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

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

Connecting to Parallel Data

Connecting to Parallel 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.

The Parallel API uses API Key authentication via the x-api-key request header.

Using API Key Authentication

Your Parallel API key is required to create a connection. To obtain your API key:

1. Log into your Parallel account at app.parallel.ai.
2. Navigate to Settings or API Keys in your account dashboard.
3. Generate or copy your API key.

After obtaining your API key, set the following connection properties:

• AuthScheme: Set this to APIKey.
• APIKey: Set this to your Parallel API key.

Example connection string:

Profile=C:\profiles\Parallel.apip;AuthScheme=APIKey;ProfileSettings='APIKey=your_api_key';

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

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

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

Execute SQL to Parallel

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 MonitorEvents WHERE MonitorId = 'mon_abc123'", engine)

Visualize Parallel Data

With the query results stored in a DataFrame, use the plot function to build a chart to display the Parallel 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 Parallel 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\Parallel.apip&AuthScheme=APIKey&ProfileSettings='APIKey=your_api_key'")
df = pandas.read_sql("SELECT ,  FROM MonitorEvents WHERE MonitorId = 'mon_abc123'", engine)

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