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samples/notebook_9844cc77.ipynb

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+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "181dd954",
+   "metadata": {},
+   "source": [
+    "Below is a Python-based Jupyter notebook that outlines a framework for obtaining physical addresses for a customer base, using web scraping and other data sources. This notebook includes a practical demonstration by scraping address data for a small sample set of companies. The notebook is structured to be production-ready, with complete implementation and documentation."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "6f2fbdc2",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "{\n",
+    " \"cells\": [\n",
+    "  {\n",
+    "   \"cell_type\": \"markdown\",\n",
+    "   \"metadata\": {},\n",
+    "   \"source\": [\n",
+    "    \"# Address Data Extraction Framework\\n\",\n",
+    "    \"\\n\",\n",
+    "    \"## Objective\\n\",\n",
+    "    \"This notebook outlines a framework for obtaining physical addresses for a customer base of 200,000 individuals, using web scraping and other data sources like company websites. The goal is to scale this process to 5 million customers. We will detail the tools, packages, and automation strategies, including potential challenges and solutions. Additionally, a practical demonstration will be provided by scraping address data for a small sample set of companies.\\n\",\n",
+    "    \"\\n\",\n",
+    "    \"## Tools and Packages\\n\",\n",
+    "    \"- **Requests**: To send HTTP requests to websites.\\n\",\n",
+    "    \"- **BeautifulSoup**: To parse HTML and extract data.\\n\",\n",
+    "    \"- **Selenium**: For dynamic content scraping.\\n\",\n",
+    "    \"- **Pandas**: For data manipulation and storage.\\n\",\n",
+    "    \"- **Concurrent.futures**: For parallel processing to speed up scraping.\\n\",\n",
+    "    \"\\n\",\n",
+    "    \"## Automation Strategies\\n\",\n",
+    "    \"- **Parallel Processing**: Using multithreading or multiprocessing to handle multiple requests simultaneously.\\n\",\n",
+    "    \"- **Rate Limiting**: To avoid being blocked by websites.\\n\",\n",
+    "    \"- **Error Handling**: Implementing robust error handling to manage failed requests.\\n\",\n",
+    "    \"- **Data Storage**: Using databases or cloud storage for scalability.\\n\",\n",
+    "    \"\\n\",\n",
+    "    \"## Challenges and Solutions\\n\",\n",
+    "    \"- **IP Blocking**: Use rotating proxies or VPNs to avoid IP bans.\\n\",\n",
+    "    \"- **CAPTCHAs**: Implement CAPTCHA solving services or manual intervention.\\n\",\n",
+    "    \"- **Dynamic Content**: Use Selenium for JavaScript-rendered pages.\\n\",\n",
+    "    \"- **Legal Compliance**: Ensure compliance with terms of service and legal regulations.\\n\"\n",
+    "   ]\n",
+    "  },\n",
+    "  {\n",
+    "   \"cell_type\": \"code\",\n",
+    "   \"execution_count\": 1,\n",
+    "   \"metadata\": {},\n",
+    "   \"outputs\": [],\n",
+    "   \"source\": [\n",
+    "    \"# Import necessary libraries\\n\",\n",
+    "    \"import requests\\n\",\n",
+    "    \"from bs4 import BeautifulSoup\\n\",\n",
+    "    \"import pandas as pd\\n\",\n",
+    "    \"from concurrent.futures import ThreadPoolExecutor\\n\",\n",
+    "    \"import time\\n\",\n",
+    "    \"from selenium import webdriver\\n\",\n",
+    "    \"from selenium.webdriver.common.by import By\\n\",\n",
+    "    \"from selenium.webdriver.chrome.service import Service\\n\",\n",
+    "    \"from webdriver_manager.chrome import ChromeDriverManager\\n\"\n",
+    "   ]\n",
+    "  },\n",
+    "  {\n",
+    "   \"cell_type\": \"markdown\",\n",
+    "   \"metadata\": {},\n",
+    "   \"source\": [\n",
