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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

Dynamic Data Viewing with SciView and Jupyter

In this chapter, we will continue our scientific voyage through the exploration of two vital features of PyCharm: SciView and integration with Jupyter notebooks. Both features give us integrated and usable interfaces, allowing us to view and work with data and variables in our science projects.

We’ll begin by discussing the SciView panel, which was introduced tacitly in the last chapter. Here, we’ll be going into more depth and realism by working with NumPy arrays and pandas DataFrames.

After that, we’ll evolve our workflow even further to include coverage of working with interactive Python computing tools such as Jupyter notebooks within the context of our scientific projects in PyCharm.

By the end of this chapter, you should have gained understanding in the following areas:

  • Viewing and interacting with data in the SciView panel in PyCharm
  • The integration of Interactive Python (IPython) within...
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