How to Build an ETL App for SerpApi Data in Python with CData

The rich ecosystem of Python modules lets you get to work quickly and integrate your systems more effectively. With the CData API Driver for Python and the petl framework, you can build SerpApi-connected applications and pipelines for extracting, transforming, and loading SerpApi data. This article shows how to connect to SerpApi with the CData Python Connector and use petl and pandas to extract, transform, and load SerpApi data.

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

Connecting to SerpApi Data

Connecting to SerpApi 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.

Start by setting the Profile connection property to the location of the SerpAPI Profile on disk (e.g. C:\profiles\SerpAPI.apip). Next, set the ProfileSettings connection property to the connection string for SerpAPI (see below).

SerpAPI API Profile Settings

SerpAPI uses API key authentication. To obtain an API key:

1. Sign in to your SerpAPI account at https://serpapi.com
2. Navigate to the API Key page at https://serpapi.com/manage-api-key
3. Copy your private API key

After obtaining your API key, set the following connection properties:

• AuthScheme: Set this to APIKey.

Set the following in the ProfileSettings connection property:

• APIKey: Set this to your SerpAPI private API key.

After installing the CData SerpApi Connector, follow the procedure below to install the other required modules and start accessing SerpApi 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 SerpApi Data 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.api as mod

You can now connect with a connection string. Use the connect function for the CData SerpApi Connector to create a connection for working with SerpApi data.

cnxn = mod.connect("Profile=C:\profiles\SerpAPI.apip;AuthScheme=APIKey;ProfileSettings='APIKey=your_api_key_here';")

Create a SQL Statement to Query SerpApi

Use SQL to create a statement for querying SerpApi. In this article, we read data from the AmazonSearch entity.

sql = "SELECT Asin, Title FROM AmazonSearch WHERE SearchQuery = 'mechanical keyboard'"

Extract, Transform, and Load the SerpApi Data

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

Loading SerpApi Data into a CSV File

table1 = etl.fromdb(cnxn,sql)

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

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

With the CData API Driver for Python, you can work with SerpApi data 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 CData API Driver for Python to start building Python apps and scripts with connectivity to SerpApi data. Reach out to our Support Team if you have any questions.

Full Source Code

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

cnxn = mod.connect("Profile=C:\profiles\SerpAPI.apip;AuthScheme=APIKey;ProfileSettings='APIKey=your_api_key_here';")

sql = "SELECT Asin, Title FROM AmazonSearch WHERE SearchQuery = 'mechanical keyboard'"

table1 = etl.fromdb(cnxn,sql)

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

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