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

Machine learning-based insights

Unlike the previous analysis methods, the methods discussed in this subsection and other similar ones are based on more complex mathematical models and ML algorithms. Given the scope of this book, we will not be going into the specific theoretical details for these models, but it’s still worth seeing some of them in action by applying them to our dataset.

First, let’s consider the feature correlation matrix for our dataset. As the name suggests, this model is a matrix (a 2D table) that contains the correlation between each pair of numerical attributes (or features) within our dataset. A correlation between two features is a real number between -1 and 1, indicating the magnitude and direction of the correlation. The higher the value, the more correlated the two features are.

To obtain the feature correlation matrix from a pandas DataFrame, we must call the corr() method, as shown here:

corr_matrix = combined_user_df.corr()

We...