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“Things get done only if the data we gather can inform and inspire those in a position to make a difference” - Michael Schmoker, Former School Administrator.
So, how can your data turn into something inspirational? It is through data scraping from the websites. This process helps you pull out the correct data and transform them into insightful information.
Sounds simple? But in actuality, it is not? As the internet has tons of information displayed across websites.
Can you extract and deploy every single piece of data manually for each of your projects? No human can do this painstaking task.
What can be the possible solution? Few codes to extract data from thousands of pages instead of doing it with your hands.
Yes, it is the automation technique that many data scientists and developers deploy to procure accurate results in a quick turnaround.
As the subject of discussion is vast, the article has some digestible section that includes the following:
Without any further adieu, let us dive into our technical discussion and talk about web scraping using python in detail in this article!
In simple words, Web scraping with Python is the task of collecting volumes of information from websites, aka web data extraction. What are the applications for web scraping with Python?
There are several, but few general ones include the following:
All the information you extract through a programming language like Python has one commonality, it helps businesses or individuals like you make clever decisions from public data.
You can now make a hassle-free extract of countless data that does not exist in the manual version of the same.
Web scraping is an automation technique to retrieve volumes of data from different websites and convert and store them in a structured format. You can do it via several methods. But here, it is all about scraping with Python.
A brief about the basics helps you comprehend the process with efficacy.
The web crawler helps the scraper extract the requested data from the internet. That was a crisp intro to crawlers and scrapers, Right? But it has more to it.
So, how do they work? Are they the same or not? Read below to know further.
Do you know what spiders do? They crawl around the wall and build their webs, right? Yes, web crawlers/spiders are AI that surf the internet to search and index your content through the URL links. They are just like a person who has more time and does not have a to-do list for that day.
In general, any project crawling process precedes scraping. After the website(s) is crawled through and the URLs get figured are handed over to your scraper tool.
Web scraping tools known as web scrapers have the potential to extract data from any website within a blink of an eye. These tools help you to develop data for ML in specific.
It is a customized tool that extracts web data with accuracy and efficiency at rapid rates. The data selectors present in the scrapers detect and extract info from the HTML files.
People synonymously toss web scraping and crawling. But there exist a few differences. They are:
|
Web scraping |
Web crawling |
|
Downloads the info |
Indexes the web page |
|
A Web site visit is not necessary |
A Web site is mandatory throughout the entire process |
|
Deployed on for small- and large-scale data |
Housed for large-scale data only
|
|
Finds application in ML, Retail marketing, Equity search, Travel and Tourism, Real estate, Academic research |
The areas of application include Price intelligence, Competition research, Brand monitoring, Data-driven portfolio management |
|
Requires both crawl agent and parser for parsing |
Crawling needs crawl agent alone |
|
Does not abide robots.txt (most cases) |
Not all crawlers abide by robots.txt |
So, the next question that pops up in your mind is that does web scraping with Python work? The answer is, Yes!
What is the purpose of web scraping with Python? Gather volumes of information from websites. Why collect so much information from these online sources? Insights into the application of web scraping can provide you with an answer.
The most significant use case for web scraping with Python is price intelligence. What is price intelligence? Why is it necessary?
The retailers look for the same product prices on other sites and extract them, which helps them better their marketing decisions or costs. In addition, price intelligence helps provide better prices than your competitors.
What is your marketing methodology? Is it newsletters and email marketing to promote your products or services? Then collecting email addresses is essential to reach your target audience.
How to gather those addresses? You can use a web scraper tool like Hunter.io to download the information from similar sites of your domain.
Do you have to collect vital data and stats for your high-end research project(s)? Then web scraper saves your time on manual copying of voluminous data.
Do you want to find out the business trends and methods that make your business stay differentiated from the rest? Then these scraping tactics can help you do it.
Also, it helps to find out about people’s perspectives of your brand.
How to determine the efficiency and user-interactiveness of your website? Test your site using a web scraping tool that sends volumes of requests to figure out the response time.
