Python Web Scraping 101

Introduction

Web scraping using Python is actually very easy once you know how to do it. This guide will cover the basics of Python web scraping, including how to go about getting your environment set up properly, and then diving into the specific language constructs that you’ll need to use in order to scrape data from websites. At the end of this article, you should have all of the tools you need in order to start scraping websites from scratch using Python!

Simple Web Scraping

The Python library BeautifulSoup is great for getting data from websites. The best way to use BeautifulSoup is to iterate through all of the results on the webpage and do a search on every iteration. This will let you choose which site matches your needs and take out specific information for just that website. In order to do this, you need to make sure you have the page in an html format before you begin. If not, then load it up in a browser and copy it into a .txt file with WordPad or notepad++ so that formatting is preserved. Then, read the code below

Parsing HTML with Beautiful Soup

You can use the html.parser module to parse HTML. You create an instance of it and pass in the string containing the HTML and then it splits up the tags, creating a tree structure that you can traverse using any node you want.

Example Python Code:

import requests
from bs4 import BeautifulSoup

# Define the URL of the web page to be scraped
url = 'https://www.example.com'

# Send a request to the web page and get the HTML content
response = requests.get(url)
html_content = response.content

# Use BeautifulSoup to parse the HTML content and extract the data
soup = BeautifulSoup(html_content, 'html.parser')
title = soup.title.text.strip()

# Print the extracted data
print('Title:', title)

In this example, we start by importing the necessary libraries, requests and BeautifulSoup. We define the URL of the web page to be scraped and use the requests library to send a request to the web page and get the HTML content.

We then use the BeautifulSoup library to parse the HTML content and extract the data we need. In this case, we extract the title of the web page using the soup.title.text attribute and strip any leading or trailing whitespace using the strip() method.

Finally, we print the extracted data to the console.

Selective data extraction

Web scraping is the process of extracting data from web pages by programming a computer to recognize and parse the desired data.

Python is often used to scrape websites because it is an open-source language that has many free libraries for web scraping. In this tutorial, I’ll show you how to use Python for web scraping. First, we need to install Python and create a new project folder on your computer.

Then, we will import some libraries that will allow us to read HTML files and get information from them like links, tables and text content. Next, we’ll write a function that extracts the specific information that we are looking for from the webpage using regular expressions. Finally, we’ll run our code so it can extract all of the desired information from one webpage!

Machine Learning with OpenCV

In this blog post, we will be looking at a simple Python script to scrape and download images from the web. This process can be used for a variety of purposes, including data mining and research. We will also explore how to create an Image Hashing Model with OpenCV in order to find similar images based on color histograms.

Why learn web scraping?

Web scraping is a technique for extracting data from websites. Web scraping can be accomplished through a number of technologies, but most often it is performed using a programming language that supports scraping. In this post we’ll use Python to scrape data from the Bureau of Labor Statistics website.

We’ll see how to use Python’s built-in urllib and urllib2 libraries to fetch data from the BLS website about employment and unemployment in Wyoming.

Becoming an expert in web scraping

Web scraping is a data extraction technique that involves downloading and analyzing content from web pages. Data scraping is most often used for extracting information that is not otherwise accessible, such as information behind a paywall, or to gather statistics about the number of people who visit a site. 

While there are many different tools that can be used for web scraping, one of the most popular choices is Python. In this post we will go over some basics of how to scrape websites with Python.