Pragmatic Python Programming

Outline

Chapter 1. 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
• Summary

Chapter 2. 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
• Summary

Chapter 3. 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
• Summary

Chapter 4. Using Operating System Functionality in Python

• Interfacing with OS-Level Functionality in Python
• The os Module
• Interfacing with Files and Directories (1 of 2)
• Interfacing with Files and Directories (2 of 2)
• The os.path Module
• Process Management
• System Information
• The sys Module Overview (1 of 2)
• The sys Module Overview (2 of 2)
• Summary

Chapter 5. 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
• Summary

Chapter 6. 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
• Summary

Chapter 7. 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
• Summary

Chapter 8. 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
• Summary

Chapter 9. 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
• Summary

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