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Python is one of the programming languages you’ll use as a data engineer. There are many Python libraries you should become familiar with as a data engineer. But Python’s standard library is packed with powerful modules for a range of relevant tasks—from file manipulation to data serialization, text processing, and more.

This article compiles some of the most helpful built-in Python modules for data engineering, specifically the following:

• File and directory management
• Data handling and serialization
• Database interaction
• Text processing
• Date and time manipulation
• System interaction

Let’s get started.

 

Built-in Python Modules for Data Engineering | Image by Author
 

1. os

 

The os module is your go-to tool for interacting with the operating system. It enables you to perform various tasks such as file path manipulations, directory management, and handling environment variables.

You can perform the following data engineering tasks with the os module’s functionalities:

• Automating the creation and deletion of directories for temporary or output data storage
• Manipulating file paths when organizing large datasets across different directories
• Handling environment variables to manage configuration settings in data pipelines

OS Module – Use Underlying Operating System Functionality, a tutorial by Corey Schafer, covers all the functionality of the os module.

 

2. pathlib

 

The pathlib module provides a more modern and object-oriented approach to handling file system paths. It allows for easy manipulation of file and directory paths with an intuitive and readable syntax, making it a favorite for file management tasks.

The pathlib module can come in handy in the following data engineering tasks:

• Streamlining the process of iterating over and validating large datasets
• Simplifying the management of paths when moving or copying files during ETL (Extract, Transform, Load) processes
• Ensuring cross-platform compatibility, especially in multi-environment data engineering workflows

Here are a couple of tutorials that  cover the basics of working with pathlib module:

 

3. shutil

 

The shutil module is for common high-level file operations. Which include copying, moving, and deleting files and directories. It’s ideal for tasks that involve manipulating large datasets or multiple files.

In data engineering projects, shutil can help with:

• Efficiently moving or copying large datasets across different storage locations
• Automating the cleanup of temporary files and directories after processing data
• Creating backups of critical datasets before processing or analysis

shutil: The Ultimate Python File Management Toolkit is a comprehensive tutorial on shutil.

 

4. csv

 

The csv module is essential for handling CSV files, which are a common format for data storage and exchange. It provides tools for reading from and writing to CSV files, with customizable options for handling different CSV formats.

Here are some tasks you can use the csv module for:

• Parsing and processing large CSV files as part of ETL pipelines
• Converting CSV data into other formats, such as JSON or database tables
• Writing processed or transformed data back into CSV format for downstream applications

CSV Module – How to Read, Parse, and Write CSV Files is a good reference to use the csv module.

 

5. json

 

The built-in json module is the go-to choice for working with JSON data—quite common when working with web services and APIs. It allows you to serialize and deserialize Python objects to and from JSON strings, making it easy to exchange data between your application and external systems.

You’ll use json module for:

• Seamlessly converting API responses into Python objects for further processing
• Storing config info or metadata in a structured format
• Handling complex, nested data structures often found in big data applications

Working with JSON Data using the json Module will help you learn all about working with JSON in Python.

 

6. pickle

 

The pickle module is used for serializing and deserializing Python objects to and from a binary format. It’s particularly useful for saving complex data structures, such as lists, dictionaries, or custom objects, to disk and reloading them later.

The pickle module is useful for the following tasks:

• Caching transformed data to speed up repetitive tasks in data pipelines
• Persisting trained models or data transformation steps for reproducibility
• Storing and reloading complex configurations or datasets between processing stages

Python Pickle Module for saving objects (serialization) is a short but helpful tutorial on the pickle module.

 

7. sqlite3

 

The sqlite3 module provides a simple interface for working with SQLite databases, which are lightweight and self-contained. This module is great for projects that require structured data storage without the overhead of a database server.

• Prototyping ETL pipelines before scaling them to fully fledged database systems
• Storing metadata, logging information, or intermediate results during data processing
• Quickly querying and managing structured data without setting up a database server

A Guide to Working with SQLite Databases in Python is a comprehensive tutorial to get started with SQLite databases in Python.

 

8. datetime

 

Working with dates and times is quite common when working with real-world datasets. The datetime module helps you manage date and time data in your applications.

It provides tools for working with dates, times, and time intervals, and supports formatting and parsing date strings for:

• Parsing and formatting timestamps in logs or event data
• Managing date ranges and calculating time intervals when working with real-world datasets

Datetime Module – How to work with Dates, Times, Timedeltas, and Timezones is an excellent tutorial to learn all about the datetime module.

 

9. re

 

The re module provides powerful tools for working with regular expressions, which are crucial for text processing. It enables you to search, match, and manipulate strings based on complex patterns, making it indispensable for data cleaning, validation, and transformation tasks.

• Extracting specific patterns from logs, raw data, or unstructured text
• Validating data formats, such as dates, emails, or phone numbers, during ETL processes
• Cleaning raw text data for further analysis

You can follow re Module – How to Write and Match Regular Expressions (Regex) to learn to use the built-in re module in great detail.

 

10. subprocess

 

The subprocess module is a powerful tool for running shell commands and interacting with the system shell from within your Python script.

It’s essential for automating system tasks, invoking command-line tools, or capturing output from external processes such as:

• Automating the execution of shell scripts or data processing commands
• Capturing output from command-line tools to integrate with Python workflows
• Orchestrating complex data processing pipelines that involve multiple tools and commands

Calling External Commands Using the Subprocess Module is a tutorial on getting started with the subprocess module.

 

Wrapping Up

 

I hope you found this round-up of Python’s built-in modules for data engineering helpful.

These can be good additions to your data engineering toolkit—providing the essential functionality needed to handle a wide variety of tasks without relying on external libraries.

If you’re interested in a collection of Python libraries for data engineering, read 7 Python Libraries Every Data Engineer Should Know.

 

 

Bala Priya C is a developer and technical writer from India. She likes working at the intersection of math, programming, data science, and content creation. Her areas of interest and expertise include DevOps, data science, and natural language processing. She enjoys reading, writing, coding, and coffee! Currently, she’s working on learning and sharing her knowledge with the developer community by authoring tutorials, how-to guides, opinion pieces, and more. Bala also creates engaging resource overviews and coding tutorials.