Running Python on Windows: A Beginner's Guide

This tutorial shows how to run Python on Windows, focusing on web data extraction. It covers workspace setup, Python installation, configuring Visual Studio Code, and preparing web scraping code to extract product data from an e-commerce site.

Prepare your development workspace

A development workspace is the foundation of a coding environment. Let’s organize development files.

Project-based directory structure:

C:\Users\YourName\Projects\
    │── python_projects\
    │   │── project1\
    │   │── project2\
    └── venv\

To create this structure, open Command Prompt (cmd.exe) and execute:

mkdir C:\Users\%USERNAME%\Projects\python_projects
cd C:\Users\%USERNAME%\Projects\python_projects

Install Python

1. Go to the official website and download the latest stable release.

2. During installation, ensure to check Add Python to PATH. To verify installation, open Command Prompt and type:

python --version

# Expected output with a version number:
Python 3.X.X  

Verify package management functionality via Command Prompt:

pip --version

# Expected output:
pip 25.X.X from C:\Users\...\pip (python 3.X)

Install Visual Studio Code

In simple terms, VS Code is a text editor for source code. It’s considered to be among the best integrated development environments (IDEs). IDEs are helpful for debugging, syntax error highlighting, running code, and many other ease-of-use functions.

1. Download VS Code.

2. Install Python extension:

• Open VS Code

• Press Ctrl+Shift+X

• Search for Python

• Install Microsoft's Python extension

3. Configure Python interpreter:

• Press Ctrl+Shift+P

• Select Python: Select Interpreter

• Choose your Python installation

4. Configure VS Code for Python development:

// settings.json
{
    "python.defaultInterpreterPath": "C:\\Users\\YourName\\Projects\\python_projects\\venv\\Scripts\\python.exe",
    "python.formatting.provider": "black",
    "editor.formatOnSave": true
}

Web scraping code example

Before running Python scripts, install the required packages. Requests for HTTP operations and Beautiful Soup for HTML parsing.

pip install requests beautifulsoup4

The following Python code uses freshly installed requests, Beautiful Soup libraries, and a built-in csv module.

Here's an example of web scraping from a mock e-commerce marketplace, extracting product titles and prices.

# Import required libraries
import requests                # For making web requests
from bs4 import BeautifulSoup  # For parsing HTML
import csv                     # For saving data

def scrape_products():
    """
    Scrapes product information from a web page and saves to CSV.
    """
    # Step 1: Get the web page
    url = 'https://sandbox.oxylabs.io/products/category/pc'
    webpage = requests.get(url)
    
    # Step 2: Parse HTML content
    soup = BeautifulSoup(webpage.text, 'html.parser')
    
    # Step 3: Find all products
    products = soup.find_all('div', class_='product-card')
    
    # Step 4: Save data to CSV file
    with open('products.csv', 'w', newline='') as file:
        writer = csv.writer(file)
        
        # Write header row
        writer.writerow(['Product Name', 'Price'])
        
        # Write product data
        for product in products:
            # Get product details
            name = product.find('h4').text.strip()
            price = product.find(class_='price-wrapper').text.strip()
            
            # Save to CSV
            writer.writerow([name, price])
    
    print("Check products.csv for the results.")

# Run the scraper
scrape_products()

This Python code exercises several basics:

• HTTP GET request

• HTML parsing

• CSV data writing

The code creates a CSV file containing product information in your working directory. Each row contains a product title and its corresponding price.

For optimal performance in real-world scenarios, the Python code could, at the very least, include:

• User-Agent headers to identify as an actual browser

• Error handling for network and parsing issues

• Retry logic or timeout configurations

• Proxy support to avoid blocks

NOTE: It’s not the best idea to set up proxies on Windows through system settings, as the web scraper acts as a browser. The Python requests code should integrate proxies directly. 

Prebuilt scrapers

Prebuilt web scraper APIs automate and handle processes in web data collection architectures. Coding is reduced to a minimum as you can formulate a desirable task request without worrying about scripting complex processes.

Up-to-date scraper APIs handle complex JavaScript execution and dynamic content loading, rendering modern web applications with high accuracy.

The key pros of scraper APIs are saving time and reducing engineering resources.

Infrastructure management

• Elimination of server maintenance overhead

• Built-in handling of IP rotation and proxy management

• Automatic scaling based on throughput requirements

Reliability engineering

• Uptime management (typically 99.9%+)

• Built-in retry mechanisms for failed requests

• Automatic handling of CAPTCHAs and anti-bot measures

Data quality assurance

• Structured output formats (JSON, CSV)

• Consistent parsing of dynamic JavaScript content

• Built-in error handling and reporting mechanisms