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

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

Connecting to Drift Data

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

Drift API Profile Settings

Drift uses OAuth-based authentication.

You must first register an application here: https://dev.drift.com. Your app will be assigned a client ID and a client secret. Set these in your connection string via the OAuthClientId and OAuthClientSecret properties. More information on setting up an OAuth application can be found at https://devdocs.drift.com/docs/.

After setting the following options in the ProfileSettings connection property, you are ready to connect:

• AuthScheme: Set this to OAuth.
• OAuthClientId: Set this to the Client Id that is specified in your app settings.
• OAuthClientSecret: Set this to Client Secret that is specified in your app settings.
• CallbackURL: Set this to the Redirect URI you specified in your app settings.
• InitiateOAuth: Set this to GETANDREFRESH. You can use InitiateOAuth to manage the process to obtain the OAuthAccessToken.

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

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

engine = create_engine("api:///?Profile=C:\profiles\Drift.apip&Authscheme=OAuth&OAuthClientId=your_client_id&OAuthClientSecret=your_client_secret&CallbackUrl=your_callback_url")

Execute SQL to Drift

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, DisplayName FROM Contacts WHERE LastName = 'Stark'", engine)

Visualize Drift Data

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

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

👁 Drift data in a Python plot (Salesforce is shown).

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 Drift data. Reach out to our Support Team if you have any questions.

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Full Source Code

import pandas
import matplotlib.pyplot as plt
from sqlalchemy import create_engin

engine = create_engine("api:///?Profile=C:\profiles\Drift.apip&Authscheme=OAuth&OAuthClientId=your_client_id&OAuthClientSecret=your_client_secret&CallbackUrl=your_callback_url")
df = pandas.read_sql("SELECT Id, DisplayName FROM Contacts WHERE LastName = 'Stark'", engine)

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