PostgreSQL tutorial for Beginners – PostgreSQL – LIKE Clause

PostgreSQL – LIKE Clause

 

The PostgreSQL LIKE operator is used to match text values against a pattern using wildcards. If the search expression can be matched to the pattern expression, the LIKE operator will return true, which is 1.

There are two wildcards used in conjunction with the LIKE operator −

  • The percent sign (%)
  • The underscore (_)

 

The percent sign represents zero, one, or multiple numbers or characters. The underscore represents a single number or character. These symbols can be used in combinations.

If either of these two signs is not used in conjunction with the LIKE clause, then the LIKE acts like the equals operator.

Syntax

The basic syntax of % and _ is as follows −

SELECT FROM table_name
WHERE column LIKE 'XXXX%'

or

SELECT FROM table_name
WHERE column LIKE '%XXXX%'

or

SELECT FROM table_name
WHERE column LIKE 'XXXX_'

or

SELECT FROM table_name
WHERE column LIKE '_XXXX'

or

SELECT FROM table_name
WHERE column LIKE '_XXXX_'

You can combine N number of conditions using AND or OR operators. Here XXXX could be any numeric or string value.

Example

Here are number of examples showing WHERE part having different LIKE clause with ‘%’ and ‘_’ operators −

S. No. Statement & Description
1 WHERE SALARY::text LIKE ‘200%’

Finds any values that start with 200

2 WHERE SALARY::text LIKE ‘%200%’

Finds any values that have 200 in any position

3 WHERE SALARY::text LIKE ‘_00%’

Finds any values that have 00 in the second and third positions

4 WHERE SALARY::text LIKE ‘2_%_%’

Finds any values that start with 2 and are at least 3 characters in length

5 WHERE SALARY::text LIKE ‘%2’

Finds any values that end with 2

6 WHERE SALARY::text LIKE ‘_2%3’

Finds any values that have 2 in the second position and end with a 3

7 WHERE SALARY::text LIKE ‘2___3’

Finds any values in a five-digit number that start with 2 and end with 3

Postgres LIKE is String compare only. Hence, we need to explicitly cast the integer column to string as in the examples above.

Let us take a real example, consider the table COMPANY, having records as follows −

# select * from COMPANY;
 id | name  | age | address   | salary
----+-------+-----+-----------+--------
  1 | Paul  |  32 | California|  20000
  2 | Allen |  25 | Texas     |  15000
  3 | Teddy |  23 | Norway    |  20000
  4 | Mark  |  25 | Rich-Mond |  65000
  5 | David |  27 | Texas     |  85000
  6 | Kim   |  22 | South-Hall|  45000
  7 | James |  24 | Houston   |  10000
(7 rows)

The following is an example, which would display all the records from COMPANY table where AGE starts with 2 −

testdb=# SELECT * FROM COMPANY WHERE AGE::text LIKE '2%';

This would produce the following result −

 id | name  | age | address     | salary
----+-------+-----+-------------+--------
  2 | Allen |  25 | Texas       |  15000
  3 | Teddy |  23 | Norway      |  20000
  4 | Mark  |  25 | Rich-Mond   |  65000
  5 | David |  27 | Texas       |  85000
  6 | Kim   |  22 | South-Hall  |  45000
  7 | James |  24 | Houston     |  10000
  8 | Paul  |  24 | Houston     |  20000
(7 rows)

The following is an example, which would display all the records from COMPANY table where ADDRESS will have a hyphen (-) inside the text −

testdb=# SELECT * FROM COMPANY WHERE ADDRESS  LIKE '%-%';

This would produce the following result −

 id | name | age |                      address              | salary
----+------+-----+-------------------------------------------+--------
  4 | Mark |  25 | Rich-Mond                                 |  65000
  6 | Kim  |  22 | South-Hall                                |  45000
(2 rows)

 

 

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.

Data Science Resources: Data Science Recipes and Applied Machine Learning Recipes

Introduction to Applied Machine Learning & Data Science for Beginners, Business Analysts, Students, Researchers and Freelancers with Python & R Codes @ Western Australian Center for Applied Machine Learning & Data Science (WACAMLDS) !!!

Latest end-to-end Learn by Coding Recipes in Project-Based Learning:

Applied Statistics with R for Beginners and Business Professionals

Data Science and Machine Learning Projects in Python: Tabular Data Analytics

Data Science and Machine Learning Projects in R: Tabular Data Analytics

Python Machine Learning & Data Science Recipes: Learn by Coding

R Machine Learning & Data Science Recipes: Learn by Coding

Comparing Different Machine Learning Algorithms in Python for Classification (FREE)

Disclaimer: The information and code presented within this recipe/tutorial is only for educational and coaching purposes for beginners and developers. Anyone can practice and apply the recipe/tutorial presented here, but the reader is taking full responsibility for his/her actions. The author (content curator) of this recipe (code / program) has made every effort to ensure the accuracy of the information was correct at time of publication. The author (content curator) does not assume and hereby disclaims any liability to any party for any loss, damage, or disruption caused by errors or omissions, whether such errors or omissions result from accident, negligence, or any other cause. The information presented here could also be found in public knowledge domains.  

Google –> SETScholars