Advanced Python Topics: Exceptions & Command Line Arguments

Advanced Python Topics: Exceptions & Command Line Arguments

• discover the key concepts covered in this course
• use a try and except block to handle a Python exception
• control how you handle exceptions that your code may raise
• recognize how exceptions are defined in a hierarchy and how related exceptions can be caught and handled
• define multiple except blocks to handle various exceptions that can be raised by your code
• convert code prototyped in a Jupyter notebook into a Python script that can be executed from a shell
• run your code from the Python shell and recognize how to execute single-line and multi-line commands
• use the sys and argparse module to access command line arguments to a Python script
• parse and use the arguments passed to a Python script from the command line
• define the names and other customizable features of command line arguments to a Python script using the argparse module
• summarize the key concepts covered in this course

Examine the different types of exceptions that can be raised in Python and how they can be handled using except blocks. Explore the execution of Python scripts from the command line, including the use and parsing of command line arguments.

Advanced Python Topics: File Operations in Python

Advanced Python Topics: File Operations in Python

• discover the key concepts covered in this course
• use the open function in Python to open a file for reading
• recognize the differences between the read(), readline(), and readlines() functions when working with files in Python
• recall the differences between opening a file in write mode and append mode
• distinguish between the r+ and a+ modes to read from and write to a file
• use the load and loads functions of the json module to parse JSON data
• convert Python dictionaries and lists into JSON strings and files
• identify some of the file formats that can be handled by the CSV module in Python
• convert Python dictionaries and lists into CSV files
• define a customized file format creating a CSV dialect and use that to parse and write data
• summarize the key concepts covered in this course

Python includes several functions and modules that simplify working with files, whether reading from them or writing to them. Explore operations for standard text files and handling of formatted data such as JSON and CSV using Python modules designed for such data.

Advanced Python Topics: Migrating from Python 2 to Python 3

Advanced Python Topics: Migrating from Python 2 to Python 3

• discover the key concepts covered in this course
• set up the Jupyter Notebook IDE to run and develop Python 2 code by installing a kernel for it
• recall the variations in syntax and output for various operations such as print, division, and round for Python 2 versus Python 3
• recognize the differences in the round function behavior and the different ways to accept user input in Python versions 2 and 3
• use the 2to3 conversion tool to identify the lines in your Python 2 scripts that need to be altered for Python 3 compatibility
• convert a Python 2 script to be Python 3 compatible using the 2to3 conversion tool
• summarize the key concepts covered in this course

Discover the key differences between Python 2 and Python 3, as well as some of the options available to upgrade your Python 2 apps to run on Python 3. Explore how to set up the Jupyter Notebook IDE to use a Python 2 kernel so that you can modify and run your Python 2 scripts from this interface. Next, examine some of the variations in the syntax and the output of various operations between versions 2 and 3 of Python. How to use the 2to3 conversion tool in order to transform Python 2 scripts to executable Python 3 code is also covered.

Advanced Python Topics: Python Modules & Virtual Environments

Advanced Python Topics: Python Modules & Virtual Environments

• discover the key concepts covered in this course
• use pre-built Python modules to perform common operations by importing them into your source code
• recognize the features available in popular Python libraries such as NumPy, random, and datetime
• package Python code into a tar archive for distribution to end users
• use pip to install a module in your environment and import it into your Python applications
• install the virtualenv tool and use it to create a virtual environment for Python applications
• activate and run Python scripts from within virtual environments
• install different packages in different virtual environments and recognize how they are isolated from each other
• summarize the key concepts covered in this course

Explore built-in Python modules that allow you to perform many common operations without having to write the associated code. These operations range from random number generation to frequently used math calculations. Examine the steps involved in building custom modules to package functions, properties, and classes for distribution to end users. Discover how to use the virtualenv tool to create independent virtual environments so that Python apps with conflicting dependencies can run on the same machine.

Final Exam: Python Apprentice

Final Exam: Python Apprentice

• assign attributes to objects of classes
• code a function to implement the Insertion Sort algorithm
• code a function to perform a breadth-first traversal of a graph
• control how you handle exceptions that your code may raise
• create a classes using Python
• create functions to perform common BST operations such as lookup and finding the minimum and maximum values
• define a class to represent a graph in the form of an adjacency matrix
• define a Linked List class and implement functions to insert a node at the head or the tail of the linked list
• define classes as abstract
• define multiple base classes for a single subclass
• define multiple inheritance levels in classes
• define the classes and functions required to implement a binary search tree
• describe a linked list, and its contents and structure
• describe how a sorted list of elements can be searched efficiently using a binary search
• describe how classes can be used as blueprints to create objects
• describe how the performance of operations and algorithms is expressed in terms of the size of the input
• describe the differences between the read(), readline(), and readlines() functions when working with files in Python
• describe the operations involved when sorting a list of values using the Selection Sort algorithm
• describe the steps involved in performing an Insertion Sort and compare it to the Bubble Sort
• distinguish between the r+ and a+ modes to read from and write to a file
• identify operations that run in constant time regardless of input
• identify the various properties of sorting algorithms that must be considered when selecting the right one for your data
• identify what makes a data structure and some of the purposes they serve
• implement a custom Stack class that includes functions for the common stack operations
• implement an adjacency set representation for a graph
• implement the Bubble Sort algorithm in Python
• implement the native Queue class of Python and perform the standard queue operations on it
• initialize class variables using the init special method
• invoke base class methods from subclasses
• outline the workings of the Shell Sort and recall the performance metrics of this divide and conquer algorithm
• pass arguments to initialize the state of a class object
• perform addition operations on custom objects
• perform floor division, modulo, and power-of operations
• perform subtraction operations on custom objects
• recall how class variables are different from instance variables
• recall how state and behavior can be encapsulated in a single unit
• recall the default base class for all Python classes
• recall the different ways in which to traverse a BST and describe the method to perform a breadth-first traversal
• recall the metrics on which algorithms and operations on data are evaluated
• recognize how exceptions are defined in a hierarchy and how related exceptions can be caught and handled
• recognize how the performance of operations and algorithms is expressed in terms of the size of the input
• recognize the differences between the read(), readline(), and readlines() functions when working with files in Python
• recognize the features available in popular Python libraries such as NumPy, random, and datetime
• recognize what trees and binary trees are and recall the properties of a binary search tree
• represent objects using customized strings
• run your code from the Python shell and recognize how to execute single-line and multi-line commands
• set up the Jupyter Notebook IDE to run and develop Python 2 code by installing a kernel for it
• summarize how insert and lookup operations occur in a BST
• summarize the pre-order and in-order depth-first traversal techniques for a BST
• use a try and except block to handle a Python exception
• use class hierarchies
• use pip to install a module in your environment and import it into your Python applications
• use pre-built Python modules to perform common operations by importing them into your source code
• use the 2to3 conversion tool to identify the lines in your Python 2 scripts that need to be altered for Python 3 compatibility
• use the native Queue class of Python and perform the standard queue operations on it
• use the open function in Python to open a file for reading
• work with class hierarchies
• work with utility methods on classes
• write the code to implement a Selection Sort
• write the partition and Quicksort functions to implement a Quicksort

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