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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

Data cleansing and preprocessing

As I mentioned earlier, we’ve been pretty lucky. Some of the data my team works with can be downright filthy. When we use terms such as “dirty," “filthy,” and “cleansing” concerning data, what we’re talking about is addressing the format of the data, as well as the fitness of the data for processing. Data is only useful if it’s in a format we can work with. Structured data is what we always prefer.

Structured data refers to data that is split into identifiable fields. We’ve seen comma-separated and tab-separated text. Other examples of structured data include formats such as XML, JSON, Parquet, and HDF5. The first two, XML and JSON, are very common and have the advantage of being text formats. The latter two, Parquet and HDF5, are binary files and are specialized for storing larger datasets than would be comfortable when working with text. As we’ve seen, most tools, including...

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