Ready to get started?

Learn more about the CData Python Connector for Dropbox or download a free trial:

Download Now

Extract, Transform, and Load Dropbox Data in Python

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

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

Connecting to Dropbox Data

Connecting to Dropbox 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.

Dropbox uses the OAuth authentication standard. To authenticate using OAuth, you can use the embedded credentials or register an app with Dropbox.

See the Getting Started guide in the CData driver documentation for more information.

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

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

cnxn = mod.connect("InitiateOAuth=GETANDREFRESH;OAuthSettingsLocation=/PATH/TO/OAuthSettings.txt")")

Create a SQL Statement to Query Dropbox

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

sql = "SELECT Id, Name FROM Files WHERE Id = '1'"

Extract, Transform, and Load the Dropbox Data

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

Loading Dropbox Data into a CSV File

table1 = etl.fromdb(cnxn,sql)

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

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

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

Adding New Rows to Dropbox

table1 = [ ['Id','Name'], ['NewId1','NewName1'], ['NewId2','NewName2'], ['NewId3','NewName3'] ]

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

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

cnxn = mod.connect("InitiateOAuth=GETANDREFRESH;OAuthSettingsLocation=/PATH/TO/OAuthSettings.txt")")

sql = "SELECT Id, Name FROM Files WHERE Id = '1'"

table1 = etl.fromdb(cnxn,sql)

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

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

table3 = [ ['Id','Name'], ['NewId1','NewName1'], ['NewId2','NewName2'], ['NewId3','NewName3'] ]

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