unbounded preceding: from begining up to the current row

n, preceding: n rows before current row
n, following: means using current row and after n rows
unbounded following: start from current row and go in the end of the partition

Splitting one word to one

Code itself

when working with subqueries correlated you will self join is used in aggregated part

Certainly! Let's break down the SQL query step by step:

1. CTE `t1`:
- The first Common Table Expression (CTE) named t1 calculates the total sales (total_amt_usd) for each region by joining the sales_reps, accounts, orders, and region tables.
- It uses the GROUP BY clause to group the results by the region name (r.name).

   WITH t1 AS (
      SELECT r.name region_name, SUM(o.total_amt_usd) total_amt
      FROM sales_reps s
      JOIN accounts a ON a.sales_rep_id = s.id
      JOIN orders o ON o.account_id = a.id
      JOIN region r ON r.id = s.region_id
      GROUP BY r.name
   )
   

2. CTE `t2`:
- The second CTE named t2 calculates the maximum total sales from t1.

   t2 AS (
      SELECT MAX(total_amt) FROM t1
   )
   

3. Main Query:
- The main query selects the region name (r.name) and the count of total orders (COUNT(o.total) total_orders) for each region.
- It joins the same set of tables (sales_reps, accounts, orders, region) and groups the results by the region name.
- The HAVING clause is used to filter the results based on the condition that the total sales (SUM(o.total_amt_usd)) for each region should be equal to the maximum total sales obtained from t2.

   SELECT r.name, COUNT(o.total) total_orders
   FROM sales_reps s
   JOIN accounts a ON a.sales_rep_id = s.id
   JOIN orders o ON o.account_id = a.id
   JOIN region r ON r.id = s.region_id
   GROUP BY r.name
   HAVING SUM(o.total_amt_usd) = (SELECT * FROM t2);
   

In summary, the query identifies regions where the total sales match the maximum total sales across all regions. It provides the region names and the count of total orders for those specific regions. The use of CTEs helps break down the complex query into more manageable parts for better readability and understanding.




CTE explanation

Rounding it means in pandas formating like f .2

.

Rand()

LIKE

Sure! Let's simplify it:

1. Supervised Learning: It's like teaching your dog tricks by showing it what to do and giving it a treat when it gets it right. For example, if you show your dog pictures of cats and dogs and tell it which is which, it will learn to recognize them.

2. Unsupervised Learning: Imagine you have a big box of toys, and you don't know which ones are similar. Unsupervised learning is like sorting those toys into groups based on how they look or what they do without anyone telling you how to group them.

3. Semi-Supervised Learning: This is like when your teacher gives you some homework with answers and some without. You learn from the homework with answers, and then you try to figure out the rest on your own.

4. Reinforcement Learning: Picture yourself playing a video game. You try different moves, and when you do something good, you get points or rewards. Reinforcement learning is like teaching a computer to play a game by giving it points for doing the right things.

5. Deep Learning: Imagine you have lots of LEGO blocks, and you want to build a really cool spaceship. Deep learning is like having instructions to build the spaceship layer by layer. Each layer adds more details until you have an awesome spaceship.

So, in simple terms:
- Supervised learning is like teaching with examples and rewards.
- Unsupervised learning is like sorting things out without being told how.
- Semi-supervised learning is like doing homework with some answers and some without.
- Reinforcement learning is like learning by getting points for doing good things.
- Deep learning is like building something step by step with lots of tiny pieces.

ML

aggfunc