Book Image

Hands-On Application Development with PyCharm

By : Quan Nguyen
Book Image

Hands-On Application Development with PyCharm

By: Quan Nguyen

Overview of this book

JetBrain’s PyCharm is the most popular Integrated Development Environment (IDE) used by the Python community thanks to its numerous features that facilitate faster, more accurate, and more productive programming practices. However, the abundance of options and customizations can make PyCharm seem quite intimidating. Hands-on Application Development with PyCharm starts with PyCharm’s installation and configuration process, and systematically takes you through a number of its powerful features that can greatly improve your productivity. You’ll explore code automation, version control, graphical debugging/testing, management of virtual environments, and much more. Finally, you’ll delve into specific PyCharm features that support web development and data science, two of the fastest growing applications in Python programming. These include the integration of the Django framework as well as the extensive support for IPython and Jupyter Notebook. By the end of this PyCharm book, you will have gained extensive knowledge of the tool and be able to implement its features and make the most of its support for your projects.
Table of Contents (23 chapters)
Free Chapter
1
Section 1: The Basics of PyCharm
4
Section 2: Improving Your Productivity
9
Section 3: Web Development in PyCharm
14
Section 4: Data Science with PyCharm
18
Section 5: Plugins and Conclusion

Chapter 13

  1. To collect a dataset, you can do any of the following:
  • Download it from an external source
  • Manually collect it or use web scraping
  • Collect it via a third party
  • Work with a database
  1. Git LFS can work seamlessly with Git. Specifically, we can use Git LFS to track the extensions of large files that we want to have version control on, and Git will work with Git LFS to delegate those files when we want to push our projects to GitHub. Afterward, we simply need to use Git in the usual way.
  2. An attribute that contains continuous, numerical data often has its missing values filled out with the mean. On the other hand, attributes with discrete numerical data as well as categorical data can use the mode to fill out their missing values.
  3. In a naive encoding scheme, you may inadvertently apply some sort of an ordered relation to the data when the original data is replaced with...