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

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

Connecting to SendGrid Data

Connecting to SendGrid 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 make use of all the available features, provide the User and Password connection properties.

To connect with limited features, you can set the APIKey connection property instead. See the "Getting Started" chapter of the help documentation for a guide to obtaining the API key.

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

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

engine = create_engine("sendgrid:///?User=admin&Password=abc123")

Execute SQL to SendGrid

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, Clicks FROM AdvancedStats WHERE Type = 'Device'", engine)

Visualize SendGrid Data

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

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

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

Free Trial & More Information

Download a free, 30-day trial of the CData Python Connector for SendGrid to start building Python apps and scripts with connectivity to SendGrid 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("sendgrid:///?User=admin&Password=abc123")
df = pandas.read_sql("SELECT Name, Clicks FROM AdvancedStats WHERE Type = 'Device'", engine)

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