Course Catalog  > Programming Languages & Software Dev  > Python
WA3174

Pragmatic Python Programming (Hands-on Advanced) Training

This intensive four-day hands-on course takes the attendees on a learning path that goes well beyond the basics of Python programming and teaches the participants the best practices for using Python.
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Course Details

Duration

4 days

Prerequisites

  • Participants are expected to have some Python programming experience.
  • WA3016 Practical Python 3 Programming (Beginner) or equivalent experience.

Target Audience

  • Developers
  • Software Engineers
  • Data Analysts
Course Outline
  • Standing up Python Development Environment
    • Python IDEs and REPLs
    • VS Code vs PyCharm IDEs
    • VS Code: Debugging Perspective
    • PyCharm: Debugging Perspective
    • Python Package Managers
    • Core Pip Commands
    • The Requirements File
    • What are "Virtual Environments"?
    • Tools for Creating "Virtual Environments"
    • Creating Virtual Environments with the venv Tool
    • Activating and Deactivating Virtual Environments
  • Beyond the Basics of Python
    • PEP8
    • Hands-On Activities
    • String Formatting and Interpolation
    • Hands-On Activities
    • Common Collection Functions and Operators
    • Raw String Literals
    • Accessing Python Lists
    • Main Python List Methods
    • Joining List Elements
    • Hands-On Activities
    • Set Operations with Sets
    • Unpacking Tuples
    • Conditional Expressions (a.k.a. Ternary Operator)
    • Enumerate
    • List Comprehension
    • Dictionary Comprehension
    • Hands-On Activities
    • Zipping Lists
    • Hands-On Activities
    • Global and Local Variable Scopes
    • Python Function Parameters: "Call By Sharing"
    • Functions: Default Parameters
    • Functions: Named Parameters
    • Dealing with Arbitrary Number of Parameters
    • Keyword Function Parameters
    • Returning Multiple Values from a Function
    • Docstrings
    • A Very Basic Docstring Example of a Simple Function
    • Hands-On Activities
    • Lambda Functions in Python
    • Examples of Using Lambdas
    • Hands-On Activities
    • Lambdas in the Sorted Function
    • Closures
    • Hands-On Activities
    • Generators
    • Where to Use Generators
    • Example of a Generator
    • Generator Expressions
    • Random Numbers
    • Regular Expressions
    • The re Object Methods
    • Using Regular Expressions Examples
    • Python Collections
    • The Counter Class
    • Counter Class Example
    • Python Object De/Serialization
    • A pickle Example
    • Profiling
    • Python Built-in Profiling Capabilities
    • Example of Code Execution Profiling
  • Robust Programming Techniques
    • Defining Robust Programming
    • Assertions
    • The assert Expression in Python
    • Hands-On Activities
    • What is Unit Testing and Why Should I Care?
    • Unit Testing and Test-driven Development
    • TDD Benefits
    • Unit Testing in Python
    • Steps for Creating a Unit Test in Python
    • Running the Unit Tests
    • A Unit Test Example
    • Errors
    • The try-except-finally Construct
    • What's Wrong with this Error-Handling Code?
    • Life after an Exception
    • Assertions vs Errors (Exceptions)
    • Hands-On Activities
    • What is Logging and Why Should I Care?
    • A Simple Print Statement vs Logging
    • Logging Levels
    • The Logger Hierarchy
    • The Logging Levels
    • Setting the Logging Level
    • Configuring Logging Messages
    • Example of Using Logging
    • Logging in Python: December 9, 2021 Update
    • Hands-On Activities
  • Using Operating System Functionality in Python
    • Interfacing with OS-Level Functionality in Python
    • The os Module
    • Interfacing with Files and Directories
    • The os.path Module
    • Process Management
    • System Information
    • The sys Module Overview
  • Object Inspection and Dynamic Code Creation
    • Object Inspection
    • Object Inspection Example
    • AST, Compile, and Exec
    • Why is It Possible?
    • Example of Dynamic Code Creation and Execution
    • The eval Function
  • Introduction to NumPy
    • What is NumPy?
    • The First Take on NumPy Arrays
    • The ndarray Data Structure
    • Understanding Axes
    • Indexing Elements in a NumPy Array
    • Re-Shaping
    • Commonly Used Array Metrics
    • Commonly Used Aggregate Functions
    • Sorting Arrays
    • Vectorization
    • Vectorization Visually
    • Broadcasting
    • Broadcasting Visually
    • Filtering
    • Array Arithmetic Operations
    • Reductions: Finding the Sum of Elements by Axis
    • Array Slicing
    • 2-D Array Slicing
    • The Linear Algebra Functions
  • Introduction to pandas
    • What is pandas?
    • The Series Object
    • Accessing Values and Indexes in Series
    • The DataFrame Object
    • The DataFrame's Value Proposition
    • Creating a pandas DataFrame
    • Getting DataFrame Metrics
    • Accessing DataFrame Columns
    • Accessing DataFrame Rows
    • Accessing DataFrame Cells
    • Using iloc
    • Using loc
    • Examples of Using loc
    • Filtering Rows
    • DataFrames are Mutable via Object Reference!
    • Deleting Rows and Columns
    • Adding a New Column to a DataFrame
    • Appending / Concatenating DataFrame and Series Objects
    • Example of Appending / Concatenating DataFrames
    • Getting Descriptive Statistics of DataFrame Columns
    • Getting Descriptive Statistics of DataFrames
    • Sorting DataFrames
    • Reading From CSV Files
    • Writing to a CSV File
    • Simple Plotting with pandas
    • A Plotting Example
    • The pyplot Module
  • Data Visualization in Python
    • Why Do I Need Data Visualization?
    • Data Visualization in Python
    • Getting Started with matplotlib
    • A Basic Plot
    • Scatter Plots
    • Figures
    • Saving Figures to a File
    • Seaborn
    • Getting Started with seaborn
    • Histograms and KDE
    • Plotting Bivariate Distributions
    • Scatter Plots in seaborn
    • Pair plots in seaborn
    • Heatmaps
    • A Seaborn Scatterplot with Varying Point Sizes and Hues
  • Securing Data with Python
    • States of Digital Data
    • Protecting Sensitive Data at Rest
    • Hashing
    • Secure Hashes in Python
    • Salting
    • An Example of Creating a Secure Hash in Python
    • Hands-On Activities
    • Keyed-Hashing for Message Authentication (HMAC)
    • An HMAC Example
    • Key Stretching (Derivation)
    • Key Stretching in Python
    • The scrypt Function
    • Hands-On Activities
    • Symmetric and Asymmetric (Public) Key Encryption
    • Using the openssl Tool
  • Lab Exercises
    • Lab 1. Learning the CoLab Jupyter Notebook Environment
    • Lab 2. Standing Up Python Development Environments
    • Lab 3. Beyond the Basics of Python
    • Lab 4. Robust Programming
    • Lab 5. Using Operating System Functionality in Python
    • Lab 6. Object Inspection and Dynamic Code Creation and Execution
    • Lab 7. Programming with NumPy
    • Lab 8. Programming with pandas
    • Lab 9. Data Visualization using the seaborn-pandas Link
    • Lab 10. Securing Data with Python
    • Lab 11. Data Encryption with the openssl Tool (for Review)
    • Lab 12. Keyed-Hashing for Message Authentication Project
    • Lab 13. The Logging Project
    • Lab 14. The pandas Project
    • Lab 15. The Dynamic Object Creation Project