Essential Python

Essential Python is a hands-on programming course aimed at software, hardware, systems and support engineers who need to use Python for scripting development and tool flows, for hardware verification, for software test, for data science and machine learning, for automating test equipment, or for running Python on embedded devices.

• The syntax and semantics of the Python language
• Python development environments, documentation, and resources
• Details of some of the most commonly used modules in the Python Standard Library
• How to use Python as a language for scripting tool flows
• How to extend Python with C
• How to use Python for software test
• How to use the Numpy, Matplotlib, and Pandas modules for numerical computation and data science.

This course assumes you already know how to write computer programs. Delegates should have a good working knowledge of at least one programming language or hardware description language, suitable examples being C, C++, Java, Perl, VHDL, or SystemVerilog. An understanding of object-oriented programming would be beneficial, but is not absolutely essential.

This course is not suitable as an introductory course in computer programming, that is, this course does not teach Python as a first programming language. Please contact Doulos direct to discuss and assess your specific experience against the pre-requisites.

Doulos training materials are renowned for being the most comprehensive and user friendly available. Their style, content and coverage is unique, and has made them sought after resources in their own right. The materials include:

• Fully indexed class notes creating a complete reference manual
• Workbook full of practical examples and solutions to help you apply your knowledge

Introduction

What is Python? • The Python World • Python Implementations • The Python Shell • Running Python Programs From a File • The Python Command Line

Language Basics

Numbers • Strings • Type Conversions • Built-in Functions • String Index • String Slice • String Methods • Find and Replace • Splitting Strings • Simple Formatting

Control Statements

Comments • if Statements • Comparison and Boolean Operators • Conditional Expression • Operators • for Statements • break • continue • while Statements • assert Statements • Functions • global Variables • nonlocal Variables • Lines and Continuation • IDLE 

Lists, Tuples, and Dictionaries

Lists • Length, Concatenate, Repeat • Append, Insert, Pop, Extend, Remove • Index • Loops and Lists • Sorting Lists • List Comparison • Tuples • Dictionaries • Sets 

Formatting

F-Strings • Field Width, Justification, Padding • Number Base, Comma, Sign • Floating Point 

Files and Exceptions

Reading Standard Input • Writing to a File • Writing Files using Print • Reading from a File • Variations • readline • Exceptions • Context Manager 

Classes

Classes • Objects • Methods • Constructors • Data Attributes • Class Variables and Instance Variables • Class vs Object vs Function vs Method • The LEGB Scope Rule • Documentation Strings 

Inheritance

Inheritance • Overriding Methods • Overriding the Constructor • Virtual Method Calls • Multiple Inheritance • Testing Class Relationships • Tying Variables to a Class • Duck Typing 

Copying Objects

Copying Instance Objects • Copying Lists • Assigning to a Slice • Shallow Copying 

Magic Methods

Defining Magic Methods • + • int and round • str and repr • Some Useful Magic Methods

Iterators and Generators

Sequence, Iterator, Iterable • Iterable Unpacking • Generators • List Comprehensions • Generator Expressions • lambda • map • filter, enumerate • zip • join • Dictionary Comprehensions 

Exploring Functions

Default and Keyword Arguments • Argument Lists • None • Type Hints • Functions as Objects • Higher-Order Functions • Decorator Pattern • A Useful Decorator • Closures 

Modules

import • from ... import • __name__ • Running Modules from the Command Line • Packages • The Python Package Index • pip 

The Standard Library

math • random • statistics • datetime • time • timeit • os • os.path • os.environ • shutil • glob • sys • subprocess 

Regular Expressions

match • Group and Groups • Character Classes • Shorthands • Anchors • Greedy versus Non-greedy • search • findall • Filter the Output from Another Program • sub

Argparse

Positional Arguments • Usage and Help • Option Arguments • Options With and Without Values • Optional Values • nargs • Description, Epilog, Help • Prefix, Choices, Required

Virtual Environments

Tools for Distribution and Installation • pyenv • venv • Creating and Activating Virtual Environments • Sandboxing • pip freeze and Cloning • Version Pinning • pipenv • PyInstaller

TDD and Pytest

What is TDD? • The TDD Process • The Four-Phase Test Pattern • Fakes and Test Doubles • Pytest Architecture • A Simple Test • A Failing Test • Test Discovery • Grouping Tests in a Class • Testing that an Exception is Raised • Skipped Tests and Expected Failures • Test Summary Report • Running in a Temporary Directory • Monkey Patch Test Fixture • User-Defined Test Fixture

Extending Python with C

Extending Python • Numba • C Foreign Function Interface • Building and Running with CFFI • Compiling from C Header and Source Files • Building from a Shared Object File • Pointers and Structures • CFFI Build Script • ffi.new • Cython • Cython Language • Comparing Numba, Cython, and cffi • Compare the Speed

NumPy

The NumPy Array • A 2-D Array • More Dimensions • Initializing Arrays • Arithmetic Series • Random Arrays • Copying the Shape of an Array • reshape • Adding Dimensions • ravel • transpose • Sorting • Reduction Functions • Plotting with Matplotlib

NumPy Broadcasting and Indexing

Elementwise Operations • Elementwise Compare • Combining Arrays and Scalars • Broadcasting • Row and Column Vectors • One-Hot Encoding • Dot Product • Vectorizing a Function • Array-of-Indices • Array-of-Booleans • Grids • linspace-like ogrid • Concatentate and Stack • Split • Tile

Matplotlib

Plotting with Lines, Colors, and Markers • Text and Legend • Matplotlib APIs • Subplots • Subplots versus Figures in pyplot API • Log Axes • Types of Plot • Histogram • Plotting an Array as a Grid • Scatterplot • Numpy meshgrid • 3-D Surface Plot • Pandas • Seaborn • Seaborn Pairplot

Pandas

Pandas Data Structures • Pandas Series • Pandas DataFrame • index and columns • Basic Indexing - Series • Basic Indexing - DataFrame • loc and iloc • Slicing and Dicing • Copying and Concatentation • Querying a Dataframe • Adding Columns to a Dataframe • reset_index, set_index, and reindex • Importing and Exporting DataFrames • Time Series and Alignment • Basic Statistics • Histogram • Plotting • Handling Undefined Data • Fill Options • Data Transformations • SQL-like Merge • Outer Merge • Groupby • Hierarchical Index • Hierarchical Row and Column Indexes • Stack • Unstack •  Pivot Table

Customized Topics (on-site, team-based training)

We can also present supplemental content to address the specific requirements of your team, for example:

• Multithreading and multiprocessing
• Python on embedded processors
• Python Coding Style and Guidelines
• Moving from Python 2 to Python 3

Please contact Doulos to discuss your specific requirements.