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For finance teams, data is everything. Making informed decisions requires up-to-date and accurate financial information. This includes analyzing market trends, spotting investment opportunities, and conducting thorough research.
Enter web scraping. Web scraping is the process of extracting data from websites. It is a powerful technique that revolutionizes data collection and analysis. With vast amounts of online data, web scraping has become an essential tool for businesses and individuals.
The decision among the many online scraping solutions available typically comes down to how skilled you are at programming and how difficult the work is. Many well-known Python libraries, like Beautiful Soup, Scrapy, and Selenium, have varying functionalities.
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What is web scraping?
Web scraping is the process of extracting data from websites and storing it in a form that is useful for your business. Data extracted from websites is usually unstructured and needs to be converted into a structured form to be used for running analysis, research, or even training AI models.
If you have ever copied and pasted data from any website into an Excel spreadsheet or a Word document, essentially, it is web scraping at a very small scale. The copy-paste method is useful when web scraping needs to be done for personal projects or one-time use cases. However, when businesses need to scrape data from websites, they usually need to scrape from multiple websites and pages, and it also needs to be done repeatedly. Doing this manually would be extremely time-consuming and error-prone. Hence, organizations turn to web scraping tools that automatically extract data from websites based on business requirements. These tools can also transform data to make it usable, since most extracted data is unstructured, and upload it to the required destination.
The web scraping process
The web scraping process follows a set of common principles across all tools and use cases. These principles stay the same for this entire web scraping process:
- Identify target URLs: Users need to manually select the URLs of websites that they want to extract data from and keep them ready to input into the web scraping tool.
- Scrape data from the websites: Once you input the website URL into the web scraping tool, the web scraper will retrieve and extract all the data on the website.
- Parse the extracted data: The data scraped from websites is usually unstructured and needs to be parsed to make it useful for analysis. This can be done manually or can be automated with the help of advanced web scraping tools.
- Upload/Save the final structured data: Once the data is parsed and structured into usable form, it can be saved to the desired location. This data can be uploaded into databases or saved as XLSX, CSV, TXT, or any other required format.
Why use Python for web scraping?
Python is a popular programming language for web scraping because it has many libraries and frameworks that make it easy to extract data from websites.
Using Python for web scraping offers several advantages over other web scraping techniques:
- Dynamic websites: Dynamic web pages are created using JavaScript or other scripting languages. These pages often contain visible elements once the page is fully loaded or when the user interacts with them. Selenium can interact with these elements, making it a powerful tool for scraping data from dynamic web pages.
- User interactions: Selenium can simulate user interactions like clicks, form submissions, and scrolling. This allows you to scrape websites that require user input, such as login forms.
- Debugging: Selenium can be run in debug mode, which allows you to step through the scraping process and see what the scraper is doing at each step. This is useful for troubleshooting when things go wrong.
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How do: 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 the financial data from.
Step 2: Inspect the website
Now you need 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
Python has several web scraping libraries. Largely, we will use the following libraries:
- requests:Largely, 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 timeStep 4: Write the Python code
Now, it’s time to write the 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 a sample 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.
Is web scraping legal?
While web scraping itself isn’t illegal, especially for publicly available data on a website, it’s important to tread carefully to avoid legal and ethical issues.
The key is respecting the website’s rules. Their terms of service (TOS) and robots.txt file might restrict scraping altogether or outline acceptable practices, like how often you can request data to avoid overwhelming their servers. Additionally, certain types of data are off-limits, such as copyrighted content or personal information without someone’s consent. Data scraping regulations like GDPR (Europe) and CCPA (California) add another layer of complexity.
Finally, web scraping for malicious purposes like stealing login credentials or disrupting a website is a clear no-go. By following these guidelines, you can ensure your web scraping activities are both legal and ethical.
Conclusion
Python is an excellent option for scraping website data from financial websites 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.
Eliminate bottlenecks caused by manually scraping data from websites. Find out how Nanonets can help you scrape data from websites automatically.
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