PostgreSQL Example – How to Join Multiple Table

Join Multiple Table

Create Table Of Elves

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

Create Table Of Weapons

- Create table called weapons
CREATE TABLE weapons (
    - string variable
    name varchar(255),
    - string variable
    weapon varchar(255),
    - integer variable
    weight int

Create Table Of Armor

- Create table called armor
    - string variable
    name varchar(255),
    - string variable
    body varchar(255),
    - string variable
    helm varchar(255)

Insert Rows Into Elf Table

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

Insert Rows Into Weapon Table

INSERT INTO weapons (name, weapon, weight)
VALUES ('Dallar Woodfoot','Axe', 2),
       ('Cordin Garner', 'Halberd', 3),
       ('Keat Knigh', 'Dagger', 4),
       ('Colbat Nalor', 'Dagger', 5)

Insert Rows Into Armor Table

INSERT INTO armor (name, body, helm)
VALUES ('Dallar Woodfoot', 'Leather', 'Leather'),
       ('Cordin Garner', 'Leather', NULL),
       ('Keat Knigh', 'Plate', 'Plate'),
       ('Colbat Nalor', 'Plate', 'Plate')

Join All Tables

- All rows from table
SELECT, age, weapon, weight, body, helm FROM elves
- Join with weapon table using name as key
LEFT JOIN weapons ON ( =
- Join with armor table using name as key
LEFT JOIN armor ON ( =
name age weapon weight body helm
Dallar Woodfoot 25 Axe 2 Leather Leather
Cordin Garner 29 Halberd 3 Leather NULL
Keat Knigh 24 Dagger 4 Plate Plate
Colbat Nalor 124 Dagger 5 Plate Plate

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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