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Extract, Transform, and Load Quandl Data in Python

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

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

Connecting to Quandl Data

Connecting to Quandl 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.

Quandl uses an API key for authentication. See the help documentation for a guide to obtaining the APIKey property.

Additionally, set the DatabaseCode connection property to the code identifying the Database whose Datasets you want to query with SQL. You can search the available Databases by querying the Databases view.

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

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

cnxn = mod.connect("APIKey=abc123;DatabaseCode=WIKI;")

Create a SQL Statement to Query Quandl

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

sql = "SELECT Date, Volume FROM AAPL WHERE Collapse = 'Daily'"

Extract, Transform, and Load the Quandl Data

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

Loading Quandl Data into a CSV File

table1 = etl.fromdb(cnxn,sql)

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

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

In the following example, we add new rows to the AAPL table.

Adding New Rows to Quandl

table1 = [ ['Date','Volume'], ['NewDate1','NewVolume1'], ['NewDate2','NewVolume2'], ['NewDate3','NewVolume3'] ]

etl.appenddb(table1, cnxn, 'AAPL')

With the CData Python Connector for Quandl, you can work with Quandl 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 Quandl Python Connector to start building Python apps and scripts with connectivity to Quandl 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.quandl as mod

cnxn = mod.connect("APIKey=abc123;DatabaseCode=WIKI;")

sql = "SELECT Date, Volume FROM AAPL WHERE Collapse = 'Daily'"

table1 = etl.fromdb(cnxn,sql)

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

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

table3 = [ ['Date','Volume'], ['NewDate1','NewVolume1'], ['NewDate2','NewVolume2'], ['NewDate3','NewVolume3'] ]

etl.appenddb(table3, cnxn, 'AAPL')