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

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

Connecting to Vimeo Data

Connecting to Vimeo 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.

Vimeo is a professional video hosting platform. The Vimeo API uses personal access tokens (bearer tokens) to enable secure access to video metadata, user information, channels, groups, categories, and related resources.

Using API Key Authentication

To authenticate to the Vimeo API, you will need to provide a personal access token. To obtain your access token:

1. Log in to your Vimeo account at https://vimeo.com
2. Navigate to https://developer.vimeo.com/apps
3. Create a new app or select an existing app
4. Under "Personal Access Tokens", click "Generate" to create a new token
5. Select the required scopes: public and private for read access
6. Copy the generated token

After obtaining your access token, set the following connection properties:

• AuthScheme: Set this to APIKey.
• APIKey: Set this to your Vimeo personal access token.

Example connection string

Profile=C:\profiles\Vimeo.apip;ProfileSettings='APIKey=your_personal_access_token';

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

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

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

Execute SQL to Vimeo

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

df = pandas.read_sql("SELECT , FROM Videos WHERE UserUri = '/users/12345678'", engine)

Visualize Vimeo Data

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

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

👁 Vimeo 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 Vimeo 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\Vimeo.apip&ProfileSettings='APIKey=your_personal_access_token'")
df = pandas.read_sql("SELECT , FROM Videos WHERE UserUri = '/users/12345678'", engine)

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