PyCharm is one of the most intuitive and feature-rich integrated development environments (IDEs) available for Python development. Explore some of the important features of this IDE, such as debugging with breakpoints, Python package installation, and customizing syntax highlighting, in this 11-video course. Key concepts covered here include how to install and configure the PyCharm IDE on your system; how to customize syntax highlighting for various source files in Python project and how to minimize typing errors by using the auto-complete feature. Next, learn to apply name changes to variables and functions to all their references; learn the state of an application in the middle of code execution with the use of breakpoints; and use the step into feature to get inside function calls and step over to run them in one go. Finally, learn to pause code execution at a line only under a specified condition; and learn to use the resume button to ensure that code execution only pauses at breakpoints.

This 13-video course explores how Microsoft Excel spreadsheets can be created, opened, and modified programmatically from within Python. Learners will review the Microsoft Excel object model, the attributes of the worksheet cell object which can be leveraged to create and modify workbooks programmatically. First, you will review VBA (Visual Basic for Applications) technology, before exploring how Python and its ecosystem of libraries are fast emerging as a popular choice for easy spreadsheet automation. Then you will learn how to use openpyxl (open pixel library) to manipulate Excel's object model programmatically from within Python. Continue by learning how to write spreadsheets by using openpyxl, and examining how existing Excel workbooks can be opened, as well as how new spreadsheet files can be created, and written out to disk. Finally, you will learn how Python iterators and indexing can be used to access and manipulate individual cells, ranges consisting of many cells, as well as entire rows and columns.

Learners can explore complex operations in Microsoft Excel workbooks, including the use of conditional formatting, named ranges, and merged cells, in this 17-video course. Microsoft Excel is the best prototyping tool for data analysis, an interactive functional programming environment, and a forerunner of Python. Begin by exploring how Python and its ecosystem of libraries are fast emerging as a popular choice for easy spreadsheet automation. Then observe the formatting, alignment, and other aesthetics in Python. You will work with the Python library openpyxl; examine data analysis, the use of pivot tables, and the locking of cell references by using the $ operator; and learn how to perform complex data analysis operations using pivot tables, sorting and filtering, and formulae with both absolute and relative cell references to enable efficient copy paste. You will learn to control the workbook appearance using conditional formatting and styles. Finally, this course demonstrates how to leverage the Python Pandas library to read a spreadsheet, to group and analyze data.

This course explores how to use Python's openpyxl library to build visualizations such as line, bar, and bubble charts in Excel. In its 11 videos, you will examine how Python and its ecosystem of libraries are fast emerging as a popular choice for easy spreadsheet automation, before learning how to create line and bar charts in Excel, and learning how to use Python to control several properties of those charts, including line weights and style, data for the reference axes, formatting, and the position of ticks on those axes. Learners will observe how to construct data visualizations in Excel using Python. This course then demonstrates common types of visualizations that are supported in Excel, and how to programmatically replicate those visualizations from within Python. Finally, learners will observe demonstrations of the use of bubble charts to display three dimensions on a two-dimensional chart as well as stock charts to represent the opening, high, low, and closing prices of stocks in a single data visualization for the financial markets.

Learners can explore basic concepts of Python socket programming, and how to communicate small amounts of data between Python applications by using either the same machine or over a network, in this 9-video course. Begin by learning how to use Python language to set up a communication line by creating a socket. Then learn to initialize a simple socket, and use it to transfer text data from one application to another. This course next demonstrates how to create a client app and server app in Python, and how each app uses a socket to communicate. Learners will observe a demonstration of how to transmit a Python dictionary and custom object over a socket connection. You will learn how to use a socket model to set up a simple TCP (transmission control protocol) socket to transfer text between applications. Next, learners will examine other properties of Python sockets, including its use with the context manager and the setting of a time-out for connections. Finally, you will learn to use the Pickle library to convey Python objects over a socket connection.

This 11-video course explores advanced features of Python sockets, including the transfer of large files over sockets, two-way communication, and differences between blocking and nonblocking sockets. You will learn to transfer large files over sockets by breaking them up into chunks, and to transfer images over TCP (transmission control protocol) sockets. Then you will learn how to transfer Python objects by using the pickle module. Next, learn how to create a chat application and use it to transfer several types of data from a server application to a client. Learners continue by exploring how to configure two-way communication over sockets by building a simple chat. This course examines the performance versus reliability trade-off when one uses blocking and nonblocking sockets. You will examine and compare TCP, a connection-oriented protocol, and UDP (Universal Datagram Protocol) which is connectionless. Finally, you will examine the performance versus reliability trade-off with a TCP and UDP, and why TCP is better suited for apps which require high reliability at the other end of the communication line.

