duck typing Concepts

The Rust Programming Language

by Steve Klabnik and Carol Nichols · 14 Jun 2018 · 821pp · 178,631 words

. This concept—of being concerned only with the messages a value responds to rather than the value’s concrete type—is similar to the concept duck typing in dynamically typed languages: if it walks like a duck and quacks like a duck, then it must be a duck! In the implementation of

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we can call the draw method on. The advantage of using trait objects and Rust’s type system to write code similar to code using duck typing is that we never have to check whether a value implements a particular method at runtime or worry about getting errors if a value doesn

Real World Haskell

by Bryan O'Sullivan, John Goerzen, Donald Stewart and Donald Bruce Stewart · 2 Dec 2008 · 1,065pp · 229,099 words

rejects this expression as ill typed. Static typing can occasionally make it difficult to write some useful kinds of code. In languages such as Python, duck typing is common, where an object acts enough like another to be used as a substitute for it.[2] Fortunately, Haskell’s system of typeclasses, which

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, Installing HDBC and Drivers drop function, Functions over Lists and Tuples, Conditional Evaluation dropWhile function, Working with Sublists DST (Daylight Saving Time), ClockTime and CalendarTime duck typing, Static Types dynamic exceptions, Dynamic Exceptions–Error Handling in Monads E EAN-13 barcodes, A Little Bit About Barcodes easyList function, Testing with QuickCheck Either

Scala in Depth

by Tom Kleenex and Joshua Suereth · 2 Jan 2010 · 554pp · 108,035 words

’s type system. We learned how to define types and combine them. We looked at structural typing and how you can use it to emulate duck typing in dynamic languages. We learned how to create generic types using type parameters and how to enforce upper and lower bounds on types. We looked

Exploring Everyday Things with R and Ruby

by Sau Sheong Chang · 27 Jun 2012

. However, if you don’t, Ruby will automatically cast it to whatever you’re trying to use it for. This process is known as duck typing. The idea behind duck typing comes from the duck test: “if it walks like a duck, and quacks like a duck, then it is a duck.” What this

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the left, resulting in 4: x = 1 y = 2 op(x,y) => 4 So what does this mean? There are both benefits and drawbacks to duck typing. The most obvious drawback is that we have a method that is inconsistent: if we put different values into the method, we can get wildly

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in much simpler code. If you know what you’re doing, it can lead to code that is easier to read and to maintain. Ultimately, duck typing is more of a philosophy than a fixed way of coding in Ruby. If you want to ensure that the op method you defined can

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, R, Matrices distribution, normal, Money dollar sign ($), preceding R list item names, Lists doodling example, Shoes doodler–Shoes doodler double quotes (" "), enclosing Ruby strings, Strings duck typing, Ruby, Code like a duck–Code like a duck dynamic typing, Ruby, Code like a duck–Code like a duck E economics example, A Simple

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–Class methods and variables classes, Classes and objects–Classes and objects compiling from source code, Installing Ruby from source conditionals, Conditionals and loops–case expression duck typing, Code like a duck–Code like a duck dynamic typing, Code like a duck–Code like a duck external libraries for, Requiring External Libraries–Requiring

Python Web Development With Django

by Jeff Forcier

methods a string normally has), it can be considered to be of that type, even if it has extra attributes.This is referred to as “duck-typing”—if it waddles like a duck and quacks like a duck, then we can treat it as a duck. Operators As far as operators in

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QuerySets are where most of the action really happens. QuerySets are multifaceted objects, making good use of Python’s dynamic nature, flexibility, and so-called “duck typing” to provide a trio of important and powerful behaviors; they are database queries, containers, and building blocks all rolled into one. QuerySet as Database Query

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the form itself, enabling you to display them whenever and wherever you want.You can also iterate over the form itself, thanks to Python’s duck typing capabilities. Regardless of how you obtain them, each field has its own errors attribute, a list-like object whose string representation is the same unordered

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takes one explicit argument (an individual feed item) The Syndication app figures out which one you’re doing by taking intelligent advantage of Python’s “duck typing.”The Syndication app looks for these three options (where applicable) in reverse order from how they’re listed here: If it finds a one-argument

