Mastering Python 3 Programming

Get acquainted with the concepts of Python 3.x programming to enhance the performance of your code

What you'll learn

Get hands-on experience developing various kinds of Python applications on different platforms, architectures, and tools

Build four real-world applications: a stock portfolio, a mortgage refinance analysis tool, an email automation system, and a database-driven web app

Create Graphical User Interfaces for desktop and mobile applications

Know how to create HTTP-based microservices to build efficient and flexible server architectures

Learn lambda expressions, generators, and iterators to speed up your code

Gain a solid understanding of multiprocessing and multithreading in Python for parallelism

Optimize performance and efficiency by leveraging NumPy, SciPy, and Cython for numerical computations

Load large data using Dask in a distributed setting

Learn reactive programming in Python

Requirements

Basic Python programming knowledge is required.

Description

Python is an easy to learn, powerful programming language. It’s elegant syntax and dynamic typing, together with its interpreted nature, makes it an ideal language for scripting and rapid application development in many areas and on most platforms. If you're a developer who wishes to build a strong programming foundation with this simple yet powerful programming language Python, then this learning path is for you.This practical course is designed to teach you the programming aspects of Python 3.x and use them to build powerful applications. You will begin with exploring the new features of this version and build multiple projects to get hold of the topic. You will learn about event-driven, reactive programming, error handling, asynchronous programming, decorators and non-type annotations, descriptors and distributed computing in Python. You will also build high-performance, concurrent applications in Python and also work with some of the powerful libraries such as NumPy and SciPy. Next, you will perform large-scale computations using Dask and implement distributed applications in Python. Finally, you will learn reactive programming with Python to construct robust and responsive applications.By the end of this course you will be well-versed with the programming concepts in Python 3.x to build Python applications in a better and efficient manner.Meet Your Expert(s):We have the best work of the following esteemed author(s) to ensure that your learning journey is smooth:Matthew Macarty has taught graduate and undergraduate business school students for over 15 years and currently teaches at Bentley University. He has taught courses in statistics, quantitative methods, information systems and database design.Daniel Arbuckle holds a Doctorate in Computer Science from the University of Southern California, where he specialized in robotics and was a member of the nanotechnology lab. He now has more than ten years behind him as a consultant, during which time he’s been using Python to help an assortment of businesses, from clothing manufacturers to crowdsourcing platforms. Python has been his primary development language since he was in High School. He’s also an award-winning teacher of programming and computer science.Mohammed Kashif works as a Data Scientist at Nineleaps, India, dealing mostly with graph data analysis. Prior to this, he was working as a Python developer at Qualcomm. He completed his Master's degree in computer science from IIIT Delhi, with specialization in data engineering. His areas of interest include recommender systems, NLP, and graph analytics. In his spare time, he likes to solve questions on StackOverflow and help debug other people out of their misery. He is also an experienced teaching assistant with a demonstrated history of working in the higher-education industry.

Overview

Section 1: Real World Projects in Python 3.x

Lecture 1 The Course Overview

Lecture 2 Setting up the Python Environment

Lecture 3 Getting Started with the pandas_datareader

Lecture 4 Expanding to a List of Symbols

Lecture 5 Adding an Option Menu

Lecture 6 Implementing A Menu

Lecture 7 Defining Functions

Lecture 8 Defining More Functions

Lecture 9 Wrapping Up

Lecture 10 Working with Graphical User Interface (GUI)

