Adventures in Machine Learning

Scraping Yahoo Finance with Scrapy: A Guide to Efficient Data Extraction

Introduction to Yahoo Finance Web Scraping

In today’s digital age, data is everything. As such, individuals and businesses alike are always searching for ways to get their hands on reliable and up-to-date data.

One of the best ways to achieve this is through web scraping. Web scraping involves the extraction of large amounts of data from websites, and it can be done in many ways.

While some may opt for APIs, others prefer to scrape websites directly. In the case of Yahoo Finance, both methods are viable options.

In this article, we will delve into the concept of web scraping and explore the advantages of scraping Yahoo Finance. We will also touch on the use of Scrapy, a popular web scraping framework, and the requirements needed to get started.

Yahoo Finance API vs. Web Scraping

One of the most common ways to extract data from Yahoo Finance is through their API.

However, the use of an API comes with some limitations. For one, the data provided by the API may not be as comprehensive as that available on the website.

Additionally, there may be restrictions on the number of requests one can make using an API. On the other hand, web scraping allows for the collection of a more extensive range of data from the Yahoo Finance website.

Furthermore, there are no limitations on the number of requests that one can make. This gives web scraping an edge over using Yahoo Finance’s API.

Using Scrapy for Web Scraping

Scrapy is a powerful web scraping framework that allows for the extraction of data from websites quickly and efficiently. It is written in Python, making it a favourite among programmers who are familiar with the language.

The framework is equipped with built-in tools that enable the scraping of large amounts of data from websites, and it can be customised to meet the requirements of a specific project. As such, Scrapy is an ideal tool to use when scraping Yahoo Finance.

Bulk Scraping Requirement

The scraping of data from Yahoo Finance is often done in bulk, with large amounts of data being extracted at once. As such, this requires a considerable amount of computing power and may take up a significant amount of time.

To achieve this, one needs to have a top-notch computer with sufficient memory and processing power. Additionally, one may need to utilise cloud computing services to speed up the process and reduce the risk of overburdening their computer.

Web Scraping Requirements

To get started with web scraping, one needs a basic understanding of Python programming. This is because most popular web scraping frameworks are written in Python.

Scrapy, in particular, requires some knowledge of Python, but it is not difficult to learn. Furthermore, there are countless resources on the internet that can help one get started with Python programming.

Another requirement for web scraping is a good understanding of HTML. This is necessary as most websites are written in HTML, and one needs to understand it to extract data from a website.

Finally, one needs a web browser to scrape data from websites. This is because web scraping requires one to access the website’s source code, which can only be done through the browser’s developer tools.


In conclusion, web scraping is an essential tool that can be used to extract data from websites. Yahoo Finance, in particular, provides a wealth of information that can be useful for analysis and other purposes.

While the use of an API is a viable option, web scraping allows for the extraction of a wider range of data from the website. Getting started with web scraping requires a good understanding of Python, Scrapy web scraping framework, HTML and access to a web browser that includes developer tools.

Ultimately, with the right resources, anyone can get started with web scraping and taking advantage of the data available on Yahoo Finance or other websites.

3) Installation and Setup of Scrapy

Scrapy is a powerful web scraping framework that is widely used for data extraction from different websites. To get started with using Scrapy, one must have it installed in their system.

Installing Scrapy is pretty straightforward, and it can be done using pip, the default package manager for Python. To install Scrapy, the first step is to open a command prompt or terminal and run the following command.


pip install scrapy


After running the command, pip will automatically download and install all necessary Scrapy dependencies. The process may take a few minutes, depending on the system and internet speed.

Once installation is complete, one can verify it by running the following command in the terminal. “`

scrapy version


If Scrapy has been installed successfully, the terminal should return its version number. After successfully installing Scrapy, one can create a new Scrapy project by running the following command.


scrapy startproject projectname


Where “projectname” is the name of the Scrapy project being created. This command creates a new Scrapy project folder that consists of files and directories necessary to run a web scraper.

Among these files is the “spiders” folder where the script that does the scraping is located.

4) Creating our First Scraper using Scrapy

With Scrapy successfully installed and set up, one can now start developing the web scraper. In this section, we will look at how to create a basic scraper using Scrapy.

