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

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

Connecting to Vercel Data

Connecting to Vercel 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.

Using API Key Authentication

Vercel uses Bearer token authentication. You can use either a personal access token or an OAuth access token as the API key.

To obtain a personal access token:

1. Log into your Vercel account at https://vercel.com/
2. Navigate to Account Settings > Tokens.
3. Click Create Token, enter a name and expiration, and click Create.
4. Copy the generated token (it will only be shown once).

After obtaining your token, set the following connection properties:

• AuthScheme: Set this to APIKey.
• APIKey: Set this to your Vercel personal access token or OAuth access token.

Example Connection String

Profile=C:\profiles\Vercel.apip;AuthScheme=APIKey;APIKey=your_access_token;

Working with Teams

Many Vercel resources are scoped to a team. To scope all requests to a specific team, set the TeamId connection property to your team's ID. You can find your team ID by querying the Teams table or from the Vercel dashboard. Alternatively, you can specify TeamId in your SQL queries using the WHERE clause where supported.

Connecting to Vercel

Once the authentication is configured, you can connect to Vercel and query data from any of the available tables such as Projects, Deployments, Teams, and Domains.

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

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

engine = create_engine("api:///?Profile=C:\profiles\Vercel.apip&AuthScheme=APIKey&APIKey=your_access_token")

Execute SQL to Vercel

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 User WHERE  = ''", engine)

Visualize Vercel Data

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

df.plot(kind="bar", x="", y="")
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 Vercel 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\Vercel.apip&AuthScheme=APIKey&APIKey=your_access_token")
df = pandas.read_sql("SELECT ,  FROM User WHERE  = ''", engine)

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