[mssql/Oracle/pythondata] - Joining Person and Address Data with SQL || Simple Solution
CodeVisualizing a SQL queryVisualizing the execution of a SQL query can help understand the logical order and the steps involved. Here's a breakdown of the proc
# Code
```mssql []
/* Write your T-SQL query statement below */
/* 
    SQL Query: Retrieve all persons and their address information (if available). 
    The query uses a LEFT JOIN to ensure all persons are included, even if they have no address data.
*/

/* 
    INNER JOIN: Combines rows from two tables where there is a match in the specified columns. 
                Use it when you need data that exists in both tables.
    OUTER JOIN: Includes rows even if there is no match. Missing values are filled with NULL.
    LEFT OUTER JOIN (LEFT JOIN): Ensures all rows from the left table are included. 
                                 Rows from the right table are included where a match is found, otherwise NULL is returned for missing matches.
    RIGHT OUTER JOIN (RIGHT JOIN): Includes all rows from the right table, with matches from the left table.
*/

SELECT 
    P.firstName AS FirstName,  -- Retrieves the person's first name.
    P.lastName AS LastName,    -- Retrieves the person's last name.
    A.city AS City,            -- Retrieves the city from the address table. NULL if no match.
    A.state AS State           -- Retrieves the state from the address table. NULL if no match.
FROM 
    Person P                   -- Primary table containing personal information.
LEFT JOIN 
    Address A                  -- Secondary table containing address information.
ON 
    P.personid = A.personid;   -- Join condition: Matches persons to their address based on person ID.

/* 
    Explanation of Logic:
    - The query uses a LEFT JOIN because we want to include all rows from the `Person` table, 
      even if no matching rows exist in the `Address` table.
    - If a person does not have an address in the `Address` table, the `City` and `State` columns
      will return NULL for that person.
    - This is useful when the `Person` table is the main focus of the query, 
      and address data is optional or secondary.
*/
```
```Oracle []
SELECT 
    P.firstName AS FirstName,
    P.lastName AS LastName,
    A.city AS City,
    A.state AS State
FROM 
    Person P
LEFT JOIN 
    Address A
ON 
    P.personid = A.personid;

-- This query retrieves the first name and last name of persons along with their city and state from the Address table.
-- The LEFT JOIN ensures that all records from the Person table are included, even if there is no matching address record.
-- If a person does not have a corresponding address, the city and state will be NULL.
-- The query joins the Person table (aliased as P) and the Address table (aliased as A) on the personid field.
-- 'firstName', 'lastName', 'city', and 'state' are selected as output columns with appropriate aliases.
```
```pythondata []
import pandas as pd

def combine_two_tables(person: pd.DataFrame, address: pd.DataFrame) -> pd.DataFrame:

    # Perform a left join on personId
    # This merges the 'person' and 'address' DataFrames using a left join on the 'personId' column.
    # Left Join: Includes all rows from the 'person' DataFrame and the matching rows from the 'address' DataFrame.
    # If there is no match, the result will contain NaN for columns from the 'address' DataFrame.
    merged = pd.merge(person, address, how='left', on='personId')

    # Select and reorder the required columns
    # This selects the columns 'firstName', 'lastName', 'city', and 'state' from the merged DataFrame.
    # The 'copy()' method is used to create a copy of the selected columns to avoid modifying the original DataFrame.
    res = merged[['firstName', 'lastName', 'city', 'state']].copy()

    # Return the resulting DataFrame
    return res 
```
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# Visualizing a SQL query
Visualizing the execution of a SQL query can help understand the logical order and the steps involved. Here's a breakdown of the process:

- Parsing the SQL Statement:
The database system parses the SQL statement to check its syntax and validity.
It ensures that the SQL statement is correctly formed and that all referenced objects (tables, columns, etc.) exist.
- Transforming into Internal Representation:
The parsed SQL statement is transformed into an internal representation, such as relational algebra.
This step involves converting the SQL query into a format that the database engine can work with more efficiently.
- Optimizing the Internal Representation:
The database system optimizes the internal representation to create an execution plan.
This involves analyzing various ways to execute the query and selecting the most efficient one, often utilizing index information and other optimization techniques.
- Creating an Execution Plan:
The execution plan is a detailed set of instructions on how to retrieve the required data.
It includes steps like accessing indexes, joining tables, filtering rows, and sorting results.
- Executing the Plan:
The database system executes the plan, performing the necessary operations to retrieve the data.
This step involves reading data from disk, applying filters, joining tables, and performing any necessary calculations.
- Returning the Results:
The final step is returning the results to the user or application that issued the query.
The results are formatted according to the query's specifications and sent back to the client.

Here's a simplified visualization of the logical order of a SQL query execution:

SQL Query
   |
Parsing
   |
Internal Representation (Relational Algebra)
   |
Optimization
   |
Execution Plan
   |
Execution
   |
Results

![1736052736702.gif](https://assets.leetcode.com/users/images/c5cc8efb-53b1-492d-9b89-ec4bbadbb487_1736060123.4919496.gif)

The logical order of SQL query processing is different from the way you write the query. Here's the logical order in which SQL queries are processed:

- FROM: The first step is to identify the source tables and join them if necessary.
- WHERE: Next, the rows are filtered based on the conditions specified in the WHERE clause.
- GROUP BY: The remaining rows are then grouped based on the columns specified in the GROUP BY clause.
- HAVING: Groups are filtered based on the conditions specified in the HAVING clause.
- SELECT: The columns specified in the SELECT clause are selected from the remaining rows or groups.
- ORDER BY: The result set is sorted based on the columns specified in the ORDER BY clause.
- LIMIT/OFFSET: Finally, the number of rows returned is limited based on the LIMIT and OFFSET clauses.
['Database', 'Oracle', 'Pandas']