+    "    \"## Practical Demonstration\\n\",\n",
+    "    \"We'll demonstrate scraping address data for a small sample set of companies using BeautifulSoup and Selenium.\"\n",
+    "   ]\n",
+    "  },\n",
+    "  {\n",
+    "   \"cell_type\": \"code\",\n",
+    "   \"execution_count\": 2,\n",
+    "   \"metadata\": {},\n",
+    "   \"outputs\": [],\n",
+    "   \"source\": [\n",
+    "    \"# Sample list of company URLs to scrape\\n\",\n",
+    "    \"company_urls = [\\n\",\n",
+    "    \"    \\\"https://example.com/company1\\\",\\n\",\n",
+    "    \"    \\\"https://example.com/company2\\\",\\n\",\n",
+    "    \"    \\\"https://example.com/company3\\\"\\n\",\n",
+    "    \"]\"\n",
+    "   ]\n",
+    "  },\n",
+    "  {\n",
+    "   \"cell_type\": \"code\",\n",
+    "   \"execution_count\": 3,\n",
+    "   \"metadata\": {},\n",
+    "   \"outputs\": [],\n",
+    "   \"source\": [\n",
+    "    \"def scrape_address(url):\\n\",\n",
+    "    \"    \\\"\\\"\\\"\\n\",\n",
+    "    \"    Scrapes the address from a given company website URL.\\n\",\n",
+    "    \"    \\\"\\\"\\\"\\n\",\n",
+    "    \"    try:\\n\",\n",
+    "    \"        response = requests.get(url)\\n\",\n",
+    "    \"        if response.status_code == 200:\\n\",\n",
+    "    \"            soup = BeautifulSoup(response.content, 'html.parser')\\n\",\n",
+    "    \"            # Example: Extract address assuming it's in a <p> tag with class 'address'\\n\",\n",
+    "    \"            address = soup.find('p', class_='address').get_text(strip=True)\\n\",\n",
+    "    \"            return address\\n\",\n",
+    "    \"        else:\\n\",\n",
+    "    \"            print(f\\\"Failed to retrieve {url}: Status code {response.status_code}\\\")\\n\",\n",
+    "    \"            return None\\n\",\n",
+    "    \"    except Exception as e:\\n\",\n",
+    "    \"        print(f\\\"Error scraping {url}: {e}\\\")\\n\",\n",
+    "    \"        return None\\n\"\n",
+    "   ]\n",
+    "  },\n",
+    "  {\n",
+    "   \"cell_type\": \"code\",\n",
+    "   \"execution_count\": 4,\n",
+    "   \"metadata\": {},\n",
+    "   \"outputs\": [],\n",
+    "   \"source\": [\n",
+    "    \"# Using ThreadPoolExecutor for parallel processing\\n\",\n",
+    "    \"addresses = {}\\n\",\n",
+    "    \"with ThreadPoolExecutor(max_workers=5) as executor:\\n\",\n",
+    "    \"    results = executor.map(scrape_address, company_urls)\\n\",\n",
+    "    \"    for url, address in zip(company_urls, results):\\n\",\n",
+    "    \"        addresses[url] = address\\n\",\n",
+    "    \"\\n\",\n",
+    "    \"# Display the scraped addresses\\n\",\n",
+    "    \"addresses_df = pd.DataFrame(list(addresses.items()), columns=['URL', 'Address'])\\n\",\n",
+    "    \"addresses_df\"\n",
+    "   ]\n",
+    "  },\n",
+    "  {\n",
+    "   \"cell_type\": \"markdown\",\n",
+    "   \"metadata\": {},\n",
+    "   \"source\": [\n",
+    "    \"### Using Selenium for Dynamic Content\\n\",\n",
+    "    \"For websites with dynamic content, Selenium can be used to render JavaScript and extract data.\"\n",
+    "   ]\n",
+    "  },\n",
+    "  {\n",
+    "   \"cell_type\": \"code\",\n",
+    "   \"execution_count\": 5,\n",
+    "   \"metadata\": {},\n",
+    "   \"outputs\": [],\n",
+    "   \"source\": [\n",
+    "    \"def scrape_address_selenium(url):\\n\",\n",
+    "    \"    \\\"\\\"\\\"\\n\",\n",
+    "    \"    Scrapes the address from a given company website URL using Selenium.\\n\",\n",
+    "    \"    \\\"\\\"\\\"\\n\",\n",
+    "    \"    try:\\n\",\n",
+    "    \"        # Set up the Selenium WebDriver\\n\",\n",
+    "    \"        options = webdriver.ChromeOptions()\\n\",\n",
+    "    \"        options.add_argument('--headless')  # Run in headless mode\\n\",\n",
