Delete Duplicates
Create Table Of Elves
- Create table called elves
CREATE TABLE elves (
- string variable
name varchar(255),
- integer variable
age int,
- string variable
race varchar(255),
- string variable
alive varchar(255)
)
Insert Rows Into Elf Table
INSERT INTO elves (name, age, race, alive)
VALUES ('Dallar Woodfoot', 25, 'Elf', 'Yes'),
('Cordin Garner', 29, 'Elf', 'Yes'),
('Keat Knigh', 24, 'Elf', 'Yes'),
('Keat Knigh', 24, 'Elf', 'Yes'),
('Keat Knigh', 24, 'Elf', 'Yes'),
('Keat Knigh', 24, 'Elf', 'Yes'),
('Colbat Nalor', 124, 'Elf', 'Yes')
View Elves Table
- Retrieve all rows from the view Elf
SELECT * FROM elves
name | age | race | alive |
---|---|---|---|
Dallar Woodfoot | 25 | Elf | Yes |
Cordin Garner | 29 | Elf | Yes |
Keat Knigh | 24 | Elf | Yes |
Keat Knigh | 24 | Elf | Yes |
Keat Knigh | 24 | Elf | Yes |
Keat Knigh | 24 | Elf | Yes |
Colbat Nalor | 124 | Elf | Yes |
Drop Duplicates
Note: Normally we would use a unique identify column (e.g. person ID, product ID, etc.). However, since we don’t have a unique ID column we can use PostgreSQL’s internal system column, ctid
. Full documentation on ctid
and other system columns in avaliable here.
- Delete from the elves, calling it copy1
DELETE FROM elves copy1
- Using a second copy of elves, called copy2
USING elves copy2
- Where the internal PostgreSQL system column, ctid is smaller
WHERE copy1.ctid < copy2.ctid
- And all other columns are the same
AND copy1.name = copy2.name
AND copy1.age = copy2.age
AND copy1.race = copy2.race
AND copy1.alive = copy2.alive
name | age | race | alive |
---|---|---|---|
Dallar Woodfoot | 25 | Elf | Yes |
Cordin Garner | 29 | Elf | Yes |
Keat Knigh | 24 | Elf | Yes |
Colbat Nalor | 124 | Elf | Yes |
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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