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Modern Python Solutions - Part 2

Video Description

The latest in modern Python recipes for the busy programmer

About This Video

  • Develop succinct, expressive programs in Python

  • Get to know the best practices and common idioms through carefully explained and structured videos

  • Discover new ways to apply Python for the new age of development

  • In Detail

    Python is the preferred choice of developers, engineers, data scientists, and hobbyists everywhere. It is a great scripting language that can power your applications and provide great speed, safety, and scalability. By exposing Python as a series of simple recipes, you can gain insight into specific language features in a particular context. Having a tangible context helps make the language or standard library features easier to understand. This video comes with over 100 recipes on the latest version of Python.

    The videos will touch on all the necessary Python concepts related to data structures, OOP, functional programming, as well as statistical programming. You will get acquainted with the nuances of Python syntax and how to effectively use the advantages that it offers.

    You will be armed with the knowledge of creating applications with flexible logging, powerful configuration, and command-line options, automated unit tests, and good documentation.You will end the book equipped with the knowledge of testing, web services, and configuration and application integration tips and tricks.

    Table of Contents

    1. Chapter 1 : Basics of Classes and Objects
      1. The Course Overview 00:03:39
      2. Using a Class to Encapsulate Data and Processing 00:15:43
      3. Designing Classes with Lotsof Processing 00:09:56
      4. Designing Classes with Little Unique Processing 00:08:10
      5. Optimizing Small Objects with _slots_ 00:05:14
      6. Using More Sophisticated Collections 00:07:32
      7. Extending a Collection 00:05:39
      8. Using Properties for Lazy Attributes 00:05:54
      9. Using Settable Properties to Update Eager Attributes 00:08:07
    2. Chapter 2 : More Advanced Class Design
      1. Choosing Between Inheritance and Extension 00:12:52
      2. Separating Concerns via Multiple Inheritance 00:07:10
      3. Leveraging Python's Duck Typing 00:05:10
      4. Managing Global and Singleton Objects 00:09:03
      5. Using more Complex Structures 00:04:06
      6. Creating a Class that Has Orderable Object 00:07:55
      7. Defining an Ordered Collection 00:06:49
      8. Deleting from a List of Mappings 00:08:53
    3. Chapter 3 : Functional and Reactive Programming Features
      1. Writing Generator Functions with the Yield Statement 00:11:23
      2. Using Stacked Generator Expression 00:11:51
      3. Applying Transformations to a Collection 00:04:54
      4. Picking a Subset 00:06:22
      5. Summarizing a Collection 00:05:02
      6. Combining Map and Reduce Transformations 00:08:50
      7. Implementing “There Exists” Processing 00:06:44
      8. Creating a Partial Function 00:06:35
      9. Simplifying Complex Algorithms with Immutable Data Structures 00:09:45
      10. Writing Recursive Generator Functions with the Yield from Statement 00:06:07
    4. Chapter 4 : Input/Output, Physical Format, Logical Layout
      1. Using pathlib to Work with Filenames 00:13:02
      2. Reading and Writing Files with Context Managers 00:07:45
      3. Replacing a File While Preserving the Previous Version 00:06:09
      4. Reading Delimited Files with the CSV Module 00:05:56
      5. Reading Complex Formats Using Regular Expressions 00:03:04
      6. Reading JSON Documents 00:13:56
      7. Reading XML Documents 00:06:45
      8. Reading HTML Documents 00:06:49
      9. Upgrading CSV from DictReader to the namedtuple Reader 00:05:55
      10. Upgrading CSV from a DictReader to a Namespace Reader 00:05:01
      11. Using Multiple Contexts for Reading and Writing Files 00:05:41
    5. Chapter 5 : Statistical Programming and Linear Regression
      1. Using the Built-in Statistic Library 00:09:57
      2. Average of Values in a Counter 00:07:21
      3. Computing the Coefficient of a Correlation 00:04:23
      4. Computing Regression Parameters 00:06:17
      5. Computing an Autocorrelation 00:08:29
      6. Confirming that the Data is Random – the Null Hypothesis 00:10:31
      7. Locating Outliers 00:10:02
      8. Analyzing Many Variables in One Pass 00:09:06