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Python

Google Contacts Python Connector

Read, write, and update Google Contacts with Python

Easily connect Python-based data access, visualization, ORM, ETL, AI/ML, and custom apps with Google Contacts!

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CData Drivers now work with AI Coding tools

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

Features

Connect to live Google Contacts data, for real-time data access with the Google Contacts JDBC Driver
Full support for data aggregation and complex JOINs in SQL queries
Secure connectivity through modern cryptography, including TLS 1.2, SHA-256, ECC, etc.
Seamless integration with leading BI, reporting, and ETL tools and with custom applications via the Google Contacts Connector.

Specifications

Python Database API (DB-API) Modules for Google Contacts with bi-directional access.
Write SQL, get Google Contacts data. Access Google Contacts through standard Python Database Connectivity.
Integration with popular Python tools like Pandas, SQLAlchemy, Dash & petl.
Simple command-line based data exploration of Google Contacts data!
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 Google Contacts

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


Connecting to Google Contacts with Python

CData Python Connectors leverage the Database API (DB-API) interface to make it easy to work with Google Contacts 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 Google Contacts
  • Query Google Contacts to retrieve or update data
  • Connect your Google Contacts data with Python data tools.


Connecting to Google Contacts in Python

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

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.

import cdata.googlecontacts as mod
conn = mod.connect("[email protected]; Password=password;")

#Create cursor and iterate over results
cur = conn.cursor()
cur.execute("SELECT * FROM Contacts")
	
rs = cur.fetchall()
	
for row in rs:
print(row)
		
engine = create_engine("googlecontacts///Password=password&User=user")

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

df.plot()
plt.show()

Visualize Google Contacts Data with pandas

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

More than read-only: full update/CRUD support

Google Contacts 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 Google Contacts Connector as easily as interacting with a database table.

AI-assisted development with CData CLI

Build Google Contacts integrations faster with AI that understands your schema

Schema-aware AI

CData CLI gives AI coding tools access to your Google Contacts schema. No more guessing table names or column types—AI sees the same metadata in your Python Connectors.

Your AI Knows SQL

How to find table names, column names, and how to generate SQL syntax are things that AI knows well from millions of training data. No need for customization, no hallucinations. Your AI acts like a domain specialist to Google Contacts.

More Accurate, More Token-Efficient

With CData CLI's queryable schema detection and highly efficient queries with filters, aggregation, joins with correct pushdown, your AI will achieve more accuracy with less token usage.

Supported AI Coding Tools
Download CData CLI