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Hands-On Application Development with PyCharm

You're reading from   Hands-On Application Development with PyCharm Build applications like a pro with the ultimate python development tool

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Product type Paperback
Published in Oct 2023
Publisher Packt
ISBN-13 9781837632350
Length 652 pages
Edition 2nd Edition
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Authors (2):
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Quan Nguyen Quan Nguyen
Author Profile Icon Quan Nguyen
Quan Nguyen
Bruce M. Van Horn II Bruce M. Van Horn II
Author Profile Icon Bruce M. Van Horn II
Bruce M. Van Horn II
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Table of Contents (24) Chapters Close

Preface 1. Part 1: The Basics of PyCharm
2. Chapter 1: Introduction to PyCharm – the Most Popular IDE for Python FREE CHAPTER 3. Chapter 2: Installing and Configuring PyCharm 4. Part 2: Improving Your Productivity
5. Chapter 3: Customizing Interpreters and Virtual Environments 6. Chapter 4: Editing and Formatting with Ease in PyCharm 7. Chapter 5: Version Control with Git in PyCharm 8. Chapter 6: Seamless Testing, Debugging, and Profiling 9. Part 3: Web Development in PyCharm
10. Chapter 7: Web Development with JavaScript, HTML, and CSS 11. Chapter 8: Building a Dynamic Web Application with Flask 12. Chapter 9: Creating a RESTful API with FastAPI 13. Chapter 10: More Full Stack Frameworks – Django and Pyramid 14. Chapter 11: Understanding Database Management in PyCharm 15. Part 4: Data Science with PyCharm
16. Chapter 12: Turning On Scientific Mode 17. Chapter 13: Dynamic Data Viewing with SciView and Jupyter 18. Chapter 14: Building a Data Pipeline in PyCharm 19. Part 5: Plugins and Conclusion
20. Chapter 15: More Possibilities with Plugins 21. Chapter 16: Your Next Steps with PyCharm 22. Index 23. Other Books You May Enjoy

Building a Data Pipeline in PyCharm

The term data pipeline generally denotes a step-wise procedure that entails collecting, processing, and analyzing data. This term is widely used in the industry to express the need for a reliable workflow that takes raw data and converts it into actionable insights. Some data pipelines work at massive scales, such as a marketing technology (MarTech) company ingesting millions of data points from Kafka streams, storing them in large data stores such as Hadoop or Clickhouse, and then cleansing, enriching, and visualizing that data. Other times, the data is smaller but far more impactful, such as the project we’ll be working on in this chapter.

In this chapter, we will learn about the following topics:

  • How to work with and maintain datasets
  • How to clean and preprocess data
  • How to visualize data
  • How to utilize machine learning (ML)

Throughout this chapter, you will be able to apply what you have learned about the...

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