---
title: "Python for Data Engineering"
url: https://daily.dev/posts/python-for-data-engineering-77k7sldza
source_url: https://medium.com/@mariusz_kujawski/python-for-data-engineering-6bd6140033d4?source=rss------python-5
type: article
source: "Medium"
published: 2023-12-05T16:23:53.188Z
updated: 2023-12-05T16:23:50.681Z
tags: ["python", "data-science", "testing", "pandas", "data-engineering", "data-structures", "pyspark", "duckdb"]
reading_time: 14
upvotes: 7
comments: 0
language: en
---

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# Python for Data Engineering

**[Medium](https://daily.dev/sources/medium_js)** · 14 min read · 7 upvotes · 0 comments

## Summary

Python is widely used in data engineering for its versatile and powerful libraries. It offers built-in data structures like lists, tuples, dictionaries, and sets. List comprehension provides a shorter syntax for creating new lists. Python can be integrated with cloud storage using libraries like gcsfs, adlfs, and s3fs. Unit testing and mocking play a vital role in ensuring the correctness of data engineering code. DataFrame libraries like Pandas, PySpark, and Polars facilitate data manipulation and analysis. DuckDB is an in-memory analytical database management system, while Faker generates synthetic data for testing.

## Full article

daily.dev links to this article rather than hosting it. Read it at the original source: <https://medium.com/@mariusz_kujawski/python-for-data-engineering-6bd6140033d4?source=rss------python-5>

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---

Tags: [#python](https://daily.dev/tags/python), [#data-science](https://daily.dev/tags/data-science), [#testing](https://daily.dev/tags/testing), [#pandas](https://daily.dev/tags/pandas), [#data-engineering](https://daily.dev/tags/data-engineering), [#data-structures](https://daily.dev/tags/data-structures), [#pyspark](https://daily.dev/tags/pyspark), [#duckdb](https://daily.dev/tags/duckdb)

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