How to Visualize MongoDB 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 Python Connector for MongoDB, the pandas & Matplotlib modules, and the SQLAlchemy toolkit, you can build MongoDB-connected Python applications and scripts for visualizing MongoDB data. This article shows how to use the pandas, SQLAlchemy, and Matplotlib built-in functions to connect to MongoDB data, execute queries, and visualize the results.

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

Connecting to MongoDB Data

Connecting to MongoDB 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, Database, User, and Password connection properties to connect to MongoDB. To access MongoDB collections as tables you can use automatic schema discovery or write your own schema definitions. Schemas are defined in .rsd files, which have a simple format. You can also execute free-form queries that are not tied to the schema.

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

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

engine = create_engine("mongodb:///?Server=MyServer&Port=27017&Database=test&User=test&Password=Password")

Execute SQL to MongoDB

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

df = pandas.read_sql("SELECT borough, cuisine FROM restaurants WHERE Name = 'Morris Park Bake Shop'", engine)

Visualize MongoDB Data

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

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

Free Trial & More Information

Download a free, 30-day trial of the CData Python Connector for MongoDB to start building Python apps and scripts with connectivity to MongoDB 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("mongodb:///?Server=MyServer&Port=27017&Database=test&User=test&Password=Password")
df = pandas.read_sql("SELECT borough, cuisine FROM restaurants WHERE Name = 'Morris Park Bake Shop'", engine)

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