Top 5 Programming Languages for web scraping. Which Programming Language To Choose & Why?

Top 5 Programming Languages for web scraping. Which Programming Language To Choose & Why?

It’s important that a developer selects the best programming language that will help them scrape certain data that they want to scrape. These days programming languages are quite robust when it comes to supporting different use cases, such as web scraping.

When a developer wants to build a web scraper, the best programming language they can go for is the one they are most comfortable and familiar with. Web data can come in highly complex formats very often, and the structure of the web pages can rotate time and again, and it needs the developers to adjust the code accordingly.

When selecting the programming language, the first and main criterion should be proper familiarity with it. Web scraping is supported in almost any programming language, so the one a developer is most familiar with should be chosen.

For instance, if you know PHP. start with PHP only and later take it from there. It will make sure that you already have built-in resources for that language, as well as prior experience and knowledge about how it functions. It will also help you do web scraping faster.

Apart from these, there are a few other parameters that you should consider when selecting any programming language for web scraping. Let’s have a look at those parameters.

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Scraping Python library. Scraping using the Best Python Libraries

There are a number of great web scraping tools available that can make your life much easier. Here’s the list of top Python web scraping libraries that we choose to scrape:

1. BeautifulSoup : This is a Python library used to parse HTML and XML documents.
2. Requests: Best to make HTTP requests.
3. Selenium: Used to automate web browser interactions.
4. Scrapy Python : This is a Python framework used to build web crawlers.

Let’s get started.

1. Beautiful Soup Web Scraping with Python

Beautiful Soup is a Python library for parsing HTML and XML documents. It creates a parse tree for parsed pages that can be used to extract data from HTML, which is useful for web scraping. It is also used to extract data from some JavaScript-based web pages.

Open your terminal and run the command below:

pip install beautifulsoup4

With Beautiful Soup installed, create a new python file, name it beautiful_soup.py

We are going to scrape (Books to Scrape) website for demonstration purposes. The Books to Scrape website looks like this:

We want to extract the titles of each book and display them on the terminal. The first step in scraping a website is understanding its HTML layout. In this case, you can view the HTML layout of this page by right-clicking on the page, above the first book in the list. Then click Inspect .

Below is a screenshot showing the inspected HTML elements.

You can see that the list is inside the

element. The next direct child is the
1. element.

   What we want is the book title, which is inside the , inside the

   , inside the

   , and finally inside the

   element.

   To scrape and get the book title, let’s create a new Python file and call it beautiful_soup.py

   When done, add the following code to the beautiful_soup.py file:

   from urllib.request import urlopen from bs4 import BeautifulSoup url_to_scrape = request_page = urlopen(url_to_scrape) page_html = request_page.read() request_page.close() html_soup = BeautifulSoup(page_html, ‘html.parser’) for data in html_soup.select(‘ol’): for title in data.find_all(‘a’): print(title.get_text())

   In the above code snippet, we open our webpage with the help of the urlopen() method. The read() method reads the whole page and assigns the contents to the page_html variable. We then parse the page using html.parser to help us understand HTML code in a nested fashion.

   element. We loop through the HTML elements inside the
   element to get the tags which contain the book names. Finally, we print out each text inside the tags on every loop it runs with the help of the get_text() method.

   python beautiful_soup.py

   This should display something like this:

   Now let’s get the prices of the books too.

   tag, inside a

   tag. As you can see there is more than one
   tag and more than one
   tag. To get the right element with the book price, we will use CSS class selectors; lucky for us; each class is unique for each tag.

Python web scraping. Введение

Представьте, что мы хотим произвести скрапинг платформы, содержащей общедоступные объявления о недвижимости. Мы хотим получить цену недвижимости, ее адрес, расстояние, название станции и ближайший до нее тип транспорта для того, чтобы узнать, как цены на недвижимость распределяются в зависимости от доступности общественного транспорта в конкретном городе.

Предположим, что запрос приведет к странице результатов, которая выглядит следующим образом:
Как только мы узнаем, в каких элементах сайта хранятся необходимые данные, нам нужно придумать логику скрапинга, которая позволит нам получить всю нужную информацию из каждого объявления.
Нам предстоит ответить на следующие вопросы:

1. Как получить одну точку данных для одного свойства (например данные из тега price в первом объявлении)?
2. Как получить все точки данных для одного свойства со всей страницы (например все теги price с одной страницы)?
3. Как получить все точки данных для одного свойства всех страниц с результатами (например все теги price со всех страниц с результатами)?
4. Как устранить несоответствие, когда данные могут быть разных типов (например, есть некоторые объявления, в которых в поле цены указана цена по запросу. В конечном итоге у нас будет столбец, состоящий из числовых и строковых значений, что в нашем случае не позволяет провести анализ)?