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Extract, Transform, and Load Open Exchange Rates Data in Python

The CData Python Connector for Open Exchange Rates enables you to create ETL applications and pipelines for Open Exchange Rates data 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 Open Exchange Rates and the petl framework, you can build Open Exchange Rates-connected applications and pipelines for extracting, transforming, and loading Open Exchange Rates data. This article shows how to connect to Open Exchange Rates with the CData Python Connector and use petl and pandas to extract, transform, and load Open Exchange Rates data.

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

Connecting to Open Exchange Rates Data

Connecting to Open Exchange Rates 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.

The Open Exchange Rates API supports basic authentication with an App Id. After you register, your App Id is displayed in your account dashboard. Set this to the AppId connection property.

After installing the CData Open Exchange Rates Connector, follow the procedure below to install the other required modules and start accessing Open Exchange Rates 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 Open Exchange Rates 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.openexchangerates as mod

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

cnxn = mod.connect("AppId=abc1234;")

Create a SQL Statement to Query Open Exchange Rates

Use SQL to create a statement for querying Open Exchange Rates. In this article, we read data from the Projects entity.

sql = "SELECT Id, Statistics_ViewCount FROM Projects WHERE Id = 'MyProjectId'"

Extract, Transform, and Load the Open Exchange Rates Data

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

Loading Open Exchange Rates Data into a CSV File

table1 = etl.fromdb(cnxn,sql)

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

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

With the CData Python Connector for Open Exchange Rates, you can work with Open Exchange Rates 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 Open Exchange Rates Python Connector to start building Python apps and scripts with connectivity to Open Exchange Rates 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.openexchangerates as mod

cnxn = mod.connect("AppId=abc1234;")

sql = "SELECT Id, Statistics_ViewCount FROM Projects WHERE Id = 'MyProjectId'"

table1 = etl.fromdb(cnxn,sql)

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

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