Skip to main content

Python metaclass for making named tuples with programmability

Project description

Adding the programmability of normal classes to named tuples

In essence, the programmable tuple base in this module is able to make user-defined classes in Python has got the immutability of named tuple, while retaining the programmability of user-defined classes at the same time. Merely minimal change to the code for class definition is needed, and a lot features for normal classes definition, like methods and inheritance, are supported.

The basic motivation for this is to make code more secure and less error-prone for objects that does not frequently need to be mutated during its life time, especially when we do not want to relinquish the extensibility and programmability of normal classes by changing to use the austere tuples.

Basic usage


The programmable tuple is directly modelled after the named tuple class in the standard library. So unlike plain user-defined classes with an extensible __dict__, the instances could only have a set of pre-defined fields for each class. Since instances cannot be changed after the initialization, all the information about an instance needs to be given to the initializer. So the arguments to the initializer uniquely define values of the programmable tuple. Hence they are called the defining fields of the class. Besides the defining fields, additional fields can be added to the class instances to hold some other essential data. These fields are going to be termed the data fields. This can be achieved by assigning a list of names to the __data_fields__ attribute of the class, in the same way as the __slots__ attribute is used. And the actual value for the data fields can be set in the initializer in the same way as normal. For example, to define an programmable tuple for people to store their first and last name, and we would like the instances to carry the full name with comma separation for alphabetization, we can just define

class Person(ProgrammableTuple):
    __data_fields__ = ['full_name']
    def __init__(self, first_name, last_name):
        self.full_name = ', '.join([last_name, first_name])

Then in this way, if we make an instance by running Person('John', 'Smith'), the values of all the fields, defining fields and data fields, can all be able to be retrieved by using the dot notation, like p.full_name. Note that if some fields are desired to be hold private, the same underscore convention of python could be used. Just it is not advised to keep defining attributes private.

For the fields, there are two keyword arguments that can be used for the class creation. The auto_defining argument, which is True by default, controls the automatic assignment of the defining fields to the self object in the initializer before the actual invocation of the user-defined initializer. For fields that is not explicitly given a value in the initializer, default_attr argument can be set to a function that returns the default value to set when given the name of the field as a string.

Note that although there is no compulsory requirement that the values set to the defining fields should match the argument that is given to the initializer, it is advised that at least the defining fields can be used to reproduce the object. For instance, for a class named A with fields a and b, it is a good practice to keep A(spam.a, spam.b) == spam for any instance spam of the class A, while spam.a does not need to match the argument a that was used for creating spam. Frequently the argument will accept a wide range of types for the argument, but a specific form is going to be stored as the attribute. This form can be termed the canonical form for that argument. For example, the initializer could allow any iterator for a defining field, but it is better to cast it to a tuple to be stored in the immutable object. Then the tuple form of the elements is the canonical form of that argument. It does not need to match that actual argument used for its creation but it is always able to reproduce the value. For cases where most of the defining fields are just taken to be the value from the argument, the auto_defining option can be set to True to save the lines of code. But for cases where almost all arguments need to be cast and specifically assigned, that option can be turned off to save of overhead of the automatic assignments.


Methods can also be defined for programmable tuples with exactly the same syntax as the normal user-defined classes. Just here the only place where self could be mutated is in the __init__ method, any attempt to mutate self would cause an error in any other method. So the methods here should be ones that concentrates more on the return value rather than mutating the state of the object. Due to this apparent deviation from the classical Smalltalk-style object-orientated programming, the methods normally could be clearly defined outside the class as a normal function, and then then we can forward them into the class for convenience. For instance, if we have got a class for symbolic mathematical expressions and a function to compute the derivative with respect to a symbol, we could do

def diff_expr(expr, symb):
    """Compute the derivative w.r.t. a symbol"""
    ... ...

class Expr(ProgrammableTuple):
    ... ...
    diff = diff_expr
    ... ...

In this way, to differentiate an expression e with respect to a symbol x, we could do both e.diff(x) and diff_expr(e, x). It only needs to be noted that for functions that is intended to be used as a method as well, the argument to be used as self needs to be put in the first slot. Of course methods can be kept in the class only as normal if it is desirable.

Non-destructive update

Frequently we need values of user-defined class that is different from an existing value by relatively small amount. With mutable class, frequently this is achieved by mutating the instance. However, here the instances are no longer mutable. So methods to update instances non-destructively are provided. Note that these methods will return new instances with the field updated and leave the original value intact, in the same way as the Haskell records works.

Basically two methods are provided for this purpose, _update and _replace. Both of them takes keyword arguments with the keys being the name of the field to be updated and values being the new value. But for the _update method, only defining fields are able to be updated, and more importantly, a new instance will be created by using the updated defining fields through the initializer. At the same time, the _replace method will just perform a plain replacement of a particular field without going through the initializer again, and it works for both defining and data fields.

Both of these two methods are named with an initial underscore, this is not only an attempt to be consistent with the named tuple in the standard library, but an encourage to use them only in methods as well. Then then wrapping methods could carry the actual semantics of the update operation.


Programmable tuple classes can inherit from other programmable tuple classes. And this inheritance has been made to be as similar to the plain mutable classes as possible. Instances of subclass are instances of the corresponding superclass and has access to all the methods of the superclass. There is just one notable difference, in the initializer, the built-in super function is not working as before. To call the initializer of superclass, we can either use self.super().__init__ instead, or we can name the superclass explicitly, like SuperClass.__init__(self, args).


Instances of an programmable tuples with all the defining fields hashable are hashable. The default hashing function is the default hashing of the tuple formed by the class identity and the defining fields.

Instances are all picklable.

As the named tuple, classes of this metaclass will carry an _asdict method to convert the instance to dictionary. The method comes with two keyword arguments, full can be used to make the dictionary contain the data fields as well, and ordered can be used to return an ordered dictionary instead. Both of the two default to false.

Project details

Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

programmabletuple-0.5.0.tar.gz (14.7 kB view hashes)

Uploaded source

Built Distributions

programmabletuple-0.5.0-py3.4.egg (19.9 kB view hashes)

Uploaded 3 4

programmabletuple-0.5.0-py3-none-any.whl (16.0 kB view hashes)

Uploaded py3

Supported by

AWS AWS Cloud computing Datadog Datadog Monitoring Facebook / Instagram Facebook / Instagram PSF Sponsor Fastly Fastly CDN Google Google Object Storage and Download Analytics Huawei Huawei PSF Sponsor Microsoft Microsoft PSF Sponsor NVIDIA NVIDIA PSF Sponsor Pingdom Pingdom Monitoring Salesforce Salesforce PSF Sponsor Sentry Sentry Error logging StatusPage StatusPage Status page