Computer data refers to information that undergoes processing or is stored within a computer system. Almost all smart devices, such as phones and computers we frequently use today, store all our operations on the back side, called data. This data can be of many types, such as audio, text, and image. Today, data is important for many companies in many respects. Therefore, it has become a necessity for businesses to obtain data. Today, companies meet their data acquisition needs with web scraping Python codes. Python web scraping tools are the easiest and most effective way for companies to obtain data.
Nowadays, web scraping tools can be developed with many programming languages. In fact, many programming languages have libraries to facilitate developers in their web scraping process. However, Python is the programming language most preferred by companies and developers in web scraping processes. In this article, we’ll cover why Python is often used in web scraping applications. We will then develop sample applications for web scraping Python.
Why Is Python Suitable for Web Scraping Applications?
Today, developers can extract data from many web pages in multiple programming languages. But Python provides excellent benefits to developers in their web process. This is why Python is the number one choice of developers for web scraping.
Some of the benefits Python provides for developers are as follows:
- Fast Code Development Possibility: Python code has a simple and understandable syntax. Developers can quickly develop and easily read Python code.
- Wide Library Support: Python provides developers with a set of powerful libraries for performing web scraping. For example, popular libraries such as BeautifulSoup, Scrapy, and Requests library can be used to analyze web pages, pull data, and manipulate it.
- Data Processing and Analysis Capabilities: Python provides powerful tools for data manipulation, analysis, and processing for all the data obtained as a result of data scraping. For example, the Pandas library is widely used to manipulate tabular data easily.
- Community Support: Python community has a large and active user community. This community offers developers many resources to provide answers to questions about web scraping, guides, and help when you need it.
What Are the Ways of Scraping Data in Python?
There are two main ways to develop a web scraping project with the Python programming language. One is to use a Python library, another is to use a web scraper API.
Learn the best practices for web scraping with Python.
There are many libraries Python provides to developers in their web scraping process. BeautifulSoup, Scrapy, Requests, and Selenium are among these libraries. Libraries are easy-to-use libraries, but they are insufficient for developers who want to create automated processes in their web scraping process. If developers want to create automated processes using these libraries, they have to manually configure many configurations such as proxy use, IP rotation, and request intervals.
Another popular method developers can follow in their web scraping process is to use a web scraping API. There are many popular web scraping APIs available today, such as the Zenscrape API, the Zenserp API, and the scrapestack API. It allows developers to create automated processes as many web scraping APIs have proxy pools. Developers do not need to make any configuration while obtaining data from target websites.
Extracting Data With a Python Library
In this section, we will scrape the HTML content of the ‘https://example.org/‘ website using Python’s BeautifulSoup and Requests libraries. For this, let’s open a file in the file path where we will develop the application and paste the following codes into it.
import requests
from bs4 import BeautifulSoup
# Proxy usage conf.
# Proxy rotation conf.
# Request interval conf.
response = requests.get("https://example.org/")
if response.status_code == 200:
soup = BeautifulSoup(response.content, "html.parser")
print(soup)
else:
print("Error Code: ", response.status_code)The HTML content we will get after running the application is as follows.
Extracting Data With the Zenscrape API
Now we will develop a web scraping application using a web scraping API with Python. For this, we will use the Zenscrape API, which is the most popular web scraping API today. Zenscrape API is a web scraping API that provides developers with fast and efficient web scraping.
This API has a fairly large pool of proxies. Also, it automatically enables proxy rotation in web scraping processes. Thus, developers can stream data from web pages in an automated manner without any additional configuration in their web scraping process.
Before we develop a web scraping application using this API, we will sign up for one of the subscription plans provided by this API and obtain an API key.
After obtaining the API key, let’s create a Python file and put the following codes in this file.
import requests
headers = {
"apikey": "YOUR-API-KEY"}
params = (
("url","https://example.org/"),
("premium","true"),
("country","de"),
("render","true"),
);
response = requests.get('https://app.zenscrape.com/api/v1/get', headers=headers, params=params);
print(response.text)Let’s run the application after we put our own API key in the API key field. After running the application, the response we get will be as follows.
Explore more features in Zenscrape comprehensive documentation.
Conclusion
In short, Python is the programming language of choice for web scraping applications. Because of the many unique advantages Python provides to developers, developers can develop web scraping applications in the Python programming language in many ways. Among these ways, using a web scraping API frees developers from many costly operations such as proxy settings.
Use our best web scraper API with a proxy pool, integrate it into Python in just a few steps, and start using it quickly.
FAQs
Q: How to Scrape Data From Any Web Page?
A: There are many ways to scrape data from their pages. The most popular among these are using web scraping libraries provided by programming languages and using a web scraping API. To scrape data from web pages using a web scraping API, sending an HTTP request to the web scraping API is usually sufficient.
Q: What Features Should a Good Web Scraping API Have?
A: Good web scraping API should have the following features:
- Proxy usage
- Proxy rotation
- Location-based scraping
- High availability
- High speed
Q: What Are the Python Web Scraping Libraries?
A: Python has many unique web scraping libraries available to developers. The most popular of these libraries are BeautifulSoup, Scrapy, Requests, and Selenium.
Q: Does the Zenscrape API Provide the HTML Code of the Target Website With JavaScript Rendering?
A: Yes, it does. Zenscape API provides the HTML code of the target website with JavaScript rendering. In this way, they scrape the data that users see on web pages.
