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.

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Current Title:

Small book image
Hands-On Application Development with PyCharm
Quan Nguyen
Sep, 2019 | 494 pages
Small book image
Hands-On Application Development with PyCharm
Quan Nguyen | Bruce M Van Horn II
Oct, 2023 | 652 pages
Table of Contents (23 chapters)
Preface
Preface
Who this book is for
What this book covers
To get the most out of this book
Get in touch
Free Chapter
1
Section 1: The Basics of PyCharm
Section 1: The Basics of PyCharm
2
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
Summary
Questions
Further reading
3
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
Summary
Questions
Further reading
4
Section 2: Improving Your Productivity
Section 2: Improving Your Productivity
5
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
Summary
Questions
Further reading
6
Editing and Formatting with Ease in PyCharm
Editing and Formatting with Ease in PyCharm
Technical requirements
Code inspection
Customizable code completion support
Refactoring
A dynamic approach to documentation
Summary
Questions
Further reading
7
Version Control with Git in PyCharm
Version Control with Git in PyCharm
Technical requirements
Version control and Git essentials
Git and GitHub
Version control in PyCharm
Summary
Questions
Further reading
8
Seamless Testing, Debugging, and Profiling
Seamless Testing, Debugging, and Profiling
Technical requirements
Testing
Debugging
Profiling
Summary
Questions
Further reading
9
Section 3: Web Development in PyCharm
Section 3: Web Development in PyCharm
10
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
Summary
Questions
Further reading
11
Integrating Django in PyCharm
Integrating Django in PyCharm
Technical requirements
An overview of Django
Django in PyCharm
Summary
Questions
Further reading
12
Understanding Database Management with PyCharm
Understanding Database Management with PyCharm
Technical requirements
Connecting to a data source
Working with a database in PyCharm
Summary
Questions
Further reading
13
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
Summary
Questions
Further reading
14
Section 4: Data Science with PyCharm
Section 4: Data Science with PyCharm
15
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
Summary
Questions
Further reading
16
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
Summary
Questions
Further reading
17
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
Summary
Questions
Further reading
18
Section 5: Plugins and Conclusion
Section 5: Plugins and Conclusion
19
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
Summary
Questions
Further reading
20
Future Developments
Future Developments
Technical requirements
Miscellaneous topics in PyCharm
Taking a step back
Moving forward with PyCharm
Summary
Questions
Further reading
21
Assessments
Assessments
Chapter 1
Chapter 2
Chapter 3
Chapter 4
Chapter 5
Chapter 6
Chapter 7
Chapter 8
Chapter 9
Chapter 10
Chapter 11
Chapter 12
Chapter 13
Chapter 14
Chapter 15
22
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Dynamic Data Viewing with SciView and Jupyter

This chapter walks you through two of the most important functionalities of PyCharm in the context of data science projects—the SciView panel and Jupyter notebooks. Both of these functionalities offer a great interface so that we can view and work with the data and variables we have in a given data science project.

First, we will discuss the process of using the SciView panel, another PyCharm-specific panel or window tool, to inspect common data science-related data structures such as NumPy arrays and Pandas DataFrames. We will then learn about the integration of interactive Python computing tools such as Jupyter notebooks in PyCharm and how to use them in our own projects.

The following topics will be covered in this chapter:

  • Viewing and interacting with data via the SciView panel
  • Understanding the integration of Interactive...
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