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Extract, Transform, and Load Google Search Results in Python

The CData Python Connector for Google Search enables you to create ETL applications and pipelines for Google Search results in Python with petl.

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 and the petl framework, you can build Google Search-connected applications and pipelines for extracting, transforming, and loading Google Search results. This article shows how to connect to Google Search with the CData Python Connector and use petl and pandas to extract, transform, and load Google Search 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.

After installing the CData Google Search Connector, follow the procedure below to install the other required modules and start accessing Google Search through Python objects.

Install Required Modules

Use the pip utility to install the required modules and frameworks:

pip install petl
pip install pandas

Build an ETL App for Google Search Results in Python

Once the required modules and frameworks are installed, we are ready to build our ETL app. Code snippets follow, but the full source code is available at the end of the article.

First, be sure to import the modules (including the CData Connector) with the following:

import petl as etl
import pandas as pd
import cdata.googlesearch as mod

You can now connect with a connection string. Use the connect function for the CData Google Search Connector to create a connection for working with Google Search results.

cnxn = mod.connect("CustomSearchId=def456;ApiKey=abc123;")

Create a SQL Statement to Query Google Search

Use SQL to create a statement for querying Google Search. In this article, we read data from the VideoSearch entity.

sql = "SELECT Title, ViewCount FROM VideoSearch WHERE SearchTerms = 'WayneTech'"

Extract, Transform, and Load the Google Search Results

With the query results stored in a DataFrame, we can use petl to extract, transform, and load the Google Search results. In this example, we extract Google Search results, sort the data by the ViewCount column, and load the data into a CSV file.

Loading Google Search Results into a CSV File

table1 = etl.fromdb(cnxn,sql)

table2 = etl.sort(table1,'ViewCount')

etl.tocsv(table2,'videosearch_data.csv')

With the CData Python Connector for Google Search, you can work with Google Search results just like you would with any database, including direct access to data in ETL packages like petl.

Free Trial & More Information

Download a free, 30-day trial of the Google Search Python Connector 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 petl as etl
import pandas as pd
import cdata.googlesearch as mod

cnxn = mod.connect("CustomSearchId=def456;ApiKey=abc123;")

sql = "SELECT Title, ViewCount FROM VideoSearch WHERE SearchTerms = 'WayneTech'"

table1 = etl.fromdb(cnxn,sql)

table2 = etl.sort(table1,'ViewCount')

etl.tocsv(table2,'videosearch_data.csv')