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

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

Connecting to GraphQL Data

Connecting to GraphQL 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.

You must specify the URL of the GraphQL service. The driver supports two types of authentication:

• Set AuthScheme to Basic. You must specify the User and Password of the GraphQL service.
• Take a look at the OAuth section in the Help documentation for detailed instructions.

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

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

engine = create_engine("graphql:///?AuthScheme=Basic&User=username&Password=password&URL=https://mysite.com&InitiateOAuth=GETANDREFRESH")

Execute SQL to GraphQL

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

df = pandas.read_sql("SELECT Name, Email FROM Users WHERE UserLogin = 'admin'", engine)

Visualize GraphQL Data

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

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

👁 GraphQL data in a Python plot (Salesforce is shown).

Free Trial & More Information

Download a free, 30-day trial of the CData Python Connector for GraphQL to start building Python apps and scripts with connectivity to GraphQL 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("graphql:///?AuthScheme=Basic&User=username&Password=password&URL=https://mysite.com&InitiateOAuth=GETANDREFRESH")
df = pandas.read_sql("SELECT Name, Email FROM Users WHERE UserLogin = 'admin'", engine)

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