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

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

Connecting to Sentry Data

Connecting to Sentry 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

Sentry uses token-based authentication. To obtain an Auth Token:

1. Log in to your Sentry account at https://sentry.io
2. Navigate to Settings > Auth Tokens
3. Click "Create New Token"
4. Select the required scopes and click "Create Token"
5. Copy the generated token (it will only be shown once)

After obtaining your Auth Token, set the following connection properties:

• AuthScheme: Set this to APIKey.

Set the following in the ProfileSettings connection property:

• APIKey: Set this to your Sentry Auth Token.
• OrganizationId: Set this to your Sentry organization slug or ID.

Example Connection String

Profile=C:\profiles\Sentry.apip;AuthScheme=APIKey;ProfileSettings="APIKey=your_auth_token;OrganizationId=your_org_slug";

Connecting to Sentry

Once the authentication is configured, you can connect to Sentry and query data from any of the available tables such as Organizations, Projects, Issues, and Events.

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

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

engine = create_engine("api:///?Profile=C:\profiles\Sentry.apip&AuthScheme=APIKey&ProfileSettings="APIKey=your_auth_token&OrganizationId=your_org_slug"")

Execute SQL to Sentry

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 UserOrganizations WHERE = ''", engine)

Visualize Sentry Data

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

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

👁 Sentry 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 Sentry 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\Sentry.apip&AuthScheme=APIKey&ProfileSettings="APIKey=your_auth_token&OrganizationId=your_org_slug"")
df = pandas.read_sql("SELECT , FROM UserOrganizations WHERE = ''", engine)

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