To create a basic scraper using Scrapy, we have to define a Spider class that inherits from the Scrapy.Spider class. This class will represent the scraper and define its behaviour, including what URLs to visit and how to scrape data from them.


import scrapy

class MySpider(scrapy.Spider):

name = ‘myspider’

start_urls = [‘’,]

def parse(self, response):

for quote in response.css(‘div.quote’):

yield {

‘text’: quote.css(‘span.text::text’).get(),

‘author’: quote.css(‘span small::text’).get(),

‘tags’: quote.css(‘div.tags a.tag::text’).getall(),



In the code snippet above, we have defined a Spider class called “MySpider,” which will scrape data from the “” website. The “start_urls” list contains the URLs that the scraper will visit to fetch data.

When the scraper visits a page, it calls the “parse” method, which is responsible for extracting data from web pages. In our example, the “parse” method uses Scrapy selectors to extract the text of the quote, the author of the quote, and the tags associated with the quote.

The scraped data is then stored in a dictionary and yielded back to Scrapy, which writes it to a CSV file. To run our scraper, we have to save the code snippet above to a .py file in the “spiders” folder of our Scrapy project and run the following command in the terminal.


scrapy crawl myspider -o quotes.csv


This command runs our spider and saves the results to a CSV file called “quotes.csv”. The -o option specifies the output destination, which can be a JSON, CSV, or XML file.


In summary, the use of Scrapy as a web scraping tool is a powerful way to extract data from websites. With its power and versatility, Scrapy allows for the extraction of data quickly and efficiently.

The creation of a scraper using Scrapy involves defining a Spider class that defines the behaviour of the scraper, including what URLs to visit and what data to extract. The scraped data can then be saved to different output formats, such as CSV, JSON or XML files, for further analysis.



The Scrapy framework is a powerful tool for web scraping that provides unique advantages over other web scraping methodologies. In this article, we have discussed the installation and setup of Scrapy, as well as the creation of our first web scraper using Scrapy.

There are a number of advantages that come with using the Scrapy framework for web scraping. Let’s take a closer look.

Advantages of Scrapy Framework

1. Concurrency and Distributed Scraping: Scrapy is designed to leverage asynchronous programming concepts, thereby offering concurrency support.

This means that a single Scrapy spider can send multiple requests simultaneously, thereby reducing the time it takes to scrape data from a website. Additionally, Scrapy supports distributed crawling, meaning that multiple Scrapy processes can run simultaneously, thereby allowing web scraping to scale across multiple machines or servers.

2. Powerful Data Extraction: Scrapy uses powerful selectors such as XPath and CSS to extract data from web pages.

This allows developers to specify precisely which data they want to scrape and how to scrape it. This makes Scrapy an efficient and effective tool for scraping large amounts of data with minimal effort.

3. Supports Multiple Output Formats: Scrapy supports multiple output formats, including CSV, JSON and XML.

This allows developers to extract data in the format that is best suited for their needs, so they can spend more time on analyzing and processing the data instead of converting it to the desired format. 4.

Versatility: Scrapy can be used to scrape data from virtually any website, ranging from simple websites to complex, dynamic websites. Its versatility makes it a popular choice for web scraping in various industries, including finance, e-commerce, and marketing.

5. Active Community and Support: Scrapy has a large and active user community that provides support and advice on how to use the framework.

Additionally, Scrapy has detailed documentation that makes it easy for beginners to get started with web scraping.


Overall, Scrapy is an excellent tool for web scraping that offers several advantages over other methods. Its concurrency features, powerful data extraction capabilities, support for multiple output formats, versatility, and active community make it an ideal choice for developers who need to scrape data from websites.

With Scrapy, you can extract large amounts of data from websites quickly and efficiently while saving time and streamlining your web scraping process. In conclusion, web scraping is an essential tool for extracting data from websites that can be useful in many industries.

Scrapy, a web scraping framework, offers unique advantages such as concurrency, powerful data extraction, and support for multiple output formats. With Scrapy, developers can scrape data from any website quickly and efficiently, thereby reducing the time and effort needed for web scraping.

The article also highlights the importance of having a good understanding of Python, Scrapy, HTML and using a web browser with developer tools. Whether one is a beginner or an experienced web scraper, Scrapy is a versatile tool that can help streamline the web scraping process, thus making it easier to extract large amounts of data from websites without hassle.

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