Python for Data Analysis

by Wes McKinney · 30 Dec 2011 · 752pp · 131,533 words

extensively use the functions getattr and related functions hasattr and setattr in this book, they can be used very effectively to write generic, reusable code. “Duck” typing Often you may not care about the type of an object but rather only whether it has certain methods or behavior. For example, you can

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, Removing Duplicates dsplit function, Concatenating and Splitting Arrays dstack function, Concatenating and Splitting Arrays dtype object, What Is This Book About? (see data types) “duck” typing in Python, “Duck” typing–“Duck” typing dummy variables, Computing Indicator/Dummy Variables–Computing Indicator/Dummy Variables dumps function, JSON Data duplicated method, Removing Duplicates, Removing Duplicates duplicates, Removing Duplicates–Removing

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Missing Data issubdtype function, NumPy dtype Hierarchy issubset method, Set issuperset method, Set is_monotonic method, Index Objects is_unique method, Index Objects iter function, “Duck” typing iterating over groups, Iterating Over Groups–Iterating Over Groups iterator argument, Reading and Writing Data in Text Format iterator protocol

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, “Duck” typing, Generators itertools module, itertools module–itertools module, itertools module ix_ function, Fancy Indexing J join method, Merging on Index, Merging on Index, Merging on Index,

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-reference, Variables and pass-by-reference–Variables and pass-by-reference, Dynamic references, strong types–Dynamic references, strong types, Attributes and methods–Attributes and methods, “Duck” typing–“Duck” typing, Imports–Imports, Binary operators and comparisons–Binary operators and comparisons, Strictness versus laziness–Strictness versus laziness, Mutable and immutable objects–Mutable and immutable objects, Scalar

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-reference, Variables and pass-by-reference–Variables and pass-by-reference, Dynamic references, strong types–Dynamic references, strong types, Attributes and methods–Attributes and methods, “Duck” typing–“Duck” typing, Imports–Imports, Binary operators and comparisons–Binary operators and comparisons, Strictness versus laziness–Strictness versus laziness, Mutable and immutable objects–Mutable and immutable objects attributes

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versus laziness strongly-typed language, Dynamic references, strong types–Dynamic references, strong types variables in, Variables and pass-by-reference–Variables and pass-by-reference “duck” typing, “Duck” typing–“Duck” typing sequence functions in, Built-in Sequence Functions–reversed, enumerate–enumerate, sorted–sorted, zip–zip, reversed–reversed enumerate function, enumerate–enumerate reversed function, reversed–reversed sorted

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-reference, Variables and pass-by-reference–Variables and pass-by-reference, Dynamic references, strong types–Dynamic references, strong types, Attributes and methods–Attributes and methods, “Duck” typing–“Duck” typing, Imports–Imports, Binary operators and comparisons–Binary operators and comparisons, Strictness versus laziness–Strictness versus laziness, Mutable and immutable objects–Mutable and immutable objects attributes

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in, Attributes and methods–Attributes and methods comments in, Comments–Comments “duck” typing, “Duck” typing–“Duck” typing functions in, Function and object method calls–Function and object method calls import directive, Imports–Imports indentation, Indentation, not braces–Indentation, not braces methods in

Rapid GUI Programming With Python and Qt

by Mark Summerfield · 27 Oct 2007 · 643pp · 53,639 words

1925 (bronze)" Although we have shown polymorphism using a special method, it works exactly the same for ordinary methods. Python uses dynamic typing, also called duck typing (“If it walks like a duck and it quacks like a duck, it is a duck”). This is very ﬂexible. For example, suppose we had

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, and gave an example of how to create a simple interface class. We learned more about using isinstance() for type testing, and about hasattr() and duck typing. We concluded the chapter with an overview of how Python modules and multiﬁle applications work. We also looked at the doctest module and saw how

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help minimize the introduction of bugs. Sometimes we pass complex objects to dialogs that may appear to make testing impossible. But thanks to Python’s duck typing we can always create a fake class that simulates enough behavior to be usable for testing. For example, in Chapter 12, we use a property

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of table model itself. The branch and leaf nodes have many methods in common because in some contexts, they can be used interchangeably (thanks to duck typing). class BranchNode(object): def __init__(self, name, parent=None): super(BranchNode, self).__init__(parent) self.name = name self.parent = parent self.children = [] A branch node

