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Use pandas to Visualize DataRobot Data in Python

The CData Python Connector for DataRobot enables you use pandas and other modules to analyze and visualize live DataRobot data in Python.

The rich ecosystem of Python modules lets you get to work quickly and integrate your systems more effectively. With the CData Python Connector for DataRobot, the pandas & Matplotlib modules, and the SQLAlchemy toolkit, you can build DataRobot-connected Python applications and scripts for visualizing DataRobot data. This article shows how to use the pandas, SQLAlchemy, and Matplotlib built-in functions to connect to DataRobot data, execute queries, and visualize the results.

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

Connecting to DataRobot Data

Connecting to DataRobot 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 connect to DataRobot, the following connection properties are required: User, Password, and PredictionInstance. DataRobotKey may also be required depending on your type of DataRobot predictions instance. If using the Predictions API, DataFile is required. The CSV DataFile should include a header row as the first row of the datafile. APIKey is not required, but can be supplied. If not supplied, the driver will handle obtaining an APIKey.

User, DataRobotKey, and APIKey are the credentials for the DataRobot account.

ProjectID, DataFile, and ModelId are the parameters for the project, dataset, and model type.

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

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

engine = create_engine("datarobot:///?\TO\input_file.csv&DataRobotKey=123-abc-456-def&User=username&Password=password")

Execute SQL to DataRobot

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, Prediction1Value FROM Predictions WHERE Id = '1'", engine)

Visualize DataRobot Data

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

df.plot(kind="bar", x="Id", y="Prediction1Value")

Free Trial & More Information

Download a free, 30-day trial of the DataRobot Python Connector to start building Python apps and scripts with connectivity to DataRobot 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("datarobot:///?\TO\input_file.csv&DataRobotKey=123-abc-456-def&User=username&Password=password")
df = pandas.read_sql("SELECT Id, Prediction1Value FROM Predictions WHERE Id = '1'", engine)

df.plot(kind="bar", x="Id", y="Prediction1Value")