Lecture 11 Assigning Events

Lecture 12 Setting Up the Refinance App

Lecture 13 Adding User Input

Lecture 14 Calculating Payments

Lecture 15 Adding Comparison Controls

Lecture 16 Evaluation Function

Lecture 17 Using Python to Send Email

Lecture 18 Working with External Files

Lecture 19 Working with Excel Spreadsheets

Lecture 20 Setting up the Email App

Lecture 21 Reading and Deleting Contacts

Lecture 22 Adding Contacts

Lecture 23 Completing the Email Functionality

Lecture 24 Setting Up the Environment

Lecture 25 Adding an App to the website

Lecture 26 Defining the Model

Lecture 27 Administrating the model

Lecture 28 Creating the Homepage

Lecture 29 Creating the Quotes Page

Section 2: Mastering Python 3.x

Lecture 30 The Course Overview

Lecture 31 Installing Python

Lecture 32 Using the Command Line Tools

Lecture 33 Introducing Kivy and Kv

Lecture 34 Responding to User Actions

Lecture 35 Properties and Basic Reactive Programming

Lecture 36 ReactiveX for More Advanced Reactive Programming

Lecture 37 Writing Our Oware Client

Lecture 38 Introducing Async IO and Coroutines

Lecture 39 Creating an HTTP Microservice with asyncio and aiohttp

Lecture 40 Using ReactiveX Together with asyncio

Lecture 41 Writing Our Oware Server

Lecture 42 Using Type Annotations to Make Our Code More Bug-Resistant

Lecture 43 Using Tests to Find Bugs, and Keep Them from Coming Back

Lecture 44 Test-Driven Development

Lecture 45 Hardening Our Oware Code

Lecture 46 Using Concurrent.futures to Launch and Manage Worker Processes

Lecture 47 Using Multiprocessing to Handle Lower Level Multi-process Concurrency

Lecture 48 Using Subprocess to Handle Very Low Level Multi-process Concurrency

Lecture 49 Optimizing Inter-Process Communication with __getstate__ and __setstate__

Lecture 50 Decorators on Functions and Classes

Lecture 51 Non-Type Annotations as Metadata on Functions and Parameters

Lecture 52 Descriptors to Control Attribute Access

Lecture 53 Context Managers for Active Scopes and RAII

Lecture 54 Distributing Applications in ZipApp Format

Lecture 55 Distributing Libraries in Wheel Format

Lecture 56 Distributing Programs Using PyInstaller

Lecture 57 Compiling Python Using Cython

Section 3: High-Performance Computing with Python 3.x

Lecture 58 The Course Overview

Lecture 59 Exploring Python Datatypes

Lecture 60 Using Lambda Expressions

Lecture 61 Comprehensions for Speedups

Lecture 62 Generators and Iterators

Lecture 63 Using Decorators for Time Analysis

Lecture 64 Introduction to the Threading Module

Lecture 65 Using Threads with Locks

Lecture 66 Global Interpreter Lock

Lecture 67 Multiprocessing in Python

Lecture 68 Using a Pool of Workers

Lecture 69 Introduction to NumPy

Lecture 70 Exploring NumPy Arrays

Lecture 71 Indexing in NumPy Arrays

Lecture 72 Operations and Broadcasting on NumPy Arrays

Lecture 73 Performance Comparison of NumPy Arrays

Lecture 74 Combining SciPy with NumPy

Lecture 75 Introduction to Cython

Lecture 76 Implement a Program Using Cython

Lecture 77 Time Analysis of a Cython Program

Lecture 78 Cython Data Types

Lecture 79 Using Cython Functions

Lecture 80 Combining NumPy and Cython

Lecture 81 Introduction to Numba

Lecture 82 Setting Up Numba

Lecture 83 Creating Your First Program with Numba

Lecture 84 Digging Deeper into Numba

Lecture 85 Threading Using Numba

Lecture 86 Performance Comparison with Numba

Lecture 87 Introduction to Synchronous Programming

Lecture 88 Understanding Asynchronous Programming

Lecture 89 Asynchronous Programming in Python

Lecture 90 Distributed Systems Architecture

Lecture 91 Introduction to Dask

Lecture 92 Setting Up Dask

Lecture 93 Blocked Algorithms and Dask Arrays

Lecture 94 Writing Your First Program Using Dask

Lecture 95 Using @delayed to Parallelize Code

Lecture 96 Performance Comparison with Dask

Lecture 97 Introduction to Reactive Programming

Lecture 98 Observables and Observers

Lecture 99 Overview of Data Operators

Lecture 100 Reactive Programming in Python Using RxPy

Lecture 101 Using Data Operators with RxPy

This course is for Python Programmers who want to extend their skillset to scale their code and improve their code performance.