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

How to Visualize Gmail Data in Python with pandas

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

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

Connecting to Gmail Data

Connecting to Gmail 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.

There are two ways to authenticate to Gmail. Before selecting one, first ensure that you have enabled IMAP access in your Gmail account settings. See the "Connecting to Gmail" section under "Getting Started" in the installed documentation for a guide.

The User and Password properties, under the Authentication section, can be set to valid Gmail user credentials.

Alternatively, instead of providing the Password, you can use the OAuth authentication standard. To access Google APIs on behalf on individual users, you can use the embedded credentials or you can register your own OAuth app.

OAuth also enables you to use a service account to connect on behalf of users in a Google Apps domain. To authenticate with a service account, you will need to register an application to obtain the OAuth JWT values.

In addition to the OAuth values, you will need to provide the User. See the "Getting Started" chapter in the help documentation for a guide to using OAuth.

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

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

engine = create_engine("gmail:///?User=username&Password=password")

Execute SQL to Gmail

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

df = pandas.read_sql("SELECT Subject, Size FROM Inbox WHERE From = ''", engine)

Visualize Gmail Data

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

df.plot(kind="bar", x="Subject", y="Size")

Free Trial & More Information

Download a free, 30-day trial of the CData Python Connector for Gmail to start building Python apps and scripts with connectivity to Gmail 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("gmail:///?User=username&Password=password")
df = pandas.read_sql("SELECT Subject, Size FROM Inbox WHERE From = ''", engine)

df.plot(kind="bar", x="Subject", y="Size")