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Python Connector Libraries for Parquet Data Connectivity. Integrate Parquet with popular Python tools like Pandas, SQLAlchemy, Dash & petl.

How to Visualize Parquet Data in Python with pandas

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

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

Connecting to Parquet Data

Connecting to Parquet 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.

Connect to your local Parquet file(s) by setting the URI connection property to the location of the Parquet file.

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

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

engine = create_engine("parquet:///?URI=C:/folder/table.parquet")

Execute SQL to Parquet

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, Column1 FROM SampleTable_1 WHERE Column2 = 'SAMPLE_VALUE'", engine)

Visualize Parquet Data

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

df.plot(kind="bar", x="Id", y="Column1")

Free Trial & More Information

Download a free, 30-day trial of the CData Python Connector for Parquet to start building Python apps and scripts with connectivity to Parquet 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("parquet:///?URI=C:/folder/table.parquet")
df = pandas.read_sql("SELECT Id, Column1 FROM SampleTable_1 WHERE Column2 = 'SAMPLE_VALUE'", engine)

df.plot(kind="bar", x="Id", y="Column1")