CData Python Connector for Square

Overview

The CData Python Connector for Square allows developers to write Python scripts with connectivity to Square. The connector wraps the complexity of accessing Square data in an interface commonly used by python connectors to common database systems.

Key Features

A variety of WHL files that accommodate several execution environments after installation with "pip install".
Supported for Python 3.6 and Python 3.7 (including Anacondas), within both Windows and Linux, whether 64-bit or 32-bit. A wheel for for Python 3.8 distributions on Mac is also available.
Write and execute SQL queries to fetch and update data in Square.
Custom dialect class that enables SQLAlchemy ORM to use this connector.

Getting Started

See Getting Started to install the connector to your python distribution and to create a basic connection to Square.

Using the Python Connector

See Using the Connector for examples of executing basic SELECT, INSERT, UPDATE, DELETE, and EXECUTE queries with the module's provided classes. See to connect Square data to tools such as Pandas.

SQLAlchemy ORM

SQLAlchemy can be leveraged to model the tables in Square with mapped classes. See From SQLAlchemy for instructions for configuring the Python connector with SQLAlchemy.

Pandas

Pandas' DataFrames can be used alongside the connector to generate analytical graphics. See From Pandas for a guide.

Schema Discovery

See Schema Discovery to query the provided system tables, which allows users to discover the available tables, views, and stored procedure, alongside additional information about their columns or parameters.

SQL Compliance

See SQL Compliance for a syntax reference and code examples outlining the supported SQL.

Caching Data

See Caching Data to configure replication and caching for a range of scenarios common to remote data access. Configurations include:

Autocache: Automatically cache data to a lightweight database. Save data for later offline use or enable fast reporting from the cache.
Replication: Copy data to local and cloud data stores such as Oracle, SQL Server, Google Cloud SQL, and so on. The replication commands allow for intelligent incremental updates to cached data.
No caching: Work with remote data only. No local cache file is created.

Data Model

See Data Model for the available database objects. This section also provides more detailed information on querying specific Square entities.

Connection String Options

The Connection properties describe the various options that can be used to establish a connection.