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

How to Visualize Snowflake Data in Python with pandas

Use pandas and other modules to analyze and visualize live Snowflake 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 Snowflake, the pandas & Matplotlib modules, and the SQLAlchemy toolkit, you can build Snowflake-connected Python applications and scripts for visualizing Snowflake data. This article shows how to use the pandas, SQLAlchemy, and Matplotlib built-in functions to connect to Snowflake data, execute queries, and visualize the results.

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

Connecting to Snowflake Data

Connecting to Snowflake 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 Snowflake:

  1. Set User and Password to your Snowflake credentials and set the AuthScheme property to PASSWORD or OKTA.
  2. Set URL to the URL of the Snowflake instance (i.e.:
  3. Set Warehouse to the Snowflake warehouse.
  4. (Optional) Set Account to your Snowflake account if your URL does not conform to the format above.
  5. (Optional) Set Database and Schema to restrict the tables and views exposed.

See the Getting Started guide in the CData driver documentation for more information.

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

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

engine = create_engine("snowflake:///?User=Admin&Password=test123&Server=localhost&Database=Northwind&Warehouse=TestWarehouse&Account=Tester1")

Execute SQL to Snowflake

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

df = pandas.read_sql("SELECT Id, ProductName FROM Products WHERE Id = '1'", engine)

Visualize Snowflake Data

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

df.plot(kind="bar", x="Id", y="ProductName")

Free Trial & More Information

Download a free, 30-day trial of the CData Python Connector for Snowflake to start building Python apps and scripts with connectivity to Snowflake 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("snowflake:///?User=Admin&Password=test123&Server=localhost&Database=Northwind&Warehouse=TestWarehouse&Account=Tester1")
df = pandas.read_sql("SELECT Id, ProductName FROM Products WHERE Id = '1'", engine)

df.plot(kind="bar", x="Id", y="ProductName")