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

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

Connecting to Bannerbear Data

Connecting to Bannerbear 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.

Start by setting the Profile connection property to the location of the Bannerbear Profile on disk (e.g. C:\profiles\Bannerbear.apip). Next, set the ProfileSettings connection property to the connection string for Bannerbear (see below).

Bannerbear API Profile Settings

Retrieve your API keys from the Bannerbear dashboard. Specify the key type (Project or Master) via the KeyType property.

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

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

engine = create_engine("api:///?Profile=C:\profiles\Bannerbear.apip&ProfileSettings='APIKey=your_api_key'")

Execute SQL to Bannerbear

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

df = pandas.read_sql("SELECT Uid, CreatedAt FROM Account WHERE PaidPlanName = 'premium'", engine)

Visualize Bannerbear Data

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

df.plot(kind="bar", x="Uid", y="CreatedAt")
plt.show()

Free Trial & More Information

Download a free, 30-day trial of the CData API Driver for Python to start building Python apps and scripts with connectivity to Bannerbear 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("api:///?Profile=C:\profiles\Bannerbear.apip&ProfileSettings='APIKey=your_api_key'")
df = pandas.read_sql("SELECT Uid, CreatedAt FROM Account WHERE PaidPlanName = 'premium'", engine)

df.plot(kind="bar", x="Uid", y="CreatedAt")
plt.show()