What is the best programming language to deploy for web scraping? Python.
Many programming languages help you build a web scraper from scratch. So, why choose Python over them? The following benefits list will persuade you to go for it and also pick it as your career option:
Do you know that all the websites are not the same and have different formats? So, the scrapping process needs iterations with every new web data entry per second.
But doing manual processes is nerve-wracking? Here is where the fully automated library of Python enables the web scraper to auto-extract data every day.
The write-once and write-few lines of automated Python codings are a time and effort saver to a great extent with faster data extraction rates.
In general, every scraping involves two processes that are,
Many web scraping tools handle only the former process and not the latter one. But Python does scraping and parsing, and it saves the data extracted and visualizes it with Matplotlib.
The vital benefit of deploying Python tools for the scrapping process is that all its syntaxes are easy-to-read. Even amateurs can write the scraping codes without much effort.
Do you know the reason behind Python’s popularity? It has the easiest-to-write codes for generating scraping scripts as well. You can write down a few coding lines in a matter of minutes without any hassle.
Do you want to develop web scrapers that deliver high-performance? Scrapy and Beautiful Soup are some fast and efficient web scraper tools from Python that help you do it.
With Python, you don’t have to define your variables and enter them directly wherever you need. It is a time-saver benefit.
Python’s web scraper tools can do much more than data extraction, and they include:
The convenience of Python scraping codes is write-and-execute once. Later, the scraper tool automatically collects volumes of data every day that saves your energy and time.
There are several libraries in Python that makes a developer/programmers life a breeze, and it includes:
Due to its popularity, the Python community is expanding every day. So, if you face any issues while coding, you can get help from the experts in the community.
Basics of web scraping with Python, Check! Reasons to scrap using Python, Check!. Now, let us look at web scraping with a python tutorial.
The following is the step-by-step guide to do web scraping with Python:
In our example, we are scraping data for Adidas shoes from Myntra, and the link would be https://www.myntra.com/adidas-shoes. We are focusing on Adidas sports shoes, and the URL would be: https://www.myntra.com/sports-shoes/adidas/adidas-men-navy--red-woven-design-magnificeo-running-shoes/14782120/buy.
Note: For web scraping with Python, you have to store the data in the CSV format for later use.
You can find the data spaced between the nested tags, and for retrieving that info, you have to inspect the page. Follow these steps:
Now, right-click your mouse on the image and select the inspect option from it.
After clicking the inspect option, you will get a window pop up open like the below:
Now, locate your data embedded between the tag. Now you can see the product details here. The tag and class name may vary from different sites or images.
The next step in web scraping with Python is to create the workspace. For this, you need to download and install Python.
Choose from the several IDE that suits your needs.
Next, install the following libraries for your workspace:
python -m pip install selenium pandas beautifulsoup4
You have to import these installed libraries for usage in your Chrome browser by setting the path to chromedriver. You need not worry about the location here if the path is correct.
Remember to add the pathname at the end along with the location.
Make sure you declare the variables and set the site URL you plan to scrape:
models = [ ]
prices = [ ]
driver.get(site URL comes here)
Finally, you need to extract the information embedded between the
Click paste the following code in your command line to run the code:
Python main.py
Now store your extracted data in a format that is suitable for you. For instance, you can keep it in a CSV format that helps with easy import.
df = pd.DataFrame({ attributes of
})
df.to_csv('file name', index=False, encoding='utf-8')
Now when you rerun the code, the file name is created.
This web scraping with python tutorial is a simple one that is effective for single-page data scraping.
Last but not least, a brief look into the top libraries in Python would deliver a perfect ending to our discussion of web scraping with Python.
First, let us start with the popular and beautiful one.
Web scraping with Python is a technically challenging task that requires more knowledge, practice, hands-on experience to get your career started. Well, web scraping with a python tutorial is beneficial to a great extent. But online Python certifications pave the strong foundation for your bright data scientist career. Choose the best one today!
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