Create simplescrapper.py

Author: Narra_Venkata_Raghu_Charan

scripts/simplescrapper.py

@@ -0,0 +1,91 @@
+
+import requests
+from bs4 import BeautifulSoup
+import pandas as pd
+
+def scrape_categories(url):
+    try:
+        # Send a GET request to the URL
+        response = requests.get(url)
+        response.raise_for_status()  # Check for request errors
+
+        # Parse the HTML content using BeautifulSoup
+        soup = BeautifulSoup(response.content, 'html.parser')
+
+        # Find all category elements (adjust based on the website structure)
+        categories = []
+        for category in soup.find_all('a', class_='category-link'):  # Adjust class or tag based on the website structure
+            category_name = category.text.strip()
+            category_url = category['href']
+            categories.append({'Category': category_name, 'URL': category_url})
+
+        return categories
+    except Exception as e:
+        print(f"An error occurred while scraping categories: {e}")
+        return []
+
+def scrape_products(category_url):
+    try:
+        # Send a GET request to the category URL
+        response = requests.get(category_url)
+        response.raise_for_status()  # Check for request errors
+
+        # Parse the HTML content using BeautifulSoup
+        soup = BeautifulSoup(response.content, 'html.parser')
+
+        # Find all product elements (adjust based on the website structure)
+        products = []
+        for product in soup.find_all('div', class_='product-item'):  # Adjust class or tag based on the website structure
+            product_name = product.find('h2', class_='product-name').text.strip()  # Adjust based on the website structure
+            product_price = product.find('span', class_='product-price').text.strip()  # Adjust based on the website structure
+            product_url = product.find('a', class_='product-link')['href']  # Adjust based on the website structure
+            products.append({'Product Name': product_name, 'Price': product_price, 'URL': product_url})
+
+        return products
+    except Exception as e:
+        print(f"An error occurred while scraping products: {e}")
+        return []
+
+def save_to_excel(categories, output_file):
+    try:
+        # Create a DataFrame from the categories list
+        df = pd.DataFrame(categories)
+
+        # Save the DataFrame to an Excel file
+        df.to_excel(output_file, index=False)
+        print(f"Categories and products saved to {output_file}")
+    except Exception as e:
+        print(f"An error occurred while saving to Excel: {e}")
+
+if __name__ == "__main__":
+    # Input: E-commerce website URL
+    url = input("Enter the e-commerce website URL: ").strip()
+
+    # Output: Excel file name
+    output_file = 'categories_and_products.xlsx'
+
+    # Scrape categories
+    categories = scrape_categories(url)
+
+    # Scrape products for each category
+    for category in categories:
+        category_url = category['URL']
+        products = scrape_products(category_url)
+        for product in products:
+            product['Category'] = category['Category']  # Add category name to each product
+            category['Products'] = products
+
+    # Flatten the data for saving to Excel
+    all_data = []
+    for category in categories:
+        for product in category.get('Products', []):
+            all_data.append({
+                'Category': category['Category'],
+                'Product Name': product['Product Name'],
+                'Price': product['Price'],
+                'Product URL': product['URL'],
+                'Category URL': category['URL']
+            })
+
+    # Save to Excel
+    save_to_excel(all_data, output_file)