How to Visualize Aircall 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 Aircall-connected Python applications and scripts for visualizing Aircall data. This article shows how to use the pandas, SQLAlchemy, and Matplotlib built-in functions to connect to Aircall data, execute queries, and visualize the results.

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

Connecting to Aircall Data

Connecting to Aircall 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 Aircall Profile on disk (e.g. C:\profiles\Aircall.apip). Next, set the ProfileSettings connection property to the connection string for Aircall (see below).

Aircall API Profile Settings

In Aircall, go to your company settings and create a new API key to receive an API ID and API token. Combine them as

APIId:APIToken

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

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

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

Execute SQL to Aircall

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, Direction FROM Calls WHERE Status = 'completed'", engine)

Visualize Aircall Data

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

df.plot(kind="bar", x="Id", y="Direction")
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 Aircall 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\Aircall.apip&ProfileSettings='APIKey=your_api_id:your_api_token'")
df = pandas.read_sql("SELECT Id, Direction FROM Calls WHERE Status = 'completed'", engine)

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