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

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

Connecting to Box Data

Connecting to Box 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.

Box uses the OAuth standard to authenticate. To authenticate to Box, you will need to obtain the OAuthClientId, OAuthClientSecret, and CallbackURL by registering an app. See the "Getting Started" chapter of the help documentation for a guide to using OAuth.

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

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

engine = create_engine("box:///?OAuthClientId=MyOAuthClientId&OAuthClientSecret=MyOAuthClientSecret&CallbackURL=http://localhost:33333&InitiateOAuth=GETANDREFRESH&OAuthSettingsLocation=/PATH/TO/OAuthSettings.txt")

Execute SQL to Box

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

df = pandas.read_sql("SELECT Name, Size FROM Files WHERE Id = '123'", engine)

Visualize Box Data

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

df.plot(kind="bar", x="Name", y="Size")
plt.show()

Free Trial & More Information

Download a free, 30-day trial of the CData Python Connector for Box to start building Python apps and scripts with connectivity to Box 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("box:///?OAuthClientId=MyOAuthClientId&OAuthClientSecret=MyOAuthClientSecret&CallbackURL=http://localhost:33333&InitiateOAuth=GETANDREFRESH&OAuthSettingsLocation=/PATH/TO/OAuthSettings.txt")
df = pandas.read_sql("SELECT Name, Size FROM Files WHERE Id = '123'", engine)

df.plot(kind="bar", x="Name", y="Size")
plt.show()