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Use pandas to Visualize Google Directory Data in Python

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

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

Connecting to Google Directory Data

Connecting to Google Directory 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.

Google uses the OAuth authentication standard. You can authorize the data provider to access Google Spreadsheets as an individual user or with a Google Apps Domain service account. See the Getting Started section of the data provider help documentation for an authentication guide.

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

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

engine = create_engine("googledirectory:///?OAuthClientId=MyOAuthClientId&OAuthClientSecret=MyOAuthClientSecret&CallbackURL=http://localhost&InitiateOAuth=GETANDREFRESH&OAuthSettingsLocation=/PATH/TO/OAuthSettings.txt")

Execute SQL to Google Directory

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, Description FROM MyTable WHERE Status = 'confirmed'", engine)

Visualize Google Directory Data

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

df.plot(kind="bar", x="Id", y="Description")
plt.show()

Free Trial & More Information

Download a free, 30-day trial of the Google Directory Python Connector to start building Python apps and scripts with connectivity to Google Directory 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("googledirectory:///?OAuthClientId=MyOAuthClientId&OAuthClientSecret=MyOAuthClientSecret&CallbackURL=http://localhost&InitiateOAuth=GETANDREFRESH&OAuthSettingsLocation=/PATH/TO/OAuthSettings.txt")
df = pandas.read_sql("SELECT Id, Description FROM MyTable WHERE Status = 'confirmed'", engine)

df.plot(kind="bar", x="Id", y="Description")
plt.show()