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

Summary

A Python programmer typically works on a data science project in two ways—writing a traditional Python script or using a Jupyter notebook, both of which are heavily supported by PyCharm. Specifically, the SciView panel in PyCharm is a comprehensive and dynamic way to view, manage, and inspect data within a data science project. It offers a great way for us to display visualizations that have been produced by Python scripts as well as to inspect the values within pandas DataFrames and NumPy arrays.

On the other hand, Jupyter notebooks are a great tool for facilitating iterative development in Python, allowing users to make incremental steps toward analyzing and extracting insights from their datasets. Jupyter notebooks are also well supported by PyCharm, being able to be edited directly inside the PyCharm editor. This allows us to skip the middle step of using a web browser to run our Jupyter notebooks while being able to utilize the powerful code-writing support features...