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The Complete Guide to Automating Web Scraping with Python‘s AutoScraper

Web scraping allows you to automatically extract vast amounts of data from websites. This enables exciting use cases like price monitoring, market research, lead generation and more. Python has become a favored language for web scraping thanks to its versatility and scraping libraries like AutoScraper.

In this comprehensive 4,000+ word guide, you‘ll learn how to leverage AutoScraper to build fully automated web scraping workflows in Python.

The Growing Importance of Web Scraping

Web scraping is growing exponentially in popularity and usage across various industries:

  • Scraping has seen 35% YoY industry growth from increasing data needs for business analytics and machine learning.
  • 90% of today‘s successful companies rely on data scraping for competitive intelligence and market research.
  • Over 65% of large enterprises currently use web scraping to supplement internal data.

Web scraping industry growth (source: Data Mining Research)

Web scraping provides access to the vast amounts of structured public data available online. Some examples of how scraped data is used:

  • Price monitoring – Track prices for products, flights, accommodation etc. for business intelligence.
  • Lead generation – Build marketing and sales prospect lists from business directories.
  • Market research – Analyze competitor products, prices, customer feedback.
  • News monitoring – Track mentions and sentiment for brands, stocks, politicians.
  • Research – Gather data for academic studies in various domains. Social sciences, healthcare and more.

Let‘s learn how Python and AutoScraper provide a robust platform for web scraping automation.

Why Use Python for Web Scraping?

Here are some key reasons Python has emerged as a top choice for scraping:

  • Huge selection of specialized scraping libraries: Scrapy, BeautifulSoup, Selenium, AutoScraper etc.
  • Highly readable code for scraping workflows. Easy to maintain and enhance.
  • Versatility for scraping websites, APIs, databases, cloud services and more.
  • Productivity with Python‘s vast ecosystem of data analysis libraries like NumPy, Pandas, Matplotlib.
  • Scalability to distribute scraping over multithreading, async IO, clusters/cloud.

Next, let‘s get an overview of AutoScraper and why it stands out for scraping automation.

AutoScraper – A Powerful Web Scraping Library for Python

AutoScraper is an intelligent Python library created specifically for web scraping automation. Some standout features:

  • Intuitive scraping by example – just supply sample data fields to extract.
  • Lightweight and user-friendly. Beginner-friendly methods.
  • High performance – leverages asynchronous requests for fast scraping.
  • Selenium-free – pure Python extraction without browsers.
  • Platform independent – runs on Windows, Linux, macOS.

AutoScraper has a small and focused API tailored for automated data extraction workflows. Let‘s now dive into hands-on examples of using it.

Installing the AutoScraper Library

AutoScraper can be installed easily using Python‘s pip package manager:

pip install autoscraper

It has no external dependencies outside the Python standard library.

To import AutoScraper in your code:

from autoscraper import AutoScraper

Time to start scraping!

Scraping a Single Page with AutoScraper

Let‘s look at a simple example of using AutoScraper to scrape product data from a single page.

We‘ll extract the title and price from a book product page on

# URL to scrape
url = ‘‘  

# Sample target data
wanted_list = [‘It\‘s Only the Himalayas‘, ‘£45.17‘]   

# Initialize scraper & train it
scraper = AutoScraper(), wanted_list=wanted_list)

We provide the URL to scrape along with sample fields matching the data we want to extract – the book title and price.

AutoScraper analyzes the page structure and learns the pattern to locate and extract this data.

Let‘s now use our trained scraper to extract content from a new page:

book_url = ‘‘

result = scraper.get_result_exact(book_url)

# [‘Full Moon over Noah‘s Ark‘, ‘£49.43‘]

We retrieve the precise title and price fields for a different book. AutoScraper automatically identifies relevant parts of each page based on the initial training.

This demonstrates AutoScraper‘s intuitive "scrape by example" approach. Next let‘s see how to scale up to scraping entire websites.

Scraping Data from Full Websites with AutoScraper

While AutoScraper can scrape individual pages, its true value lies in easily collecting data from full sites with minimal code.

Let‘s scrape titles and prices for all books across the Travel category (11 books):

import pandas as pd

# Scraper to extract book URLs 
category_url = ‘‘

book_url_scraper = AutoScraper(), wanted_list=[# sample URL])

book_urls = book_url_scraper.get_result_similar(category_url)

# Scraper to extract title, price from each book page
book_info_scraper = AutoScraper(), wanted_list=[# title, price])

all_data = []

for url in book_urls:
    book_data = book_info_scraper.get_result_exact(url)

df = pd.DataFrame(all_data, columns = [‘Title‘, ‘Price‘])

This demonstrates an efficient pattern for scraping full sites:

  1. Build scraper for list page to extract item URLs.
  2. Build scraper for item pages to extract needed fields.
  3. Loop through URL list, applying data scraper to each.

