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 extensive 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 large amounts of data online, web scraping has become an essential tool for businesses and individuals.
Deciding between the many online scraping solutions available usually comes down to how skilled you are at programming and how difficult the job is. Many popular Python libraries, such as Beautiful Soup, Scrapy, and Selenium, have different 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 generally unstructured and needs to be converted into a structured format to be used for analysis, research, or even training ai models.
If you've ever copied and pasted data from any website into an Excel spreadsheet or Word document, it's basically web scraping on a very small scale. The copy and paste method is useful when web scraping is necessary for personal projects or unique use cases. However, when companies need to extract data from websites, they typically need to extract data from multiple websites and pages, and they also need to do so repeatedly. Doing this manually would be time-consuming and error-prone. Therefore, organizations turn to web scraping tools that automatically extract data from websites based on business requirements. These tools can also transform the data to make it usable, since most of the extracted data is unstructured, and load 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 remain the same for this entire web scraping process:
- Identify destination URLs: Users need to manually select the URLs of the websites they want to extract data from and keep them ready for input into the web scraping tool.
- Extract data from websites: Once you enter the website URL in the web scraper tool, the web scraper will retrieve and extract all the data from the website.
- Analyze extracted data: Data extracted from websites is generally unstructured and needs to be analyzed to be useful for analysis. This can be done manually or it can be automated with the help of advanced web scraping tools.
- Load/Save the final structured data: Once the data is analyzed and structured into a usable format, it can be saved to the desired location. This data can be loaded 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 others web scraping techniques:
- Dynamic websites: Dynamic web pages are created using JavaScript or other programming languages. These pages typically contain elements that are visible 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 extracting data from dynamic web pages.
- User interactions: Selenium can simulate user interactions such as clicks, form submissions, and scrolling. This allows you to remove websites that require user input, such as login forms.
- Depuration: Selenium can be run in debug mode, allowing you to step through the scraping process and see what the scraper does at each step. This is useful for troubleshooting when something goes wrong.
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How to do: Extract data from websites using Python?
Let's take a look at the step-by-step process of using Python to extract website data.
Step 1 – Choose the website and web page URL
The first step is to select the website from which you want to extract financial data.
Step 2: Inspect the website
Now you need to understand the structure of the website. Understand which attributes of the elements are of interest to you. 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 that will be used in the code.
Note the class names and identifiers of these elements, as they will be used in Python code.
Step 3: Install important libraries
Python has several web scraping libraries. To a large extent, we will use the following libraries:
- requests:Largely, to make HTTP requests to the website.
- beautifulsoup: to parse the HTML code
- pandas:– to store the extracted data in a data frame
- time– to add a delay between requests to avoid flooding 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:
- Use requests to send an HTTP GET request
- Using BeautifulSoup to parse HTML code
- Extract required data from HTML code
- Store information in a pandas data frame
- Add a delay between requests to avoid flooding the website with requests
Here is a sample Python code to extract 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: Export 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 was extracted and stored correctly.
Is web scraping legal?
While web scraping itself is not illegal, especially for publicly available data on a website, it is important to proceed carefully to avoid legal and ethical issues.
The key is to respect the website's rules. Your terms of service (TOS) and robots.txt file may restrict scraping entirely or outline acceptable practices, such as how often data can be requested to avoid overwhelming your servers. Additionally, certain types of data are prohibited, such as copyrighted content or personal information without someone's consent. Data mining regulations like GDPR (Europe) and CCPA (California) add another layer of complexity.
Finally, web scraping for malicious purposes, such as stealing login credentials or defacing a website, is clearly prohibited. By following these guidelines, you can ensure that your web scraping activities are legal and ethical.
Conclusion
Python is a great option for extracting data from financial websites in real time. Another alternative is to use automated systems. website scraping tools llike nanonetworks. You can use the free website to text conversion tool. But, if you need to automate web scraping for larger projects, you can contact Nanonets.
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