Efficient Application Development With Python3 For Beginners

Learn to code in Python from scratch with hands-on projects

What you'll learn

Implement the List and Dictionary data types to take text as input and produce a word count

Work with Python Modules to create your first web-scraping app in Python

Handle files using your Python code to build your own Python-based text editor

Programming in Python using a modular approach

Developing apps using object-oriented Python programming

Build powerful Graphical User Interfaces (GUIs)

Speed up your code with natively Python idioms

Requirements

This course doesn't assume any knowledge of Python or Python programming experience.

Description

Python is an open-source community-supported, a general-purpose programming language that, over the years, has also become one of the bastions of data science. Thanks to its flexibility and vast popularity that data analysis, visualization, and machine learning can be easily carried out with Python.With this application development course with Python 3, you'll first learn about variables, control flow statements & much more make use of them in Python programs. Then you will learn to use Python's advanced data structures such as lists and dictionaries. Then you will get a hands-on project building such as build a game that consists of a deck of playing cards, Dice-Rolling Simulator in Python, Building Architectural Marvels & much more. Moving further, you'll learn to troubleshoot your python application where you can quickly detect which lines of code are causing problems, and fix them quickly without going through 300 pages of unnecessary detail.Contents and OverviewThis training program includes 3 complete courses, carefully chosen to give you the most comprehensive training possible.The first course, Begin Python Programming in 7 Days will get you started by setting up your environment and the tools you need to start programming in Python. You will be learning about variables and operators and how to make use of them in Python programs. You will learn all about control flow statements and loops in Python and you will be using them in your programs to solve your coding problems. Then you will learn to use Python's advanced data structures such as lists and dictionaries. You will be able to organize in functions and save time coding by writing code that can be reused. Then, you will learn about Python modules and how to make use of them. On the last day, you will start interacting with files using Python code. The course will give you a strong entry point into programming in general and programming in Python in particular.The second course, Python By Example explores Python basics, data structures, and algorithms. We'll build a die rolling simulator to see how to use Python dictionaries, loops, functions, and control statements. Then, we will see how we can develop dictionaries that contain other dictionaries to build complex data structures. Next, we will use a modular approach to build a game that consists of a deck of playing cards. We will use object-oriented (OOP) Python classes to do so. We will display the playing cards both in a textual form, which we create, as well as via image files. This will lead to our displaying card images in a graphical form using Python's built-in Tkinter package. In the next part, we will use multiple inheritances with OOP classes. While the Java and C# programming languages are limited to only single inheritance, Python classes can inherit from multiple classes. You will learn how to use multiple inheritances with Python. You will also build Graphical User Interfaces (GUIs). We will use Python's built-in Tkinter package and delve more deeply into GUI programming. By the end of this video tutorial, you will have built some useful utilities using Python. Python is very strong at searching directory folders, replacing words within modules, and much more. You will find these utilities useful in your everyday work as a developer.The third course, Troubleshooting Python Application Development takes you through a structured journey of performance problems that your application is likely to encounter, and presents both the intuition and the solution to these issues. You'll get things done, without a lengthy detour into how Python is implemented or computational theory. Quickly detect which lines of code are causing problems, and fix them quickly without going through 300 pages of unnecessary detail.About the Authors:Colibri Digital is a technology consultancy company founded in 2015 by James Cross and Ingrid Funie. The company works to help its clients navigate the rapidly changing and complex world of emerging technologies, with deep expertise in areas such as big data, data science, machine learning, and cloud computing. Over the past few years, they have worked with some of the world's largest and most prestigious companies, including a tier 1 investment bank, a leading management consultancy group, and one of the World's most popular soft drinks companies, helping each of them to make better sense of its data, and process it in more intelligent ways. The company lives by its motto: Data -> Intelligence -> Action.Rudy Lai is the founder of QuantCopy, a sales acceleration startup using AI to write sales emails to prospects. By taking in leads from your pipelines, QuantCopy researches them online and generates sales emails from that data. It also has a suite of email automation tools to schedule, send, and track email performance—key analytics that all feed back into how our AI generates content. Prior to founding QuantCopy, Rudy ran HighDimension.IO, a machine learning consultancy, where he experienced first-hand the frustrations of outbound sales and prospecting. As a founding partner, he helped startups and enterprises with High Dimension, IO's Machine-Learning-as-a-Service, allowing them to scale up data expertise in the blink of an eye.In the first part of his career, Rudy spent 5+ years in quantitative trading at leading investment banks such as Morgan Stanley. This valuable experience allowed him to witness the power of data, but also the pitfalls of automation using data science and machine learning. Quantitative trading was also a great platform to learn deeply about reinforcement learning and supervised learning topics in a commercial setting. Rudy holds a Computer Science degree from Imperial College London, where he was part of the Dean's List, and received awards such as the Deutsche Bank Artificial Intelligence prize.Burkhard is a professional software test automation designer, developer, and analyst. He has more than 18 years' professional experience working for several software companies in California, USA. He currently works as an independent Python consultant from New York. He is the author of the Python GUI Programming Recipes using PyQt5 Packt video course. He is the author of Python GUI Programming Cookbook, First and Second Edition. This book is also available as a Packt video course. He is also the author of the Python Projects Packt video course. In his professional career, he has developed advanced in-house testing frameworks written in Python 3. He has also created advanced test automation GUIs in Python, which highly increased the productivity of the software development testing team. When not dreaming in Python code, he reads programming books about design, likes to go for walks, and reads classical poetry.