Explore how the SOLID principles can help to make software designs easier to understand and maintain for Python developers. In this 14-video course, learners will examine the five SOLID principles-Single Responsibility, Open/Closed, Liskov's Substitution, Interface Segregation, and Dependency Inversion-as well as creational, structural, and behavioral design patterns. Key concepts covered here include the basic principles of good design in code; learning the Single Responsibility and Open/Closed principles of good design; and learning the Liskov's Substitution, Interface Segregation, and Dependency Inversion principles of good design. Next, learners will examine the principle of Least Knowledge and the Hollywood principle of good design; examine issues that may arise when classes do not implement the principle of Single Responsibility; and observe how to implement the principles of Single Responsibility and Open/Closed. Continue by learning how to design and implement the Liskov's Substitution principle, the Interface Segregation principle, and the Dependency Inversion principle. Finally, learners will study the three broad categories of design patterns and when to use each of them.

In this 16-video course, learners will explore the details and implementation of five commonly used creational design patterns: Singleton, Factory, Abstract Factory, Builder, and Object Pool. Key concepts covered here include how the Singleton pattern works and when to use it; how to write code for a simple implementation of the Singleton pattern; and how to implement the Singleton pattern by using a more Pythonic style and global objects in Python. Next, learn how the Factory and Abstract Factory patterns work; how to iteratively improve the design of code using refactoring; and how to design and implement the serializer with the Factory pattern. Continue by learning how to apply the Abstract Factory pattern to create a family of objects; how the Builder pattern works and how to implement a simple design for the Builder pattern; and how the Object Pool pattern works and how to implement the Object Pool pattern to limit the number of instances. Finally, learn how to improve the Object Pool pattern by making the object pool a singleton.

Explore the design and implementation of five commonly used structural Python design patterns: Adapter, Decorator, Facade, Proxy, and Flyweight. In this 14-video course, learners examine how these patterns can be used for tasks such as working with legacy components, dynamically adding responsibilities, offering a simple client interface, controlling object access, and efficiently using lightweight resources. Key concepts covered here include design of the Adapter pattern and need for the pattern when working with legacy components; learning how to write code for the Adapter pattern to offer a consistent interface to clients; and learning design of the Decorator pattern and the importance for adding responsibilities dynamically. Continue by observing how to implement the Decorator pattern to allow adding responsibilities at runtime. Next, you will learn about the design of the Façade pattern and implementing the pattern to offer a simple interface to clients; learn to design and implement the Proxy pattern to control access to an object; and learn the design of the Flyweight pattern and how to implement the pattern to efficiently use lightweight resources.

Explore the design and implementation of five commonly used behavioral design patterns: Strategy, Chain of Responsibility, Observer, Command, and Iterator. Examine how these patterns can be used in Python built-in functions, in simple and complex use cases, for performing undo operations, and with Python special methods. Key concepts covered in this course include the Strategy pattern, how to design and implement the pattern, and how it is used in Python built-in functions; and learning the Chain of Responsibility pattern and how to write code to implement the pattern. Next, you will learn about the Observer pattern and how to implement the pattern for a simple use case and how to implement the pattern for a more complex use case. Finally, learners will study the Command pattern and how to implement the pattern to perform undo operations; and learn the Iterator pattern and its applications and learn to design an Iterator by using special methods in Python.

In this practice lab, learners will be presented with a series of exercises to practice developing in Python. Exercises include tasks such implementing good design principles and writing applications that can communicate using TPC sockets. Learners will also practice working with Singleton, Observer and Factory design patterns and implementing iterators using special methods.

Learners can also use the environment as an open sandbox. No installation or configuration is required, so you can gain immediate hands-on experience. Create new files or upload your own from a storage location of your choice, such as GitHub, and you can practice coding right away! You can even download a copy of your work when you’re done.

Whether you’re looking to dive into the code presented within our courses or you want to work on your own coding projects, this lab environment will provide you with everything you need. So, go ahead and start coding today!

Final Exam: Pythonista will test your knowledge and application of the topics presented throughout the Pythonista track of the Skillsoft Aspire Python Novice to Pythonista Journey.