Use pandas to Visualize Pinterest Data in Python

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Pinterest Python Connector

Python Connector Libraries for Pinterest Data Connectivity. Integrate Pinterest with popular Python tools like Pandas, SQLAlchemy, Dash & petl.

The CData Python Connector for Pinterest enables you use pandas and other modules to analyze and visualize live Pinterest data in Python.

The rich ecosystem of Python modules lets you get to work quickly and integrate your systems more effectively. With the CData Python Connector for Pinterest, the pandas & Matplotlib modules, and the SQLAlchemy toolkit, you can build Pinterest-connected Python applications and scripts for visualizing Pinterest data. This article shows how to use the pandas, SQLAlchemy, and Matplotlib built-in functions to connect to Pinterest data, execute queries, and visualize the results.

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

Connecting to Pinterest Data

Connecting to Pinterest 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.

Pinterest authentication is based on the standard OAuth flow. To authenticate, you must initially create an app via the Pinterest developer platform where you can obtain an OAuthClientId, OAuthClientSecret, and CallbackURL.

Set InitiateOAuth to GETANDREFRESH and set OAuthClientId, OAuthClientSecret, and CallbackURL based on the property values for the app you created.

See the Help documentation for other OAuth authentication flows.

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

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

engine = create_engine("pinterest:///?OAuthClientId=YourClientId&OAuthClientSecret=YourClientSecret&CallbackURL='https://localhost:33333'InitiateOAuth=GETANDREFRESH&OAuthSettingsLocation=/PATH/TO/OAuthSettings.txt")

Execute SQL to Pinterest

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, Username FROM Users WHERE FirstName = 'Jane'", engine)

Visualize Pinterest Data

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

df.plot(kind="bar", x="Id", y="Username")

Free Trial & More Information

Download a free, 30-day trial of the Pinterest Python Connector to start building Python apps and scripts with connectivity to Pinterest 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("pinterest:///?OAuthClientId=YourClientId&OAuthClientSecret=YourClientSecret&CallbackURL='https://localhost:33333'InitiateOAuth=GETANDREFRESH&OAuthSettingsLocation=/PATH/TO/OAuthSettings.txt")
df = pandas.read_sql("SELECT Id, Username FROM Users WHERE FirstName = 'Jane'", engine)

df.plot(kind="bar", x="Id", y="Username")