How to Visualize Linear 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 Python Connector for Linear, the pandas & Matplotlib modules, and the SQLAlchemy toolkit, you can build Linear-connected Python applications and scripts for visualizing Linear data. This article shows how to use the pandas, SQLAlchemy, and Matplotlib built-in functions to connect to Linear data, execute queries, and visualize the results.

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

Connecting to Linear Data

Connecting to Linear 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.

You can authenticate to Linear with a personal API key or with OAuth 2.0. The API key is the simplest option for connecting with your own Linear account.

Authenticating with an API Key

Set the following connection properties:

• AuthScheme: Set this to APIKey.
• APIKey: A Linear personal API key.

To create a personal API key, log in to Linear, open Settings > Security & access > Personal API keys, select New API key, and create it. Copy the key immediately, because Linear shows it only once.

Authenticating with OAuth

OAuth requires a custom OAuth application registered in Linear (Settings > API > OAuth applications), which provides the OAuthClientId and OAuthClientSecret. Two flows are supported:

• Authorization code: Set AuthScheme to OAuth, InitiateOAuth to GETANDREFRESH, and provide OAuthClientId, OAuthClientSecret, and the CallbackURL defined in your application (e.g., http://localhost:33333). The driver opens Linear in your browser so you can grant access.
• Client credentials: Set AuthScheme to OAuthClient and provide OAuthClientId and OAuthClientSecret. This authenticates the application itself, with no browser interaction, and suits machine-to-machine integrations.

By default, the driver requests the read,write scopes. The driver refreshes the access token automatically when it expires.

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

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

engine = create_engine("linear:///?AuthScheme=APIKey&APIKey=myAPIKey")

Execute SQL to Linear

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, name FROM Team WHERE key = 'ENG'", engine)

Visualize Linear Data

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

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

Free Trial & More Information

Download a free, 30-day trial of the CData Python Connector for Linear to start building Python apps and scripts with connectivity to Linear 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("linear:///?AuthScheme=APIKey&APIKey=myAPIKey")
df = pandas.read_sql("SELECT id, name FROM Team WHERE key = 'ENG'", engine)

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