Ready to get started?

Learn more about the CData Python Connector for Apache Hive or download a free trial:

Download Now

Use pandas to Visualize Hive Data in Python

The CData Python Connector for Hive enables you use pandas and other modules to analyze and visualize live Hive 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 Hive, the pandas & Matplotlib modules, and the SQLAlchemy toolkit, you can build Hive-connected Python applications and scripts for visualizing Hive data. This article shows how to use the pandas, SQLAlchemy, and Matplotlib built-in functions to connect to Hive data, execute queries, and visualize the results.

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

Connecting to Hive Data

Connecting to Hive 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.

Set the Server, Port, TransportMode, and AuthScheme connection properties to connect to Hive.

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

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

engine = create_engine("apachehive:///?Server=127.0.0.1&Port=10000&TransportMode=BINARY")

Execute SQL to Hive

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

df = pandas.read_sql("SELECT City, CompanyName FROM Customers WHERE Country = 'US'", engine)

Visualize Hive Data

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

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

Free Trial & More Information

Download a free, 30-day trial of the Hive Python Connector to start building Python apps and scripts with connectivity to Hive 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("apachehive:///?Server=127.0.0.1&Port=10000&TransportMode=BINARY")
df = pandas.read_sql("SELECT City, CompanyName FROM Customers WHERE Country = 'US'", engine)

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