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Use pandas to Visualize QuickBase Data in Python

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

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

Connecting to QuickBase Data

Connecting to QuickBase 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.

User Authentication Method

To authenticate with user credentials, specify the following connection properties:

  1. Set the User and Password.
  2. If your application requires an ApplicationToken;, you must provide it otherwise an error will be thrown. You can find the ApplicationToken under SpecificApp > Settings > App management > App properties > Advanced settings > Security options > Manage Application Token.

User Token Authentication

To authenticate with a user token, specify the following connection properties:

  1. Set UserToken and you are ready to connect. You can find the UserToken under Quick Base > My Preferences > My User Information > Manage User Tokens.

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

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

engine = create_engine("quickbase:///?User=user@domain.com&Password=password&Domain=myinstance.quickbase.com&ApplicationToken=bwkxrb5da2wn57bzfh9xn24")

Execute SQL to QuickBase

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, Column1 FROM SampleTable_1 WHERE Column2 = '100'", engine)

Visualize QuickBase Data

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

df.plot(kind="bar", x="Id", y="Column1")
plt.show()

Free Trial & More Information

Download a free, 30-day trial of the QuickBase Python Connector to start building Python apps and scripts with connectivity to QuickBase 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("quickbase:///?User=user@domain.com&Password=password&Domain=myinstance.quickbase.com&ApplicationToken=bwkxrb5da2wn57bzfh9xn24")
df = pandas.read_sql("SELECT Id, Column1 FROM SampleTable_1 WHERE Column2 = '100'", engine)

df.plot(kind="bar", x="Id", y="Column1")
plt.show()