PostgreSQL – SELECT Database
This chapter explains various methods of accessing the database. Assume that we have already created a database in our previous chapter. You can select the database using either of the following methods −
- Database SQL Prompt
- OS Command Prompt
Database SQL Prompt
Assume you have already launched your PostgreSQL client and you have landed at the following SQL prompt −
You can check the available database list using l, i.e., backslash el command as follows −
postgres-# l List of databases Name | Owner | Encoding | Collate | Ctype | Access privileges -----------+----------+----------+---------+-------+----------------------- postgres | postgres | UTF8 | C | C | template0 | postgres | UTF8 | C | C | =c/postgres + | | | | | postgres=CTc/postgres template1 | postgres | UTF8 | C | C | =c/postgres + | | | | | postgres=CTc/postgres testdb | postgres | UTF8 | C | C | (4 rows) postgres-#
Now, type the following command to connect/select a desired database; here, we will connect to the testdb database.
postgres=# c testdb; psql (9.2.4) Type "help" for help. You are now connected to database "testdb" as user "postgres". testdb=#
OS Command Prompt
You can select your database from the command prompt itself at the time when you login to your database. Following is a simple example −
psql -h localhost -p 5432 -U postgress testdb Password for user postgress: **** psql (9.2.4) Type "help" for help. You are now connected to database "testdb" as user "postgres". testdb=#
You are now logged into PostgreSQL testdb and ready to execute your commands inside testdb. To exit from the database, you can use the command q.
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) !!!
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