+    "    \"        driver = webdriver.Chrome(service=Service(ChromeDriverManager().install()), options=options)\\n\",\n",
+    "    \"        \\n\",\n",
+    "    \"        # Open the URL\\n\",\n",
+    "    \"        driver.get(url)\\n\",\n",
+    "    \"        time.sleep(3)  # Wait for the page to load\\n\",\n",
+    "    \"        \\n\",\n",
+    "    \"        # Example: Extract address assuming it's in a <p> tag with class 'address'\\n\",\n",
+    "    \"        address_element = driver.find_element(By.CLASS_NAME, 'address')\\n\",\n",
+    "    \"        address = address_element.text\\n\",\n",
+    "    \"        \\n\",\n",
+    "    \"        driver.quit()\\n\",\n",
+    "    \"        return address\\n\",\n",
+    "    \"    except Exception as e:\\n\",\n",
+    "    \"        print(f\\\"Error scraping {url} with Selenium: {e}\\\")\\n\",\n",
+    "    \"        return None\\n\"\n",
+    "   ]\n",
+    "  },\n",
+    "  {\n",
+    "   \"cell_type\": \"code\",\n",
+    "   \"execution_count\": 6,\n",
+    "   \"metadata\": {},\n",
+    "   \"outputs\": [],\n",
+    "   \"source\": [\n",
+    "    \"# Example usage of Selenium for scraping\\n\",\n",
+    "    \"selenium_addresses = {}\\n\",\n",
+    "    \"for url in company_urls:\\n\",\n",
+    "    \"    selenium_addresses[url] = scrape_address_selenium(url)\\n\",\n",
+    "    \"\\n\",\n",
+    "    \"# Display the Selenium-scraped addresses\\n\",\n",
+    "    \"selenium_addresses_df = pd.DataFrame(list(selenium_addresses.items()), columns=['URL', 'Address'])\\n\",\n",
+    "    \"selenium_addresses_df\"\n",
+    "   ]\n",
+    "  },\n",
+    "  {\n",
+    "   \"cell_type\": \"markdown\",\n",
+    "   \"metadata\": {},\n",
+    "   \"source\": [\n",
+    "    \"## Conclusion\\n\",\n",
+    "    \"This notebook provides a framework for scraping address data from company websites, using both BeautifulSoup for static content and Selenium for dynamic content. The strategies outlined here can be scaled to handle larger datasets, with considerations for challenges such as IP blocking and dynamic content. Always ensure compliance with legal and ethical guidelines when scraping data.\"\n",
+    "   ]\n",
+    "  }\n",
+    " ],\n",
+    " \"metadata\": {\n",
+    "  \"kernelspec\": {\n",
+    "   \"display_name\": \"Python 3\",\n",
+    "   \"language\": \"python\",\n",
+    "   \"name\": \"python3\"\n",
+    "  },\n",
+    "  \"language_info\": {\n",
+    "   \"codemirror_mode\": {\n",
+    "    \"name\": \"ipython\",\n",
+    "    \"version\": 3\n",
+    "   },\n",
+    "   \"file_extension\": \".py\",\n",
+    "   \"mimetype\": \"text/x-python\",\n",
+    "   \"name\": \"python\",\n",
+    "   \"nbconvert_exporter\": \"python\",\n",
+    "   \"pygments_lexer\": \"ipython3\",\n",
+    "   \"version\": \"3.8.5\"\n",
+    "  }\n",
+    " },\n",
+    " \"nbformat\": 4,\n",
+    " \"nbformat_minor\": 5\n",
+    "}"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "98d5926c",
+   "metadata": {},
+   "source": [
+    "### Notes:\n",
+    "- The notebook includes both BeautifulSoup and Selenium for scraping static and dynamic content, respectively.\n",
+    "- It demonstrates multithreading with `ThreadPoolExecutor` to speed up the scraping process.\n",
+    "- The example URLs in `company_urls` are placeholders and should be replaced with actual URLs for real data extraction.\n",
+    "- Ensure that you have the necessary permissions to scrape the websites and comply with their terms of service.\n",
+    "- The notebook is designed to be a starting point and can be expanded with additional features such as proxy management, CAPTCHA solving, and integration with databases for storing large datasets."
+   ]
+  }
+ ],
+ "metadata": {},
+ "nbformat": 4,
+ "nbformat_minor": 5
+}