Open-source relational database management system PostgreSQL is popular for its extensibility and power. As a relational database, PostgreSQL organises data into tables with predefined relationships for efficient retrieval and management. in this article we will talk about how to Use PostgreSQL in Python
MySQL, MariaDB, and SQLite are relational databases with different features. PostgreSQL installation, connection, and querying using Python are covered in the tutorial. Its seamless integration with Python lets developers use both technologies for data manipulation.
PostgreSQL is used in small projects and enterprise systems due to its reliability, extensibility, and complex query support. Data-driven insights require knowledge of relational databases like PostgreSQL. This tutorial uses Python to teach PostgreSQL data retrieval and manipulation in a relational database context.
What is PostgreSQL?
Free and open source means that anyone can use PostgreSQL, change it, and share it with others without a licence. People, small businesses, and big businesses all like it because of this. PostgreSQL is an object-relational database that combines object-oriented computing ideas like inheritance and polymorphism with relational database features like tables with rows and columns. You can store complicated data structures and set data types this way.
PostgreSQL uses most of the SQL standard, which makes it easy for developers who know a lot about SQL to use. PostgreSQL is very easy to add on to. You don’t have to recompile your database to change data types, functions, or even write code in a different language! PostgreSQL has a big community of developers and users who are always working on it. In other words, you can get help if you need it.
How to Use PostgreSQL in Python
- Open your terminal or command prompt and run the following command:
pip install psycopg2
- Import the psycopg2 library:
import psycopg2
- Connect to the PostgreSQL database:
conn = psycopg2.connect( database=”your_database_name”, user=”your_username”, password=”your_password”, host=”your_host”, # Usually ‘localhost’ for a local database port=”5432″ # Usually the default port )
- Replace the placeholders with your actual database credentials.
- Create a cursor object:
cursor = conn.cursor()
- The cursor object is used to execute SQL statements and fetch results.
- Execute SQL commands:
# Create a table cursor.execute(“”” CREATE TABLE IF NOT EXISTS employees ( id serial PRIMARY KEY, name VARCHAR(50), salary INTEGER ) “””) # Insert data cursor.execute(“INSERT INTO employees (name, salary) VALUES (%s, %s)”, (“John Doe”, 50000)) # Select data cursor.execute(“SELECT * FROM employees”) rows = cursor.fetchall() for row in rows: print(row)
- Use the
cursor.execute()method to execute SQL commands:
conn.commit()
- Commit changes (for data modification):
cursor.close() conn.close()
- Close the cursor and connection:
Security Considerations in Python and PostgreSQL Integration
Authentication and Authorization
- PostgreSQL users and Python tools that connect to the database must use strong passwords.
- If you want fine-grained role-based access control, PostgreSQL can do it. In order to have more control, give access to roles instead of users.
Configuring Secure Connection
- SSL/TLS encryption protects Python-PostgreSQL data in transit. Set up SSL certificates.
- Python scripts should not store passwords in plaintext. Use environment variables or secure credential storage.
Verifying and Sanitising Input
- Validate all user input: To stop SQL injection attacks, make sure that all user-provided data is correct and clean before using it in SQL searches. You can use parameterized searches and prepared statements with the psycopg2 library.
- Escape special characters in user inputs to stop queries from acting in ways that were not meant.
Tips for Performance Tuning in PostgreSQL and Python
Improvements to PostgreSQL
- Index strategically: Make indexes on columns that are used a lot for sorting, filtering, and joining. For different types of data and query patterns, use the right index types (B-tree, GIN, GiST).
- Analyse Data Distribution: To plan your queries, use ANALYSE to gather statistics. You could use autovacuum or manual VACUUM to get back space and keep statistics up to date.
- Tune Configuration Parameters: Change shared_buffers, work_mem, maintenance_work_mem, and other parameters based on hardware and workload.
Improvements to Python
- Pick Libraries That Work Well: For working with databases, use psycopg2, and for changing data, use NumPy or Pandas.
- Fewer database round trips: only get the data you need, use cursors wisely, and do inserts and updates in groups.
- Monitor Performance: Store frequently used data in Python-side caches (like Redis or Memcached) to cut down on database queries.
- Use ORMs Wisely: ORMs like Django ORM or SQLAlchemy can make working with databases easier, but keep in mind that they may slow things down.
FAQs
Python requires a connection to PostgreSQL. A new database session and connection instance are created by the psycopg2 connect() function. We will connect to a local database called “datacamp_courses” for this tutorial.
Call the psycopg2 module’s connect() function to establish a PostgreSQL database connection. Connect() creates a connection class instance. Call the connection object’s cursor() method to create a new cursor.
You can create a new table by specifying its name, column names, and types: CREATE TABLE weather (city varchar(80), temp_lo int, low temperature temp_hi int, high temperature prcp real, precipitation date date); Enter this into psql with line breaks.
