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

Data cleaning and pre-processing

In this section, we will attempt to clean and pre-process the dataset in our current project. This process can also be called exploratory data analysis. In general, the term exploratory data analysis denotes the process of exploring and analyzing a dataset at the same time.

As we have said before, in an iterative development process with data, we need to take incremental steps to learn about the specifics of a dataset and, from there, know how to analyze it better.

For example, a dataset attribute that contains continuous numerical values (such as length or area) should be handled differently than a discrete attribute (such as age or number of siblings) or even categorical data (such as city, country, or gender). In this case, we will apply various cleaning and pre-processing techniques to the attributes in our dataset per their data types.

Data...