Overview

Section 1: Begin Python Programming in 7 Days

Lecture 1 The Course Overview

Lecture 2 Installing Python and Code Editor

Lecture 3 Getting Familiar with Command Line

Lecture 4 Writing and Running Your First Python Program

Lecture 5 Understanding Basic Syntax

Lecture 6 Assignment Day 1

Lecture 7 Understanding Python Variables

Lecture 8 Using Variables in Code

Lecture 9 Understanding Python Operators

Lecture 10 Usage of Python Operators

Lecture 11 Assignment Day 2

Lecture 12 Introducing Control Statements

Lecture 13 Usage of Control Statements

Lecture 14 Understand Loops

Lecture 15 Use Loops in Your Python Code

Lecture 16 Assignment Day 3

Lecture 17 Introducing Python Lists

Lecture 18 List Operators, Functions, and Methods

Lecture 19 Introducing the Dictionary Data Type

Lecture 20 Dictionary Operators, Functions, and Methods

Lecture 21 Assignment Day 4

Lecture 22 Introducing Functions

Lecture 23 Usage of Functions in Your Code

Lecture 24 Understanding Scope of Variables

Lecture 25 Example Code for a Scope of Variable Demonstration

Lecture 26 Assignment Day 5

Lecture 27 Python Modules

Lecture 28 Using Third-Party Python Modules

Lecture 29 Compiling Python Files

Lecture 30 Using Python Packages

Lecture 31 Assignment Day 6

Lecture 32 Reading Text from a File

Lecture 33 Writing Text to a File

Lecture 34 Handling Exceptions

Lecture 35 Assignment Day 7

Section 2: Python By Example

Lecture 36 The Course Overview

Lecture 37 Dice Rolling Simulator

Lecture 38 Nesting Python Dictionaries

Lecture 39 Using Python Generators

Lecture 40 Iterating over

Lecture 41 Deck of Cards Game Using Textual Cards

Lecture 42 Deck of Cards Game Using Graphical Cards

Lecture 43 Correctly Sizing Image Files

Lecture 44 Playing the Game

Lecture 45 Laying the Foundation

Lecture 46 Blue Prints of Architectural Design

Lecture 47 Building Our First Building

Lecture 48 The Greatness of Software Applied

Lecture 49 Building a Graphical User Interface

Lecture 50 Adding Many Widgets

Lecture 51 Using Several Layered Notebooks

Lecture 52 Making Our GUI Pretty

Lecture 53 Searching Directories

Lecture 54 Replacing Words Within Modules

Lecture 55 Administration Tasks

Lecture 56 Making Life Easy with Automation

Section 3: Troubleshooting Python Application Development

Lecture 57 The Course Overview

Lecture 58 Measuring Time Between Two Lines of Code with timeit

Lecture 59 Figuring out Where Time Is Spent with the Profile Module

Lecture 60 More Precise Time Tracking with cProfile

Lecture 61 Looking at Memory Consumption with memory_profiler

Lecture 62 Reduce Execution Time and Memory Consumption with __slots__

Lecture 63 Use Tuples Instead of Lists When Your Data Does Not Change

Lecture 64 Save on Memory Consumption with Generators Instead of Lists

Lecture 65 When to Use Lists Instead of Generators

Lecture 66 Leveraging Itertools to Create Generator Pipelines

Lecture 67 The Problem with Using Lists to Perform Vector Calculations

Lecture 68 Using NumPy’s Arrays for More Powerful Vector Representations

Lecture 69 Rewriting Our Problem with NumPy to Speed It up 40x

Lecture 70 Fast MapReduce with NumPy Broadcasting

Lecture 71 Optimize All Calculations in One Go with numexpr

Lecture 72 The Problem of Serially Executing Web Scraping Calls

Lecture 73 Simple Asynchronous Programming with coroutines and gevent

Lecture 74 Event-Driven Concurrency with Tornado

Lecture 75 Concurrency and Futures with asyncio

Lecture 76 Getting Started with Parallel Programming

Lecture 77 Doubling the Speed of Your List Processing with Tuples

Lecture 78 Easily Speed up a Group of Processes with Pool

Lecture 79 Stop Processes from Interfering with Each Other with Locks

Lecture 80 Logging What Happens When You Have Many Processes

Lecture 81 Stop Modifying the Wrong Object Instance with Correct Object Cloning

Lecture 82 Speed Up Your OOP with namedtuples

Lecture 83 Reduce Getters and Setters with Static Methods and Properties

Lecture 84 Comparing Two Different Objects

Lecture 85 Improve Readability with Abstract Base Classes in Python

This course is for Python developers, who would like to learn the Python programming language in a hands-on way & tackle application performance problems to speed up your apps.