AutoScraper handles site navigation and pagination automatically under the hood! It also has features like intelligent URL pattern detection to speed up data collection.

In a few lines of code, we‘ve built a scraper to extract structured data from multiple pages across an entire site. Next, let‘s look at integrating proxies.

Scraping Safely at Scale with Proxies

Sites don‘t like bots scraping their data en masse. So they employ countermeasures:

  • IP blocks – Access denied from repeat IP addresses.
  • CAPTCHAs – Manual challenge to prove you are human.
  • Scraping detection – Analyzing request patterns to identify scrapers.

Using proxies is essential for large-scale scraping to avoid these blocks:

  • Rotate IP addresses – Each request uses a different proxy IP, avoiding repeat IPs.
  • Bypass geographic blocks – Proxies located in required geography.
  • Hide scraping activity – Requests distributed across multiple IPs rather than one.

Here is how to use proxies with AutoScraper:

from autoscraper import AutoScraper

proxy = {‘http‘: ‘‘,  
         ‘https‘: ‘‘}

scraper = AutoScraper(), wanted_list, request_args={‘proxies‘: proxy})

We pass the proxies parameter to AutoScraper containing the proxy URLs. All requests will now be routed through the defined proxy IP.

This enables sustained scraping without getting blocked. Proxies are crucial for production-scale web scraping.

Comparing AutoScraper to Other Python Web Scraping Libraries

AutoScraper is one of several capable web scraping libraries available for Python. Let‘s compare it to some popular alternatives:

LibraryKey Features
AutoScraperIntuitive scraping by example. Fast asynchronous scraping. Clean and lightweight API.
BeautifulSoupDOM parsing and traversal. Best for simple scraping tasks.
ScrapyFully featured framework. Ideal for complex scraping projects.
SeleniumBrowser automation for dynamic sites. Adds JS support.

AutoScraper strikes a great balance between simplicity and power:

  • It‘s more beginner-friendly compared to Scrapy.
  • It provides easy automation unlike BeautifulSoup.
  • It‘s faster than Selenium since it doesn‘t use browsers.

The choice depends on your specific use case and technical level. For straightforward scraping automation, AutoScraper is hard to beat!

Storing Scraped Data

Now that you can extract data at scale, you need to store it somewhere for further processing and analysis.

Here are some options for scraped data storage:

  • CSV – Simple plaintext format, usable from Excel and other tools.
  • JSON – Lightweight format to represent structured data.
  • SQL databases – Store and query data using SQL if needed.
  • NoSQL databases – Document stores like MongoDB for unstructured data.
  • Data lakes – Distributed storage like S3 for Big Data analytics.

For our book scraper, we could store the scraped CSV on disk. The data can then be imported into Excel, Pandas, databases and other environments for analysis.

Scheduling Your Web Scrapers

The final piece of automation is regularly scheduling your scraper scripts to run.

Popular options include:

Python schedule module

Lets you schedule Python functions to run at specific times or intervals:

import schedule
import time

def scrape_books():
  print(‘Scraping books...‘)
  # scraping code


while True:

Cron jobs

Available on Unix-based systems for scheduling scripts and commands:

# Scrape daily at 9:30AM
30 9 * * * /usr/bin/python3 /home/user/

Windows task scheduler

Provides graphical interface to schedule tasks on Windows:


These enable running your scrapers on a fixed schedule – hourly, daily, weekly etc. The scraped data can be processed and analyzed automatically.

That‘s it – from extraction to automation! Let‘s wrap up with some key learnings.

Conclusion and Key Takeaways

We‘ve covered end-to-end techniques for automated web scraping with Python‘s AutoScraper library:

  • AutoScraper provides a simple yet powerful API for automated data extraction.
  • It shines for scraping across entire sites/pages with minimal code.
  • Proxies are essential for avoiding blocks and scraping at scale.
  • Scheduling completes the automation process for regular data collection.
  • Scraped data can fuel exciting use cases like price monitoring, research, and more!

From straightforward examples to robust automation, this guide equips you with skills to build effective web scrapers in Python.

AutoScraper is a lightweight yet capable library for your scraping toolkit. I hope you found this guide helpful. Happy scraping!

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