1. SELECT * FROM customers WHERE country = 'Germany';
This query retrieves all customers from Germany.

2. SELECT * FROM orders WHERE order_date >= '2025-01-01';
This query filters orders placed since 1st January 2025.

3. SELECT product_id, COUNT(*) FROM orders GROUP BY product_id;
This query counts how many orders each product has.

4. SELECT department, AVG(salary) FROM employees GROUP BY department;
This query calculates the average salary by department.

5. SELECT * FROM products ORDER BY price DESC;
This query sorts products by price in descending order.

6. SELECT c.name, o.order_id
FROM customers c
JOIN orders o ON c.id = o.customer_id;
This query joins customers with their orders.

7. SELECT customer_id, SUM(amount)
FROM payments
GROUP BY customer_id;
This query calculates total payments per customer.

8. SELECT * FROM users WHERE last_login < '2025-01-01';
This query identifies users who have not logged in recently.

9. SELECT product_id, SUM(quantity) AS total_sold
FROM sales
GROUP BY product_id
ORDER BY total_sold DESC
LIMIT 10;
This query returns the top 10 best-selling products.

10. SELECT region, SUM(revenue)
FROM sales
GROUP BY region;
This query compares total revenue across regions.

1. SELECT * FROM orders WHERE amount > 1000;
This query retrieves all high-value orders above 1000.

2. SELECT COUNT(*) FROM users WHERE status = 'inactive';
This query counts inactive users.

3. SELECT DATE(order_date), SUM(amount)
FROM orders
GROUP BY DATE(order_date);
This query aggregates daily sales.

4. SELECT * FROM employees WHERE hire_date >= '2025-01-01';
This query returns employees hired this year.

5. SELECT customer_id, COUNT(order_id)
FROM orders
GROUP BY customer_id;
This query counts how many orders each customer placed.

6. SELECT category, AVG(rating)
FROM reviews
GROUP BY category;
This query calculates average ratings per category.

7. SELECT * FROM products WHERE stock < 10;
This query identifies products with low inventory.

8. SELECT region, SUM(sales)
FROM sales
GROUP BY region
HAVING SUM(sales) > 10000;
This query filters regions with high total sales.

9. SELECT DISTINCT customer_id FROM orders;
This query finds unique customers who placed orders.

10. SELECT * FROM logs WHERE error = TRUE;
This query retrieves system error logs.

1. df[df["country"] == "Germany"]
This code filters data to get all rows where country is Germany.

2. df.groupby("department")["salary"].mean()
This code calculates average salary by department.

3. df.sort_values("price", ascending=False)
This code sorts products by price in descending order.