Which library is best for Python web scraping

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Contents

Which library is best for Python web scraping?

There are two major library options to choose from when you want to start a project off with web scraping. These libraries are Requests and BeautifulSoup4.

The beauty of Python is that it lets you decide which library is best for your use case, and what tools work best for your project. For wholeblogs, if your project needs graphics, then using the requests library may be more advantageous. If you need a quick solution, then using BeautifulSoup4 may be better.

There are two major library options to choose from when you want to start a project off with web scraping.

Requests.
BeautifulSoup4.

How to do Python web scraping without BeautifulSoup in Python?

While BeautifulSoup is a wonderful tool to help you get started with web scraping, it’s not the only way to do it. In fact, there are quite a few other tools out there that can help you perform web scraping tasks in Python.

Some of these include lxml, docx4hin, Selenium, and PyTest.

These tools may not be as slick as BeautifulSoup but they allow for more flexibility and more control than what you would find in BeautifulSoup.

How to Parse a Website with regex in Python?

Parsing is the process of understanding a string in a computer language. It’s common to parse strings as they are entered and then print them out, or display them in some way. You may want to be able to parse a website and extract information from it like title, date, price, etc.

So let’s see how we can do that with Python.

Here’s a wholeblogs of parsing the URL https://www.wholeblogs.com/.

There are various ways you can match a string in Python. I’ll be using regular expressions which are a simple and versatile pattern matching mechanism for text-based data such as text files or web pages. If you’re not familiar with regular expressions then head over to this tutorial on regex101.

Read further: 10 Best Books to Learn Python for Beginners & Advanced

What are regular expressions?

Regular expressions are a simple and versatile pattern matching mechanism for text-based data such as text files or web pages.

They allow you to extract information from a website like a title, date, price.

How to parse a website with urllib Python?

We’re going to use urllib, which is a library for working with URLs. We’ll pass it the website’s URL and then we can parse the website using regular expressions.

import urllib, re

url = 'https://www.wholeblogs.com'

How to get text in Python Web scraping with BeautifulSoup?

First, we need to import the library, bs4.

import bs4

Next, we will create a soup object by using the following code.

soup = bs4.BeautifulSoup(page)

Once we have a BeautifulSoup object on our hands, we can then iterate through each element one by one and extract text from it. We can do this by using the find() method of the BeautifulSoup class.

for (n, el) in soup:
    print("Title: " + n)

Next, for each element in our list, we’ll want to print out its text value and its length.

print("Length: " + len(el))

What are the types of regular expressions in Python?

Python has many different types of regular expressions. Most often you’ll want to use re.findall(), which is a function that will return all the matches for a given pattern in a string. Here’s a wholeblogs:

import re

domains = [ "www." , "https://www.wholeblogs.com/", "https://www.wholeblogs.com/"]

print(domains)

print("{}".format(re.findall("^[A-Za-z0-9]+$", domains)))

print("Found {} total matches." .format(len(re.findall("^[A-Za-z0-9]+$", domains)))

{"www." : 1, "https://www.wholeblogs.com/" : 1, "https://www.wholeblogs.com/" : 2} Found 3 total matches.

How to fix RegEx all URLs that do NOT contain a string?

Let’s say you have a string that should always be there.

You can use the ‘.’ to match any character in the string and then the ‘*’ to match 0 or more of any character.
So your expression would look like this: url = url “.” “*”

Now let’s say you want to parse a URL that doesn’t contain a string. What do you do?

Well, we can use what is called an empty pattern, which is just a pattern that matches nothing.
If we’re trying to parse https://www.wholeblogs.com/, then our regular expression will look like this: url = url “.” “*”

If you wanted to see if https://www.wholeblogs.com/ was available, your expression would read, url = url “.” “*” “\.” “\*”

What is the importance of regular expression?

Regular expressions are used to match patterns in text, such as extracting an email address from a string.

Here are another wholeblogs where I’ll show you how to extract an IP address from a URL:

# Regular Expression for parsing URLs

re = re.compile(r'http://[^\s]+\.[^\./]+$')

print("The URL is %s" % re.sub('', r'', url))

# Regular expression for parsing IP addresses

re = re.compile(r'([0-9]{1,3}\.[0-9]{1,3})\.([a-zA-Z0-9\-]+\.){3}\d+\.com')

print("The IP address is %s" % re.sub('', r'', ip_address))

How to fix Beautiful soup for web scraping returns none in Python

Beautiful Soup is a Python library for parsing web pages.

To parse the website, we have to find all the tags in it and iterate through them one by one. The function below generates a list of tag names and then loops through that list to find all the tags with a specific name.

import urllib2 import re

url = "https://www.wholeblogs.com/"

tags = [t for t in re.findall(r"\b[A-Z]+\.com\b", url) if t != ""]

for tag in tags:
    print(tag)


<h3>How to webscrape dynamic websites in Python?</h3>
There are many ways to webscrape websites depending on what you're looking for. One way is to use Python's urllib library which can do client-side webscraping to pull information from the URL. You can't really do server-side scraping with urllib, but it's a pretty good start if you want to do some basic stuff.

To get started let's import and print the URL of our website:


import urllib2

url = "https://www.wholeblogs.com/"

print(url)

You’ll need to understand regular expressions to successfully parse a website using urllib2. Let’s say we want to extract a title from this page.

First, let’s define the pattern we’re going to use: \W+\.\w+\.\w*

title = re.search("\\W+\.\\w+\\.\\w*", url)

If you run that code you should see output like this.

Web crawling vs web scraping

Scraping is the process of extracting data from a website without the website owners knowing. The data can be then processed, transformed, and stored for use in other programs or websites. A typical wholeblogs might be using web scraping to extract data from a Wikipedia article.

Crawling is the process of retrieving information from a website by visiting it sequentially and following its links. This type of crawling involves visiting every page on a site and checking if they contain what you’re looking for.