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

How to Visualize Google Search Data in Python with pandas

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

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

Connecting to Google Search Results

Connecting to Google Search results 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 search with a Google custom search engine, you need to set the CustomSearchId and ApiKey connection properties.

To obtain the CustomSearchId property, sign into Google Custom Search Engine and create a new search engine.

To obtain the ApiKey property, you must enable the Custom Search API in the Google API Console.

Follow the procedure below to install the required modules and start accessing Google Search 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 Search Results in Python

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

engine = create_engine("googlesearch:///?CustomSearchId=def456&ApiKey=abc123")

Execute SQL to Google Search

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

df = pandas.read_sql("SELECT Title, ViewCount FROM VideoSearch WHERE SearchTerms = 'WayneTech'", engine)

Visualize Google Search Results

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

df.plot(kind="bar", x="Title", y="ViewCount")

Free Trial & More Information

Download a free, 30-day trial of the CData Python Connector for Google Search to start building Python apps and scripts with connectivity to Google Search results. 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("googlesearch:///?CustomSearchId=def456&ApiKey=abc123")
df = pandas.read_sql("SELECT Title, ViewCount FROM VideoSearch WHERE SearchTerms = 'WayneTech'", engine)

df.plot(kind="bar", x="Title", y="ViewCount")