Web Scraping with Python Tutorial

Suppose you want to scrape competitor websites for their pricing page information. What will you do? Copy-pasting or entering data manually is too slow, time-consuming, and error-prone. You can automate it easily using Python.

Let’s see how to scrape webpages using python in this tutorial.

What are the different Python web scraping libraries?

Python is popular for web scraping owing to the abundance of third-party libraries that can scrap complex HTML structures, parse text, and interact with HTML form. Here, we’ve listed some top Python web scraping libraries.

• Urllib3 is a powerful HTTP client library for Python. This makes it easy to perform HTTP requests programmatically. It handles HTTP headers, retries, redirects, and other low-level details, making it an excellent library for web scraping. It also supports SSL verification, connection pooling, and proxying.
• BeautifulSoup allows you to parse HTML and XML documents. You can easily navigate through the HTML document tree and extract tags, meta titles, attributes, text, and other content using API. BeautifulSoup is also known for its robust error handling.
• MechanicalSoup automates the interaction between a web browser and a website efficiently. It provides a high-level API for web scraping that simulates human behavior. With MechanicalSoup, you can interact with HTML forms, click buttons, and interact with elements like a real user.
• Requests is a simple yet powerful Python library for making HTTP requests. It is designed to be easy to use and intuitive, with a clean and consistent API. With Requests, you can easily send GET and POST requests, and handle cookies, authentication, and other HTTP features. It is also widely used in web scraping due to its simplicity and ease of use.
• Selenium allows you to automate web browsers such as Chrome, Firefox, and Safari and simulate human interaction with websites. You can click buttons, fill out forms, scroll pages, and perform other actions. It is also used for testing web applications and automating repetitive tasks.
• Pandas allow storing and manipulating data in various formats, including CSV, Excel, JSON, and SQL databases. Using Pandas, you can easily clean, transform, and analyze data extracted from websites.

Extract text from any webpage in just one click. Head over to Nanonets website scraper, Add the URL and click “Scrape,” and download the webpage text as a file instantly. Try it for free now.

How to scrape data from websites using python?

Let’s take a look at the step-by-step process of using Python to scrape website data.

Step 1: Choose the Website and Webpage URL

The first step is to select the website you want to scrape. For this particular tutorial, let’s scrape https://www.imdb.com/. We will try to extract data on the top-rated movies on the website.

Step 2: Inspect the website

Now the next step is to understand the website structure. Understand what the attributes of the elements that are of your interest are. Right-click on the website to select “Inspect”. This will open the HTML code. Use the inspector tool to see the name of all the elements to use in the code.

Note these elements’ class names and ids as they will be used in the Python code.

Step 3: Installing the important libraries

As discussed earlier, Python has several web scraping libraries. Today, we will use the following libraries:

• requests – for making HTTP requests to the website
• BeautifulSoup – for parsing the HTML code
• pandas – for storing the scraped data in a data frame
• time – for adding a delay between requests to avoid overwhelming the website with requests

Install the libraries using the following command

pip install requests beautifulsoup4 pandas time

Step 4: Write the Python code

Now, it’s time to write the main python code. The code will perform the following steps:

• Using requests to send an HTTP GET request
• Using BeautifulSoup to parse the HTML code
• Extracting the required data from the HTML code
• Store the information in a pandas dataframe
• Add a delay between requests to avoid overwhelming the website with requests

Here’s the Python code to scrape the top-rated movies from IMDb:

import requests
from bs4 import BeautifulSoup
import pandas as pd
import time
# URL of the website to scrape
url = "https://www.imdb.com/chart/top"
# Send an HTTP GET request to the website
response = requests.get(url)
# Parse the HTML code using BeautifulSoup
soup = BeautifulSoup(response.content, 'html.parser')
# Extract the relevant information from the HTML code
movies = []
for row in soup.select('tbody.lister-list tr'):
title = row.find('td', class_='titleColumn').find('a').get_text()
year = row.find('td', class_='titleColumn').find('span', class_='secondaryInfo').get_text()[1:-1]
rating = row.find('td', class_='ratingColumn imdbRating').find('strong').get_text()
movies.append([title, year, rating])
# Store the information in a pandas dataframe
df = pd.DataFrame(movies, columns=['Title', 'Year', 'Rating'])
# Add a delay between requests to avoid overwhelming the website with requests
time.sleep(1)

Step 5: Exporting the extracted data

Now, let’s export the data as a CSV file. We will use the pandas library.

# Export the data to a CSV file
df.to_csv('top-rated-movies.csv', index=False)

Step 6: Verify the extracted data

Open the CSV file to verify that the data has been successfully scraped and stored.

We hope this tutorial will help you extract data from webpages easily.

Extract text from any webpage in just one click. Head over to Nanonets website scraper, Add the URL and click “Scrape,” and download the webpage text as a file instantly. Try it for free now.

How to parse text from website?

You can parse website text easily using BeautifulSoup or lxml. Here are the steps involved along with the code.

• We will send an HTTP request to the URL and get the webpage’s HTML content.
• Once you have the HTMl structure, we will use BeautifulSoup’s find() method to locate a specific HTML tag or attribute.
• And then extract the text content with the text attribute.

Here’s a code of how to parse text from a website using BeautifulSoup:

import requests
from bs4 import BeautifulSoup
# Send an HTTP request to the URL of the webpage you want to access
response = requests.get("https://www.example.com")
# Parse the HTML content using BeautifulSoup
soup = BeautifulSoup(response.content, "html.parser")
# Extract the text content of the webpage
text = soup.get_text()
print(text)

How to scrape HTML forms using Python?

To scrape HTML forms using Python, you can use a library such as BeautifulSoup, lxml, or mechanize. Here are the general steps:

1. Send an HTTP request to the URL of the webpage with the form you want to scrape. The server responds to the request by returning the HTML content of the webpage.
2. Once you have accessed the HTML content, you can use an HTML parser to locate the form you want to scrape. For example, you can use BeautifulSoup’s find() method to locate the form tag.
3. Once you have located the form, you can extract the input fields and their corresponding values using the HTML parser. For example, you can use BeautifulSoup’s find_all() method to locate all input tags within the form, and then extract their name and value attributes.
4. You can then use this data to submit the form or perform further data processing.

Here’s an example of how to scrape an HTML form using mechanize:

import mechanize
# Create a mechanize browser object
browser = mechanize.Browser()
# Send an HTTP request to the URL of the webpage with the form you want to scrape
browser.open("https://www.example.com/form")
# Select the form to scrape
browser.select_form(nr=0)
# Extract the input fields and their corresponding values
for control in browser.form.controls:
print(control.name, control.value)
# Submit the form
browser.submit()

Extract text from any webpage in just one click. Head over to Nanonets website scraper, Add the URL and click “Scrape,” and download the webpage text as a file instantly. Try it for free now.

Comparing all Python web scraping libraries

Let’s compare all the python web scraping libraries. All of them have excellent community support, but they differ in ease of use and their use cases, as mentioned in the start of the blog.

Conclusion

Python is an excellent option for scraping website data in real-time. Another alternative is to use automated website scraping tools like Nanonets. You can use the free website-to-text tool. But, if you need to automate web scraping for larger projects, you can contact Nanonets.

Extract text from any webpage in just one click. Head over to Nanonets website scraper, Add the URL and click “Scrape,” and download the webpage text as a file instantly. Try it for free now.

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Source: https://nanonets.com/blog/web-scraping-with-python-tutorial/