How to Visualize PostgreSQL Data in Python with pandas via CData Connect AI

The rich ecosystem of Python modules lets you get to work quickly and integrate your systems more effectively. With the CData Connect AI Python SDK, the pandas and Matplotlib modules, you can build PostgreSQL-connected Python applications and scripts for visualizing PostgreSQL data. This article shows how to connect to Connect AI and use the pandas and Matplotlib built-in functions to query PostgreSQL data and visualize the results.

The Connect AI Python SDK (cdata-connect-ai) is a DB-API 2.0 (PEP 249) compliant client, so pandas can read query results directly from the SDK connection object. There is no driver to install per source and no SQLAlchemy engine to configure: connect with a Personal Access Token and pass the connection straight to pandas.read_sql.

Connect to PostgreSQL in Connect AI

CData Connect AI uses a straightforward, point-and-click interface to connect to data sources.

1. Log into Connect AI, click Sources, and then click Add Connection

2. Select "PostgreSQL" from the Add Connection panel

3. Enter the necessary authentication properties to connect to PostgreSQL.

   To connect to PostgreSQL, set the Server, Port (the default port is 5432), and Database connection properties and set the User and Password you wish to use to authenticate to the server. If the Database property is not specified, the data provider connects to the user's default database.

   SSH Connectivity for PostgreSQL

   You can use SSH (Secure Shell) to authenticate with PostgreSQL, whether the instance is hosted on-premises or in supported cloud environments. SSH authentication ensures that access is encrypted (as compared to direct network connections).

   SSH Connections to PostgreSQL in Password Auth Mode

   To connect to PostgreSQL via SSH in Password Auth mode, set the following connection properties:

   • User: PostgreSQL User name
   • Password: PostgreSQL Password
   • Database: PostgreSQL database name
   • Server: PostgreSQL Server name
   • Port: PostgreSQL port number like 3306
   • UserSSH: "true"
   • SSHAuthMode: "Password"
   • SSHPort: SSH Port number
   • SSHServer: SSH Server name
   • SSHUser: SSH User name
   • SSHPassword: SSH Password

   SSH Connections to PostgreSQL in Public Key Auth Mode

   To connect to PostgreSQL via SSH in Password Auth mode, set the following connection properties:

   • User: PostgreSQL User name
   • Password: PostgreSQL Password
   • Database: PostgreSQL database name
   • Server: PostgreSQL Server name
   • Port: PostgreSQL port number like 3306
   • UserSSH: "true"
   • SSHAuthMode: "Public_Key"
   • SSHPort: SSH Port number
   • SSHServer: SSH Server name
   • SSHUser: SSH User name
   • SSHClientCret: the path for the public key certificate file
   
4. Click Save & Test
5. Navigate to the Permissions tab and update the user-based permissions.

Generate a Personal Access Token (PAT)

The Python SDK authenticates to Connect AI with your account email and a Personal Access Token (PAT). It is best practice to create a separate PAT for each application to maintain granularity of access.

1. Click the Gear icon () at the top right of the Connect AI app to open the Settings page.
2. On the Settings page, go to the Access Tokens section and click Create PAT.
3. Give the PAT a name and click Create.
4. The PAT is only visible at creation, so copy it and store it securely.

Install Required Modules

Install the SDK (with the pandas extra) and Matplotlib using the pip utility:

pip install "cdata-connect-ai[full]"
pip install matplotlib

Visualize PostgreSQL Data in Python

Import the modules, then connect to Connect AI with your account email and PAT. Identifiers are three-part: <Connection>.<Schema>.<Table>, where the connection name defaults to the source name (for example, PostgreSQL1).

import pandas
import matplotlib.pyplot as plt
import cdata_connect_ai

conn = cdata_connect_ai.connect(
    username="[email protected]",
    password="<your_pat>",
)

Query PostgreSQL with pandas

Use the read_sql function from pandas to execute a SQL statement and store the result set in a DataFrame. Pass the SDK connection directly, no engine required.

df = pandas.read_sql(
    "SELECT ShipName, ShipCity "
    "FROM [PostgreSQL1].[PostgreSQL].[Orders] "
    "WHERE ShipCountry = 'USA'",
    conn,
)

Note: pandas may print a UserWarning stating that it only officially supports SQLAlchemy connectables. This is expected when passing a DB-API connection directly—the query runs correctly and the warning is safe to ignore.

Visualize PostgreSQL Data

With the query results stored in a DataFrame, use the plot function to build a chart. The show method displays the chart in a new window.

df.plot(kind="bar", x="ShipName", y="ShipCity")
plt.show()

conn.close()

More Information and Free Trial

Now you can read live PostgreSQL data into pandas through the CData Connect AI Python SDK. For more information on connecting to PostgreSQL (and hundreds of other data sources), visit the Connect AI page. Sign up for a free trial and start working with live PostgreSQL data in Python.

Full Source Code

import pandas
import matplotlib.pyplot as plt
import cdata_connect_ai

conn = cdata_connect_ai.connect(
    username="[email protected]",
    password="<your_pat>",
)

df = pandas.read_sql(
    "SELECT ShipName, ShipCity "
    "FROM [PostgreSQL1].[PostgreSQL].[Orders] "
    "WHERE ShipCountry = 'USA'",
    conn,
)

df.plot(kind="bar", x="ShipName", y="ShipCity")
plt.show()

conn.close()