Boost Your Web Scraping Efficiency by 10x with Python Libraries
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Chapter 1: Understanding Web Scraping Challenges
Have you ever faced the frustration of slow web scraping? If you have numerous websites to scrape, you're certainly not alone. A clean dataset is crucial for data science, but many datasets aren't readily available. This is where web scraping shines, allowing us to automate the extraction process rather than entering data manually into spreadsheets.
However, the scraping process can be painfully slow. Without optimization, it can take hours or even days to scrape multiple sites. So, what's the solution? This article will guide you through it.
In this piece, we will explore how to enhance web scraping speed in Python by leveraging multithreading. We will utilize a library called concurrent.futures to achieve this. With this approach, you can potentially increase your scraping speed by up to 10 times, depending on the size of your dataset. While you might not notice significant speed improvements with smaller datasets, larger datasets will showcase the advantages. Let's dive in!
What is Multithreading?
To grasp the concept of multithreading, we need to understand a couple of terms: asynchronous (async) and thread. Asynchronous refers to executing multiple tasks simultaneously, while synchronous means tasks are performed one after the other.
Threads are the smallest unit of processing that can be managed independently by the operating system. A computer can run multiple threads, which is where multithreading comes into play. The concurrent.futures library allows you to execute jobs asynchronously, enabling faster task completion.
In web scraping, we often apply functions to numerous links, which can be treated as independent data. Thus, we can partition the data into chunks and distribute these across available threads.
Implementation
Now that we have a solid understanding of multithreading, let’s move on to implementation.
The Source
For our example, we will scrape data from FBRef.com, a site that aggregates football statistics from various leagues, from the Premier League to Major League Soccer. This site provides data on both teams and individual players.
The Problem
Our goal is to extract player names along with links to their profiles and statistics. Note that this article will not cover the basics of web scraping; instead, it will demonstrate how to implement multithreading effectively.
The Initial Code
We start with a basic code structure for extracting player names and links. Unfortunately, since there isn't a single page that lists all players, we need to open multiple pages, which slows down the process considerably. The initial execution time for this task is 15 minutes and 29 seconds.
Fortunately, the concurrent.futures library can help us overcome this challenge.
The Multithreading Pipeline
Given the slow scraping process, we can employ the multithreading concept to speed things up. We will utilize the ThreadPoolExecutor object from the concurrent.futures library.
The basic structure for implementing multithreading is as follows:
- Import necessary libraries.
- Create a function for data scraping.
- Execute the multithreading process.
The Code with concurrent.futures
So, how do we apply multithreading to our initial function? Here's what the modified code looks like:
By wrapping the previous code in a function and employing the ThreadPoolExecutor, we can effectively map the function to data chunks. In my case, the revised code completed the task in just 72 seconds, which is nearly 12 times faster than before! For larger datasets, this time-saving becomes even more pronounced.
In another scenario, I faced a project requiring the scraping of over a hundred thousand links. Initially, it was projected to take around 65 hours. Thanks to multithreading, I finished in just 6 hours! Imagine how much time that saves!
Final Remarks
Congratulations! You have now learned how to implement multithreading to enhance your web scraping speed in Python. This technique can increase your scraping efficiency by up to 10 times, freeing you to tackle other tasks or even enjoy some leisure time watching your favorite shows!
For those interested in the complete notebook, you can find it [here](link). Thank you for taking the time to read my article!
Chapter 2: Enhancing Your Web Scraping Skills
Learn how to speed up your web scraping process with this Python tutorial that focuses on using multithreading techniques for better performance.
Chapter 3: Mastering Parallelism in Python
Dive into the concepts of parallelism and concurrency in Python to achieve faster web scraping results.