The way to construct end-to-end interactive analytical dashboards utilizing PyGWalker options for insightful knowledge exploration

def generate_advanced_dataset():
   np.random.seed(42)
   start_date = datetime(2022, 1, 1)
   dates = [start_date + timedelta(days=x) for x in range(730)]
   classes = ['Electronics', 'Clothing', 'Home & Garden', 'Sports', 'Books']
   merchandise = {
       'Electronics': ['Laptop', 'Smartphone', 'Headphones', 'Tablet', 'Smartwatch'],
       'Clothes': ['T-Shirt', 'Jeans', 'Dress', 'Jacket', 'Sneakers'],
       'Dwelling & Backyard': ['Furniture', 'Lamp', 'Rug', 'Plant', 'Cookware'],
       'Sports activities': ['Yoga Mat', 'Dumbbell', 'Running Shoes', 'Bicycle', 'Tennis Racket'],
       'Books': ['Fiction', 'Non-Fiction', 'Biography', 'Science', 'History']
   }
   n_transactions = 5000
   knowledge = []
   for _ in vary(n_transactions):
       date = np.random.selection(dates)
       class = np.random.selection(classes)
       product = np.random.selection(productsAI Shorts)
       base_prices = {
           'Electronics': (200, 1500),
           'Clothes': (20, 150),
           'Dwelling & Backyard': (30, 500),
           'Sports activities': (25, 300),
           'Books': (10, 50)
       }
       value = np.random.uniform(*base_pricesAI Shorts)
       amount = np.random.selection([1, 1, 1, 2, 2, 3], p=[0.5, 0.2, 0.15, 0.1, 0.03, 0.02])
       customer_segment = np.random.selection(['Premium', 'Standard', 'Budget'], p=[0.2, 0.5, 0.3])
       age_group = np.random.selection(['18-25', '26-35', '36-45', '46-55', '56+'])
       area = np.random.selection(['North', 'South', 'East', 'West', 'Central'])
       month = date.month
       seasonal_factor = 1.0
       if month in [11, 12]:
           seasonal_factor = 1.5
       elif month in [6, 7]:
           seasonal_factor = 1.2
       income = value * amount * seasonal_factor
       low cost = np.random.selection([0, 5, 10, 15, 20, 25], p=[0.4, 0.2, 0.15, 0.15, 0.07, 0.03])
       marketing_channel = np.random.selection(['Organic', 'Social Media', 'Email', 'Paid Ads'])
       base_satisfaction = 4.0
       if customer_segment == 'Premium':
           base_satisfaction += 0.5
       if low cost > 15:
           base_satisfaction += 0.3
       satisfaction = np.clip(base_satisfaction + np.random.regular(0, 0.5), 1, 5)
       knowledge.append({
           'Date': date, 'Class': class, 'Product': product, 'Value': spherical(value, 2),
           'Amount': amount, 'Income': spherical(income, 2), 'Customer_Segment': customer_segment,
           'Age_Group': age_group, 'Area': area, 'Discount_%': low cost,
           'Marketing_Channel': marketing_channel, 'Customer_Satisfaction': spherical(satisfaction, 2),
           'Month': date.strftime('%B'), '12 months': date.12 months, 'Quarter': f'Q{(date.month-1)//3 + 1}'
       })
   df = pd.DataFrame(knowledge)
   df['Profit_Margin'] = spherical(df['Revenue'] * (1 - df['Discount_%']/100) * 0.3, 2)
   df['Days_Since_Start'] = (df['Date'] - df['Date'].min()).dt.days
   return df