How to automate web scraping using python 

Web scraping in simple meaning is extracting a large amount of data from web pages in a few minutes. I would like to share some informatical information

About How to automate web scripting using python, Also There are lots of case studies defined auto scraper on CrawlMagic, which is a specialized Automatic web scraper and Web Automation

Let’s focus on a topic, One of the most popular tools used for web scraping is the “Auto Scraper” library. This library is designed to automate the web scraping process and provide you with the extracted data in a structured format. In this tutorial, we will show you how to use Python and Auto Scraper to automate web scraping.

What is Auto Scraper?

Auto Scraper is a Python library that automates the web scraping process by extracting data from websites based on the HTML structure. It uses machine learning algorithms to determine the relevant data on a webpage and extract it automatically. With Auto Scraper, you can easily scrape data from multiple web pages at once, making it an efficient tool for data mining and research.

How to Automate Web Scraping using Python and Auto Scraper

Here are the steps to automate web scraping using Python and Auto Scraper:

Step 1: Install Auto Scraper

To install Auto Scraper, you need to run the following command in your terminal or command prompt:

pip install autoscraper

Step 2: Import the necessary libraries

After installing Auto Scraper, you need to import it along with other necessary libraries such as requests and pandas. You can do this by running the following code:

import requests
import pandas as pd
from autoscraper import AutoScraper

Step 3: Identify the data to be scraped

Next, you need to identify the data that you want to scrape from the website. This can be done by inspecting the HTML code of the webpage and identifying the relevant tags and attributes. For example, if you want to scrape the names of products from an e-commerce website, you can identify the HTML tag that contains the product name.

Step 4: Train the scraper

After identifying the data to be scraped, you can train the Auto Scraper by providing it with a list of URLs that contain the data. The Auto Scraper will then analyze the HTML structure of the webpages and extract the relevant data automatically. You can do this by running the following code:

url = 'https://www.example.com/products'
scraper = AutoScraper()
scraper.build(url, [ 'product_name', 'product_price' ])

In the above code, “product_name” and “product_price” are the data fields that you want to extract from the webpage.

Step 5: Scrape the data

Finally, you can scrape the data by providing the Auto Scraper with the URL of the webpage that you want to scrape. You can do this by running the following code:

url = 'https://www.example.com/products'
data = scraper.get_result_similar(url)

The above code will scrape the data from the specified URL and store it in a list. You can then convert this list to a Pandas dataframe and save it in a CSV file using the following code:

df = pd.DataFrame(data, columns=[ 'product_name', 'product_price' ])
df.to_csv('products.csv', index=False)

In the last, Automating web scraping using Python and Auto Scraper can save you a lot of time and effort, especially when dealing with large amounts of data. With Auto Scraper, you can easily extract data from multiple web pages at once and store it in a structured format. By following the above steps, you can start automating web scraping using Python and Auto Scraper today!