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Essential Python SELF-PACED

Standard Level - 30 hours (estimated completion time)
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PLEASE NOTE: This is a ONLINE SELF-PACED training.


Why Learn Python? Watch the video now! 

Python is a general purpose programming language that was designed to be compact, easy to use, easy to extend, and which has a large standard library and a very active development community. As well as being a general purpose programming language, Python is widely used as a scripting language, a glue language, for data science and machine learning, for Automated Test Equipment (ATE) control and data handling, and for software test.

Essential Python is intended for professionals working in the electronic systems hardware and embedded software development flows. Essential Python is for people who need to learn Python quickly to get a specific job done. It focusses on the most commonly used features of the Python language and teaches you all you need to know to start using Python properly and effectively.

Hands-on exercises and quizzes comprise approximately 50% of learning time and are carefully designed to reinforce learning.

Select the drop-down blocks below to find out more.

Essential Python is a hands-on programming course aimed at software, hardware, and support engineers who need to use Python for:

  • scripting development and tool flows
  • hardware verification
  • software test
  • data science and machine learning
  • 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 use Python for software test
  • How to extend Python with C
  • 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.

  • You 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; it does not teach Python as a first programming language.


If you require any of these precursor training options, please contact the Doulos team to discuss what will best suit your needs, or complete an online enquiry.

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

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

Introduction

About Python • The Python World • Which version? • Python Implementations • Jupyter Notebook • The Python Shell • Running Python Programs from a File • The Python Command Line • IDLE • The Zen of Python

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 • Tuple• Dictionaries • Sets 

Formatting

Formatting 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 ("C-style") • Exceptions • Context Manager 

Classes

Classes and Objects • Classes and Methods • Constructors • Adding Data Attributes to an Object • 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 • search • findall • Filter the Output from Another Program • sub • Basic Regular Expression Syntax

Argparse

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

Virtual Environments

Tools for Distribution and Installation • pyenv • pyenv - Multiple Versions • venv • Creating and Activating Virtual Environments • Sandboxing • pip freeze and Cloning • Version Pinning • pipenv • Running pipenv • Some pipenv commands • pipenv and Python Versions • --skip-lock and --ignore-pipfile • PyInstaller • PyInstaller Options • Further Tools

Test-Driven Development 

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 • Tests in Classes • Testing Exceptions • skip and xfail • Test Summary • Running in a Temporary Directory • monkeypatch • Methods of monkeypatch • User-defined Test Fixture • pytest Good Practice

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 • ffi.new • To Distribute cffi with PyInstaller • Cython Language • Primes in Python  • Primes in Numba • Primes in C • cffi Biuld Script • Primes in Cython • Compile with Cython • Check the Results • Compare the Speed

NumPy

The NumPy Array • A 2-D Array • More Dimensions • Initializing Arrays (1) • Initializing Arrays (2) • Arithmetic Series • Random Arrays • Copying the Shape of an Existing Array • reshape • Adding Dimensions • ravel • transpose • Sorting • Reduction Functions • Plotting a Function with Matplotlib

NumPy Broadcasting and Indexing

Elementwise Operations • Elementwise Compare • Other Elementwise Operations • Combining Array and Scalars • Broadcasting • Row and Column Vectors • Dot Product • Vectorizing a Function • Array-of-Indices • Indexing with Array-of-Booleans • Grids • Concatenate 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 • Plotting a Histogram • Plotting an Array as a Grid • Scatterplot • Numpy meshgrid • 3-D Surface Plot • Other Python Plotting Packages • Plotting with Pandas • Plotting with Seaborn • Seaborn Pairplot

Pandas

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 Concatenation • Querying a Dataframe • Adding Column to a Dataframe • reset_index, set_index, and reindex • Importing and Exporting Dataframes • Time Series and Alignment • Examining a Dataframe • Basic Statistics • Histogram • Plotting • Handling Undefined Data • Data Transformations • SQL-like Merge • Groupby • Hierarchical Index • Hierarchical Row and Column Indexes • Stack • Unstack • Pivot Table

Python 2 versus Python 3

Python 2 versus Python 3

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