things in DIY-approach and coding your own scraper is the fact that you are in absolute control of every single bit of functionality.
- Weak point here is an immense amount of needed resources.
- You should not forget about monitoring and improving your system from time to time, and it also requires resources.
- Coding your own scraper might be a good option for a small, short-term project.
Web scraping tools & web scraping service
Another way to reach the same result is just to use existing tools for scraping.
- Invest a bit and try existing tools to find the one, meeting your requirements best.
- You can get a lot of benefits the power of web scraping in case you find a reliable, scalable and affordable tool among the ones available in the market
- There are free tools or the ones with a substantial trial period. They are worth giving a try if you need to extract a lot of data.
- Try to work with ProWebScraper for the quick start. It's free, intuitive and allows python scrape website with the first 1000 pages for free.
Custom solution
Web Scraping In Django Using
There's another way, something in between the previous two.
It is simple – get the team of developers, so they will code a scraping tool specifically for your business' needs.
Web Scraping In Django Project
So you get a unique tool without the stress caused by accrual DIY approach. And the total cost will be much lower than in case you decide to subscribe to some existing scrapers.
Freelance developers can match too and create a good scrapper upon request, why not.
A SaaS MVP based on web scraping, data analytics, and data visualization
To sum up
Web scraping is an extremely powerful tool for extracting data and getting additional advantages over the competitors. The earlier you start exploring, the better for your business.
There are different ways to start exploring the world of web scrapers and you can start with free ones shifting to unique tools, developed in accordance with your needs and requirements.
