How to Build an ETL App for CSV 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 Python Connector for CSV and the petl framework, you can build CSV-connected applications and pipelines for extracting, transforming, and loading CSV data. This article shows how to connect to CSV with the CData Python Connector and use petl and pandas to extract, transform, and load CSV data.

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

Connecting to CSV Data

Connecting to CSV 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 DataSource property must be set to a valid local folder name.

Also, specify the IncludeFiles property to work with text files having extensions that differ from .csv, .tab, or .txt. Specify multiple file extensions in a comma-separated list. You can also set Extended Properties compatible with the Microsoft Jet OLE DB 4.0 driver. Alternatively, you can provide the format of text files in a Schema.ini file.

Set UseRowNumbers to true if you are deleting or updating in CSV. This will create a new column with the name RowNumber which will be used as key for that table.

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

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

cnxn = mod.connect("DataSource=MyCSVFilesFolder;")

Create a SQL Statement to Query CSV

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

sql = "SELECT City, TotalDue FROM Customer WHERE FirstName = 'Bob'"

Extract, Transform, and Load the CSV Data

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

Loading CSV Data into a CSV File

table1 = etl.fromdb(cnxn,sql)

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

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

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

cnxn = mod.connect("DataSource=MyCSVFilesFolder;")

sql = "SELECT City, TotalDue FROM Customer WHERE FirstName = 'Bob'"

table1 = etl.fromdb(cnxn,sql)

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

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