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

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

Connecting to Streak Data

Connecting to Streak 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.

Use the following steps to generate a new API key for authenticating to Streak.

1. Navigate to Gmail
2. Click on the Streak dropdown to the right of the search bar
3. Select the Integrations button. This will open a window where you can view existing integrations and create new API keys.
4. Under the Streak API section of integrations, click the button to Create New Key.

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

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

engine = create_engine("streak:///?ApiKey=8c84j9b4j54762ce809ej6a782d776j3")

Execute SQL to Streak

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

df = pandas.read_sql("SELECT UserKey, Email FROM Users WHERE Email = 'user@domain.com'", engine)

Visualize Streak Data

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

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

Free Trial & More Information

Download a free, 30-day trial of the CData Python Connector for Streak to start building Python apps and scripts with connectivity to Streak 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("streak:///?ApiKey=8c84j9b4j54762ce809ej6a782d776j3")
df = pandas.read_sql("SELECT UserKey, Email FROM Users WHERE Email = 'user@domain.com'", engine)

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