How to Visualize PostgreSQL Data in Python with pandas

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

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

Connecting to PostgreSQL Data

Connecting to PostgreSQL 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.

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.

Follow the procedure below to install the required modules and start accessing PostgreSQL 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 PostgreSQL Data in Python

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

engine = create_engine("postgresql:///?User=postgres&Password=admin&Database=postgres&Server=127.0.0.1&Port=5432")

Execute SQL to PostgreSQL

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 PostgreSQL Data

With the query results stored in a DataFrame, use the plot function to build a chart to display the PostgreSQL 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 CData Python Connector for PostgreSQL to start building Python apps and scripts with connectivity to PostgreSQL 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("postgresql:///?User=postgres&Password=admin&Database=postgres&Server=127.0.0.1&Port=5432")
df = pandas.read_sql("SELECT ShipName, ShipCity FROM Orders WHERE ShipCountry = 'USA'", engine)

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