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.

Preface

Who this book is for

What this book covers

To get the most out of this book

Free Chapter

Section 1: The Basics of PyCharm

Section 1: The Basics of PyCharm

Introduction to PyCharm - the Most Popular IDE for Python

Introduction to PyCharm - the Most Popular IDE for Python

The background of PyCharm

PyCharm – an essential part of the Python community

Differentiating PyCharm from other editors/IDEs

Understanding the Professional and Community editions

Further reading

Installing and Configuring PyCharm

Installing and Configuring PyCharm

Technical requirements

Downloading, installing, and registration

Setting up PyCharm

Getting started with PyCharm projects

Getting the source code from GitHub

Further reading

Section 2: Improving Your Productivity

Section 2: Improving Your Productivity

Customizing Interpreters and Virtual Environments

Customizing Interpreters and Virtual Environments

Technical requirements

Customizing the PyCharm workspace

Creating a PyCharm project – revisited

Virtual environments and interpreters

Importing an external project into PyCharm

Further reading

Editing and Formatting with Ease in PyCharm

Editing and Formatting with Ease in PyCharm

Technical requirements

Code inspection

Customizable code completion support

A dynamic approach to documentation

Further reading

Version Control with Git in PyCharm

Version Control with Git in PyCharm

Technical requirements

Version control and Git essentials

Version control in PyCharm

Further reading

Seamless Testing, Debugging, and Profiling

Seamless Testing, Debugging, and Profiling

Technical requirements

Further reading

Section 3: Web Development in PyCharm

Section 3: Web Development in PyCharm

Web Development with JavaScript, HTML, and CSS

Web Development with JavaScript, HTML, and CSS

Technical requirements

Introduction to JavaScript, HTML, and CSS

Implementing web pages in PyCharm

Further reading

Integrating Django in PyCharm

Integrating Django in PyCharm

Technical requirements

An overview of Django

Django in PyCharm

Further reading

Understanding Database Management with PyCharm

Understanding Database Management with PyCharm

Technical requirements

Connecting to a data source

Working with a database in PyCharm

Further reading

Building a Web Application in PyCharm

Building a Web Application in PyCharm

Technical requirements

Starting a web project in PyCharm

Forms and emails

Deploying your web project

Further reading

Section 4: Data Science with PyCharm

Section 4: Data Science with PyCharm

Turning on Scientific Mode

Turning on Scientific Mode

Technical requirements

Starting a scientific project in PyCharm

Understanding the advanced features of PyCharm's scientific projects

Further reading

Dynamic Data Viewing with SciView and Jupyter

Dynamic Data Viewing with SciView and Jupyter

Technical requirements

Data viewing made easy with PyCharm's SciView

Understanding IPython and magic commands

Leveraging Jupyter notebooks

Further reading

Building a Data Pipeline in PyCharm

Building a Data Pipeline in PyCharm

Technical requirements

Working with datasets

Data cleaning and pre-processing

Data analysis and insights

Scripts versus notebooks in data science

Further reading

Section 5: Plugins and Conclusion

Section 5: Plugins and Conclusion

More Possibilities with PyCharm Plugins

More Possibilities with PyCharm Plugins

Technical requirements

Exploring PyCharm plugins

Best plugins to use for your PyCharm projects

Advanced plugin-related options

Further reading

Future Developments

Future Developments

Technical requirements

Miscellaneous topics in PyCharm

Taking a step back

Moving forward with PyCharm

Further reading

Assessments

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Section 4: Data Science with PyCharm

This section starts with Chapter 11, Turning on Scientific Mode. Data science is arguably the field that has made Python increasingly popular in the programming community in the past few years (and the trend is projected to continue further). This is why a focus on data science and scientific computing tools is necessary for any good editor/IDE for Python programming. This section is therefore dedicated to the analysis of PyCharm's support for various scientific computing tasks.

The chapters in this section will discuss the specifics of SciView, the unique view in PyCharm that facilitates scientific computing practices. The integration of widely used scientific tools such as NumPy, pandas, IPython, and Jupyter Notebook will also be discussed in detail. At the end of the final chapter, we will analyze the process of building a complete...