PostgreSQL Python Connector

Read, Write, and Update PostgreSQL with Python

Easily connect Python-based Data Access, Visualization, ORM, ETL, AI/ML, and Custom Apps with PostgreSQL!


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Python Connector Libraries for PostgreSQL Data Connectivity. Integrate PostgreSQL with popular Python tools like Pandas, SQLAlchemy, Dash & petl. Easy-to-use Python Database API (DB-API) Modules connect PostgreSQL data with Python and any Python-based applications.

Features

  • Wire Protocol Driver compatible with protocol version 3.0, PostgreSQL 7.4 and above
  • Connects to on-premises and cloud-based instances, including Heroku Postgres, Amazon Aurora, Amazon Relational Database Service (RDS) and others
  • Connect to live PostgreSQL data, for real-time data access
  • Full support for data aggregation and complex JOINs in SQL queries
  • Seamless integration with leading BI, reporting, and ETL tools and with custom applications

Specifications

  • Python Database API (DB-API) Modules for PostgreSQL with bi-directional access.
  • Write SQL, get PostgreSQL data. Access PostgreSQL through standard Python Database Connectivity.
  • Integration with popular Python tools like Pandas, SQLAlchemy, Dash & petl.
  • Connect with PostgreSQL, Heroku Postgres, Amazon Aurora, Amazon Relational Database Service (RDS), etc.
  • Full Unicode support for data, parameter, & metadata.


CData Python Connectors in Action!

Watch the video overview for a first hand-look at the powerful data integration capabilities included in the CData Python Connectors.

WATCH THE PYTHON CONNECTOR VIDEO OVERVIEW

Python Connectivity with PostgreSQL

Full-featured and consistent SQL access to any supported data source through Python


  • Universal Python PostgreSQL Connectivity

    Easily connect to PostgreSQL data from common Python-based frameworks, including:


    • Data Analysis/Visualization: Jupyter Notebook, pandas, Matplotlib
    • ORM: SQLAlchemy, SQLObject, Storm
    • Web Applications: Dash, Django
    • ETL: Apache Airflow, Luigi, Bonobo, Bubbles, petl
  • Popular Tooling Integration

    The PostgreSQL Connector integrates seamlessly with popular data science and developer tooling like Anaconda, Visual Studio Python IDE, PyCharm, and more. Real Python,

  • Replication and Caching

    Our replication and caching commands make it easy to copy data to local and cloud data stores such as Oracle, SQL Server, Google Cloud SQL, etc. The replication commands include many features that allow for intelligent incremental updates to cached data.

  • String, Date, Numeric SQL Functions

    The PostgreSQL Connector includes a library of 50 plus functions that can manipulate column values into the desired result. Popular examples include Regex, JSON, and XML processing functions.

  • Collaborative Query Processing

    Our Python Connector enhances the capabilities of PostgreSQL with additional client-side processing, when needed, to enable analytic summaries of data such as SUM, AVG, MAX, MIN, etc.

  • Easily Customizable and Configurable

    The data model exposed by our PostgreSQL Connector can easily be customized to add or remove tables/columns, change data types, etc. without requiring a new build. These customizations are supported at runtime using human-readable schema files that are easy to edit.

  • Enterprise-class Secure Connectivity

    Includes standard Enterprise-class security features such as TLS/ SSL data encryption for all client-server communications.

Connecting to PostgreSQL with Python

CData Python Connectors leverage the Database API (DB-API) interface to make it easy to work with PostgreSQL from a wide range of standard Python data tools. Connecting to and working with your data in Python follows a basic pattern, regardless of data source:

  • Configure the connection properties to PostgreSQL
  • Query PostgreSQL to retrieve or update data
  • Connect your PostgreSQL data with Python data tools.


Connecting to PostgreSQL in Python

To connect to your data from Python, import the extension and create a connection:

import cdata.postgresql as mod
conn = mod.connect("User=user@domain.com; Password=password;")

#Create cursor and iterate over results
cur = conn.cursor()
cur.execute("SELECT * FROM PostgreSQLTable")
 
rs = cur.fetchall()
 
for row in rs:
print(row)

Once you import the extension, you can work with all of your enterprise data using the python modules and toolkits that you already know and love, quickly building apps that help you drive business.

Visualize PostgreSQL Data with pandas

The data-centric interfaces of the PostgreSQL Python Connector make it easy to integrate with popular tools like pandas and SQLAlchemy to visualize data in real-time.

engine = create_engine("postgresql///Password=password&User=user")

df = pandas.read_sql("SELECT * FROM PostgreSQLTable", engine)

df.plot()
plt.show()

More Than Read-Only: Full Update/CRUD Support

PostgreSQL Connector goes beyond read-only functionality to deliver full support for Create, Read Update, and Delete operations (CRUD). Your end-users can interact with the data presented by the PostgreSQL Connector as easily as interacting with a database table.