Use pandas to Visualize EnterpriseDB Data in Python

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EnterpriseDB Python Connector

Python Connector Libraries for EnterpriseDB Data Connectivity. Integrate EnterpriseDB with popular Python tools like Pandas, SQLAlchemy, Dash & petl.



The CData Python Connector for EnterpriseDB enables you use pandas and other modules to analyze and visualize live EnterpriseDB data in Python.

The rich ecosystem of Python modules lets you get to work quickly and integrate your systems more effectively. With the CData Python Connector for EnterpriseDB, the pandas & Matplotlib modules, and the SQLAlchemy toolkit, you can build EnterpriseDB-connected Python applications and scripts for visualizing EnterpriseDB data. This article shows how to use the pandas, SQLAlchemy, and Matplotlib built-in functions to connect to EnterpriseDB data, execute queries, and visualize the results.

With built-in optimized data processing, the CData Python Connector offers unmatched performance for interacting with live EnterpriseDB data in Python. When you issue complex SQL queries from EnterpriseDB, the driver pushes supported SQL operations, like filters and aggregations, directly to EnterpriseDB and utilizes the embedded SQL engine to process unsupported operations client-side (often SQL functions and JOIN operations).

Connecting to EnterpriseDB Data

Connecting to EnterpriseDB data looks just like connecting to any relational data source. Create a connection string using the required connection properties. For this article, you will pass the connection string as a parameter to the create_engine function.

The following connection properties are required in order to connect to data.

  • Server: The host name or IP of the server hosting the EnterpriseDB database.
  • Port: The port of the server hosting the EnterpriseDB database.

You can also optionally set the following:

  • Database: The default database to connect to when connecting to the EnterpriseDB Server. If this is not set, the user's default database will be used.

Connect Using Standard Authentication

To authenticate using standard authentication, set the following:

  • User: The user which will be used to authenticate with the EnterpriseDB server.
  • Password: The password which will be used to authenticate with the EnterpriseDB server.

Connect Using SSL Authentication

You can leverage SSL authentication to connect to EnterpriseDB data via a secure session. Configure the following connection properties to connect to data:

  • SSLClientCert: Set this to the name of the certificate store for the client certificate. Used in the case of 2-way SSL, where truststore and keystore are kept on both the client and server machines.
  • SSLClientCertPassword: If a client certificate store is password-protected, set this value to the store's password.
  • SSLClientCertSubject: The subject of the TLS/SSL client certificate. Used to locate the certificate in the store.
  • SSLClientCertType: The certificate type of the client store.
  • SSLServerCert: The certificate to be accepted from the server.

Follow the procedure below to install the required modules and start accessing EnterpriseDB through Python objects.

Install Required Modules

Use the pip utility to install the pandas & Matplotlib modules and the SQLAlchemy toolkit:

pip install pandas
pip install matplotlib
pip install sqlalchemy

Be sure to import the module with the following:

import pandas
import matplotlib.pyplot as plt
from sqlalchemy import create_engine

Visualize EnterpriseDB Data in Python

You can now connect with a connection string. Use the create_engine function to create an Engine for working with EnterpriseDB data.

engine = create_engine("enterprisedb:///?User=postgres&Password=admin&Database=postgres&Server=127.0.0.1&Port=5444")

Execute SQL to EnterpriseDB

Use the read_sql function from pandas to execute any SQL statement and store the resultset in a DataFrame.

df = pandas.read_sql("SELECT ShipName, ShipCity FROM Orders WHERE ShipCountry = 'USA'", engine)

Visualize EnterpriseDB Data

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

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

Free Trial & More Information

Download a free, 30-day trial of the EnterpriseDB Python Connector to start building Python apps and scripts with connectivity to EnterpriseDB data. Reach out to our Support Team if you have any questions.



Full Source Code

import pandas
import matplotlib.pyplot as plt
from sqlalchemy import create_engin

engine = create_engine("enterprisedb:///?User=postgres&Password=admin&Database=postgres&Server=127.0.0.1&Port=5444")
df = pandas.read_sql("SELECT ShipName, ShipCity FROM Orders WHERE ShipCountry = 'USA'", engine)

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