Book Image

Hands-On Application Development with PyCharm - Second Edition

By : Bruce M. Van Horn II, Quan Nguyen
5 (1)
Book Image

Hands-On Application Development with PyCharm - Second Edition

5 (1)
By: Bruce M. Van Horn II, Quan Nguyen

Overview of this book

In the quest to develop robust, professional-grade software with Python and meet tight deadlines, it’s crucial to have the best tools at your disposal. In this second edition of Hands-on Application Development with PyCharm, you’ll learn tips and tricks to work at a speed and proficiency previously reserved only for elite developers. To achieve that, you’ll be introduced to PyCharm, the premiere professional integrated development environment for Python programmers among the myriad of IDEs available. Regardless of how Python is utilized, whether for general automation scripting, utility creation, web applications, data analytics, machine learning, or business applications, PyCharm offers tooling that simplifies complex tasks and streamlines common ones. In this book, you'll find everything you need to harness PyCharm's full potential and make the most of Pycharm's productivity shortcuts. The book comprehensively covers topics ranging from installation and customization to web development, database management, and data analysis pipeline development helping you become proficient in Python application development in diverse domains. By the end of this book, you’ll have discovered the remarkable capabilities of PyCharm and how you can achieve a new level of capability and productivity.
Table of Contents (24 chapters)
1
Part 1: The Basics of PyCharm
4
Part 2: Improving Your Productivity
9
Part 3: Web Development in PyCharm
15
Part 4: Data Science with PyCharm
19
Part 5: Plugins and Conclusion

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...