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Modern Python Solutions - Part 2 [Video]

By Steven F. Lott

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Basics of Classes and Objects
- The Course Overview
- Using a Class to Encapsulate Data and Processing
- Designing Classes with Lots of Processing
- Designing Classes with Little Unique Processing
- Optimizing Small Objects with _slots_
- Using More Sophisticated Collections
- Extending a Collection
- Using Properties for Lazy Attributes
- Using Settable Properties to Update Eager Attributes
More Advanced Class Design
- Choosing Between Inheritance and Extension
- Separating Concerns via Multiple Inheritance
- Leveraging Python's Duck Typing
- Managing Global and Singleton Objects
- Using More Complex Structures
- Creating a Class that Has Orderable Object
- Defining an Ordered Collection
- Deleting from a List of Mappings
Functional and Reactive Programming Features
- Writing Generator Functions with the Yield Statement
- Using Stacked Generator Expression
- Applying Transformations to a Collection
- Picking a Subset
- Summarizing a Collection
- Combining Map and Reduce Transformations
- Implementing “There Exists” Processing
- Creating a Partial Function
- Simplifying Complex Algorithms with Immutable Data Structures
- Writing Recursive Generator Functions with the Yield from Statement
Input/Output, Physical Format, Logical Layout
- Using pathlib to Work with Filenames
- Reading and Writing Files with Context Managers
- Replacing a File While Preserving the Previous Version
- Reading Delimited Files with the CSV Module
- Reading Complex Formats Using Regular Expressions
- Reading JSON Documents
- Reading XML Documents
- Reading HTML Documents
- Upgrading CSV from DictReader to the namedtuple Reader
- Upgrading CSV from a DictReader to a Namespace Reader
- Using Multiple Contexts for Reading and Writing Files
Statistical Programming and Linear Regression
- Using the Built-in Statistic Library
- Average of Values in a Counter
- Computing the Coefficient of a Correlation
- Computing Regression Parameters
- Computing an Autocorrelation
- Confirming that the Data is Random – the Null Hypothesis
- Locating Outliers
- Analyzing Many Variables in One Pass