- What are some of the main ways of collecting datasets for a data science project?
- Can Git LFS be used with Git? If so, what is the overall process?
- Which type of attribute can have their missing values filled out with the mean? What about the mode?
- What problem does one-hot encoding address? What problem can arise from using one-hot encoding?
- Which type of attribute can benefit from bar charts? What about distribution plots?
- Why is it important to consider the feature correlation matrix for a dataset?
- Aside from predictive tasks, what can we use machine learning models for (like we did in this chapter)?
Hands-On Application Development with PyCharm
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Hands-On Application Development with PyCharm
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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)
Preface
Introduction to PyCharm - the Most Popular IDE for Python
Installing and Configuring PyCharm
Section 2: Improving Your Productivity
Customizing Interpreters and Virtual Environments
Editing and Formatting with Ease in PyCharm
Version Control with Git in PyCharm
Seamless Testing, Debugging, and Profiling
Section 3: Web Development in PyCharm
Web Development with JavaScript, HTML, and CSS
Integrating Django in PyCharm
Understanding Database Management with PyCharm
Building a Web Application in PyCharm
Section 4: Data Science with PyCharm
Turning on Scientific Mode
Dynamic Data Viewing with SciView and Jupyter
Building a Data Pipeline in PyCharm
Section 5: Plugins and Conclusion
More Possibilities with PyCharm Plugins
Future Developments
Assessments
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