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ﬁeld as a string. These methods are provided for compatibility with leaf nodes to make it easier to use either kind of node based on duck typing. The __len__() method returns how many children the branch has. def childAtRow(self, row): assert 0 <= row < len(self.children) return self.children[row][NODE

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, 408, 410, 481 drill-down, 467 driver() QSqlDatabase, 449 QSqlQuery, 450 drivers, for databases, 445 dropEvent() (QWidget), 309, 315, 316 DSU (decorate, sort, undecorate), 551 duck typing, 101, 222, 497 dump() (pickle/cPickle), 248 duplicated data, avoiding, 413 dynamic attributes, 78 dynamic function deﬁnitions, 62–63 dynamic menus; see menus and QMenu

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conversions; see conversions type(), 18, 39, 102 see also isinstance() type() (QEvent), 384 type promotion; see conversions type testing, 94 TypeError exception, 56 typing; see duck typing, dynamic typing, and static typing 624 U UDP (User Datagram Protocol), 522 .ui (user interface ﬁle), 206, 207, 515 ultimate base class; see object type

Programming Rust: Fast, Safe Systems Development

by Jim Blandy and Jason Orendorff · 21 Nov 2017 · 1,331pp · 183,137 words

way naturally: a function can operate on any value that has the properties and methods the function will need. (This is the characteristic often called duck typing: if it quacks like a duck, it’s a duck.) But it’s exactly this flexibility that makes it so difficult for those languages to

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trouble? Why didn’t Rust’s designers make the generics more like C++ templates, where the constraints are left implicit in the code, à la “duck typing?” One advantage of Rust’s approach is forward compatibility of generic code. You can change the implementation of a public generic function or method, and

Programming Scala

by Unknown · 2 Jan 2010 · 448pp · 71,301 words

and “Dependency Injection in Scala: The Cake Pattern” on page 334. Structural Types You can think of structural types as a type-safe approach to duck typing, the popular name for the way method resolution works in dynamically typed languages. In Ruby, for example, when you write starFighter.shootWeapons, the runtime looks

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is written in the generalpurpose language and parsed just like any other code. An external DSL is a language with its own grammar and parser. Duck Typing A term used in languages with dynamic typing for the way method resolution works. As long as an object accepts a method call (message send

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any matching types need to share a common parent trait or class with the structural type. Hence, structural types are a type-safe analog to duck typing in dynamically typed languages, like Ruby. Subtype A synonym for derived type. Supertype A synonym for parent type. Symbol An interned string. Literal symbols are

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-defined types, 223 infix operator notation, 223 payroll API, 219–222 payroll internal DSL, 222 payroll rules DSL implementation, 224– 229 internal versus external, 244 duck typing, 283 defined, 397 dynamic typing, 397 versus static typing, 2 dynamically typed languages, 2 E eager matching, 64 EBNF (Extended Backus-Naur Form) grammar notation

Scala in Action

by Nilanjan Raychaudhuri · 27 Mar 2012

structure, not by their names, as with other typing. If you’ve used dynamically typed languages, a structural type may give you the feel of duck typing (a style of dynamic typing) in a type-safe manner. Let’s say you want to close any resource after use as long as it

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{ def salary: BigDecimal } trait Worker extends SalariedWorker { def bonusPercentage: Double } trait HourlyWorker extends SalariedWorker { def hours: Int } def amountPaidAsSalary(workers: Vector[SalariedWorker]) = { ... } The benefit of duck typing is that it lets you abstract out commonalities without being part of the same type. Using a structural type you can easily rewrite a function

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Handles dependency using trait mixins and abstract members. Structural typing Uses structural typing to manage dependencies. The Scala structural typing feature provides duck typing[a] in a type-safe manner. Duck typing is a style of dynamic typing in which the object’s current behavior is determined by the methods and properties currently associated

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of the techniques mentioned here will be home-grown. I show you how to use a DI framework in your Scala project. a Duck typing, http://en.wikipedia.org/wiki/Duck_typing. These techniques can help to write more testable code and provide a scalable solution in Scala. Let’s take our favorite CalculatePriceService