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Hands-On Application Development with PyCharm

Hands-On Application Development with PyCharm

By : Bruce M. Van Horn II, Quan Nguyen
4.8 (4)
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Hands-On Application Development with PyCharm

Hands-On Application Development with PyCharm

4.8 (4)
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)
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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

Working with datasets

Datasets are the backbone of any data science project. With a good, well-structured dataset, we have the opportunity to explore, ideate, and discover important insights from the data. The terms good and well-structured are key. In the real world, this rarely happens by accident. I am the lead developer on a project that does data science every day. We ingest diagnostic, utilization, and performance data from various hardware platforms such as storage arrays, switches, virtualization nodes (such as VMware), backup devices, and more. We collect it for the entire enterprise; every device in every data center. Our software then turns that raw data into visualizations that provide insights, allowing organizations to effectively manage their IT estate through consolidating health monitoring, utilization and performance reporting, and capacity planning.

I’ve been at it for 10 years now and we’re always looking to support new devices and systems. Our...

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