Ready to get started?

Learn more about the CData Python Connector for Quandl or download a free trial:

Download Now

Use pandas to Visualize Quandl Data in Python

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

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

Connecting to Quandl Data

Connecting to Quandl 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.

Quandl uses an API key for authentication. See the help documentation for a guide to obtaining the APIKey property.

Additionally, set the DatabaseCode connection property to the code identifying the Database whose Datasets you want to query with SQL. You can search the available Databases by querying the Databases view.

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

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

engine = create_engine("quandl:///?APIKey=abc123&DatabaseCode=WIKI")

Execute SQL to Quandl

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

df = pandas.read_sql("SELECT Date, Volume FROM AAPL WHERE Collapse = 'Daily'", engine)

Visualize Quandl Data

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

df.plot(kind="bar", x="Date", y="Volume")
plt.show()

Free Trial & More Information

Download a free, 30-day trial of the Quandl Python Connector to start building Python apps and scripts with connectivity to Quandl 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("quandl:///?APIKey=abc123&DatabaseCode=WIKI")
df = pandas.read_sql("SELECT Date, Volume FROM AAPL WHERE Collapse = 'Daily'", engine)

df.plot(kind="bar", x="Date", y="Volume")
plt.show()