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

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

Connecting to Templated Data

Connecting to Templated 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.

To authenticate to Templated, you will need an API key. You can obtain your API key from the Templated Dashboard under the API Key tab (app.templated.io > Dashboard > API Key).

Using API Key Authentication

After setting the following connection properties, you are ready to connect:

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

Example connection string:

Profile=C:\profiles\Templated.apip;AuthScheme=APIKey;ProfileSettings='APIKey=my_api_key';

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

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

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

Execute SQL to Templated

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 Account WHERE = ''", engine)

Visualize Templated Data

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

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

👁 Templated 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 Templated 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\Templated.apip&AuthScheme=APIKey&ProfileSettings='APIKey=my_api_key'")
df = pandas.read_sql("SELECT , FROM Account WHERE = ''", engine)

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