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

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

Connecting to Typeform Data

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

TypeForm API Profile Settings

Authentication to TypeForm uses the OAuth standard.

To authenticate to TypeForm, you must first register and configure an OAuth application with TypeForm here: https://admin.typeform.com/account#/section/tokens. Your app will be assigned a client ID and a client secret which can be set in the connection string. More information on setting up an OAuth application can be found at https://developer.typeform.com/get-started/.

Note that there are several different use scenarios which all require different redirect URIs:

• CData Desktop Applications: CData desktop applications (Sync, API Server, ArcESB) accept OAuth tokens at /src/oauthCallback.rst. The host and port is the same as the default port used by the application. For example, if you use http://localhost:8019/ to access CData Sync then the redirect URI will be http://localhost:8019/src/oauthCallback.rst.
• CData Cloud Applications: CData cloud applications are similar to their desktop counterparts. If you access Connect AI at https://1.2.3.4/ then you should use the redirect https://1.2.3.4/src/oauthCallback.rst.
• Desktop Application: When using a desktop application, the URI https://localhost:33333 is recommended.
• Web Application: When developing a web application using the driver, use your own URI here such as https://my-website.com/oauth.

After setting the following connection properties, you are ready to connect:

• AuthScheme: Set this to OAuth.
• InitiateOAuth: Set this to GETANDREFRESH. You can use InitiateOAuth to manage the process to obtain the OAuthAccessToken.
• 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.

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

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

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

Execute SQL to Typeform

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, Title FROM Tags WHERE SettingsIsPublic = 'true'", engine)

Visualize Typeform Data

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

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

👁 Typeform 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 Typeform 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\TypeForm.apip&Authscheme=OAuth&OAuthClientId=your_client_id&OAuthClientSecret=your_client_secret&CallbackUrl=your_callback_url")
df = pandas.read_sql("SELECT Id, Title FROM Tags WHERE SettingsIsPublic = 'true'", engine)

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