PostgreSQL Example – How to Cartesian Product Of Tables

Cartesian Product Of Tables

Create Table Of Adventurers

- Create table called adventurers
CREATE TABLE adventurers (
    - string variable
    name varchar(255),
    - integer variable
    age int,
    - string variable
    race varchar(255)

Create Table Of Adventurer’s Equipment

- Create table called equipment
CREATE TABLE equipment (
    - string variable
    name varchar(255),
    - string variable
    clothes varchar(255),
    - string variable
    weapon varchar(255)

Insert Rows Into Adventurers Table

INSERT INTO adventurers (name, age, race)
VALUES ('Dallar Woodfoot', 25, 'Elf'),
       ('Cordin Garner', 29, 'Elf'),
       ('Keat Knigh', 24, 'Dwarf'),
       ('Colbat Nalor', 124, 'Dwarf')

Insert Rows Into Equipment Table

INSERT INTO equipment (name, clothes, weapon)
VALUES ('Dallar Woodfoot', 'Leather Armor', 'Axe'),
       ('Keat Knigh', 'Robe', 'Bow'),
       ('Tasar Keynelis', 'Tunic', 'Axe'),
       ('Sataleeti Iarroris','Chainmail', 'Axe')

Cartestian Product Of Tables

- Return the name of people from the adventurers table, age, race, clothes, and weapon
SELECT, age, race, clothes, weapon FROM adventurers
- Cross join with the equipment table
CROSS JOIN equipment
name age race clothes weapon
Dallar Woodfoot 25 Elf Leather Armor Axe
Dallar Woodfoot 25 Elf Robe Bow
Dallar Woodfoot 25 Elf Tunic Axe
Dallar Woodfoot 25 Elf Chainmail Axe
Cordin Garner 29 Elf Leather Armor Axe
Cordin Garner 29 Elf Robe Bow
Cordin Garner 29 Elf Tunic Axe
Cordin Garner 29 Elf Chainmail Axe
Keat Knigh 24 Dwarf Leather Armor Axe
Keat Knigh 24 Dwarf Robe Bow
Keat Knigh 24 Dwarf Tunic Axe
Keat Knigh 24 Dwarf Chainmail Axe
Colbat Nalor 124 Dwarf Leather Armor Axe
Colbat Nalor 124 Dwarf Robe Bow
Colbat Nalor 124 Dwarf Tunic Axe
Colbat Nalor 124 Dwarf Chainmail Axe


Python Example for Beginners

Two Machine Learning Fields

There are two sides to machine learning:

  • Practical Machine Learning:This is about querying databases, cleaning data, writing scripts to transform data and gluing algorithm and libraries together and writing custom code to squeeze reliable answers from data to satisfy difficult and ill defined questions. It’s the mess of reality.
  • Theoretical Machine Learning: This is about math and abstraction and idealized scenarios and limits and beauty and informing what is possible. It is a whole lot neater and cleaner and removed from the mess of reality.

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