# Nouveautés de Python 2.0

Auteur

  A.M. Kuchling et Moshe Zadka

## Introduction

Une nouvelle version de Python, la version 2.0, est sortie le 16 octobre 2000. Cet article traite des nouvelles fonctionnalités intéressantes de cette version, met en évidence d'autres changements utiles, et souligne quelques incompatibilités qui peuvent nécessiter la réécriture du code.

Python's development never completely stops between releases, and a steady flow of bug fixes and improvements are always being submitted. A host of minor fixes, a few optimizations, additional docstrings, and better error messages went into 2.0; to list them all would be impossible, but they're certainly significant. Consult the publicly available CVS logs if you want to see the full list. This progress is due to the five developers working for PythonLabs are now getting paid to spend their days fixing bugs, and also due to the improved communication resulting from moving to SourceForge.

## À propos de Python 1.6.

Python 1.6 peut être vu comme la version des obligations contractuelles. Après que l’équipe de développement eût quitté le CNRI en mai 2000, celui-ci a demandé la création d’une version 1.6, contenant tout le travail sur Python réalisé au CNRI. Python 1.6 représente de ce fait l’état de l’arbre CVS tel qu’il était en mai 2000, la nouvelle fonctionnalité la plus remarquable étant le support d’Unicode. Le développement a continué après mai bien sûr, donc la branche 1.6 a reçu quelques corrections pour être sûr qu’elle soit compatible avec Python 2.0. La version 1.6 fait donc partie de l’évolution de Python, ce n’est pas une branche séparée.

Alors, devriez-vous vous intéresser à Python 1.6 ? Probablement pas. Les versions 1.6final et 2.0beta1 sont sorties le même jour (5 septembre 2000), le plan étant de finaliser Python 2.0 environ un mois plus tard. Si vous avez des applications à maintenir, il n’y a pas vraiment d’intérêt à casser des choses en migrant sur la version 1.6, les réparer, puis avoir de nouveau des choses cassées à peine un mois plus tard en passant à la 2.0; il vaut mieux partir directement de la 2.0. La plupart des fonctionnalités vraiment intéressantes décrites dans ce document sont seulement dans la 2.0, parce que beaucoup de travail a été réalisé entre mai et septembre.

## Nouveau processus de développement

Le changement le plus important dans Python 2.0 ne concerne peut-être pas le code, mais le développement de Python : en mai 2000, les développeurs Python ont commencé à utiliser les outils mis à disposition par SourceForge pour stocker le code source, suivre les rapports de bogues et gérer la file d’attente des soumissions de correctifs. Pour signaler des bogues ou soumettre des correctifs pour Python 2.0, utilisez les outils de suivi des bogues et de gestion des correctifs disponibles sur la page du projet Python, à l’adresse https://sourceforge.net/projects/python/.

Le plus important des services maintenant hébergé chez SourceForge est l’arborescence CVS Python, le référentiel sous contrôle de version contenant le code source de Python. Auparavant, environ 7 personnes avaient un accès en écriture à l’arborescence CVS et tous les correctifs devaient être inspectés et archivés par l’une des personnes figurant sur cette liste restreinte. Évidemment, ce n’était pas très évolutif. En déplaçant l’arborescence CVS vers SourceForge, il est devenu possible d’accorder un accès en écriture à davantage de personnes ; en septembre 2000, 27 personnes pouvaient enregistrer les modifications, soit quatre fois plus. Cela rend possible des modifications à grande échelle qui ne seraient pas tentées si elles devaient être filtrées par le petit groupe de développeurs principaux. Par exemple, un jour, Peter Schneider-Kamp a eu l’idée de supprimer la compatibilité K&R C et de convertir le code source C de Python en ANSI C. Après avoir obtenu l’approbation de la liste de diffusion python-dev, il s’est lancé dans une série d’archives qui ont duré environ une semaine, d’autres développeurs l'ont rejoint et le travail a été fait. S’il n’y avait eut que 5 personnes ayant un accès en écriture, cette tâche aurait probablement été considérée comme « agréable, mais ne valant pas le temps ni les efforts nécessaires » et cela ne se serait jamais fait.

Le passage à l’utilisation des services de SourceForge a entraîné une augmentation remarquable du rythme de développement. Les correctifs sont maintenant soumis, commentés, révisés par des personnes autres que l’auteur d’origine et échangés entre les personnes jusqu’à ce que le correctif soit jugé utile. Les bogues sont suivis dans un emplacement central et peuvent être attribués à une personne spécifique pour être corrigés. , et nous pouvons compter le nombre de bogues ouverts pour mesurer les progrès. Cela n’a pas coûté cher: les développeurs ont désormais plus de courrier électronique à traiter, davantage de listes de diffusion à suivre et des outils spéciaux ont dû être créés pour le nouvel environnement. Par exemple, SourceForge envoie des messages électroniques de correctif et de notification de bogues par défaut qui ne sont d’aucune utilité, Ka-Ping Yee a donc créé un scraper HTML qui envoie des messages plus utiles.

La facilité d’ajout de code a provoqué quelques problèmes de croissance initiaux, tels que le code a été archivé avant qu’il ne soit prêt ou sans l’accord clair du groupe de développeurs. Le processus d’approbation qui a émergé est quelque peu similaire à celui utilisé par le groupe Apache. Les développeurs peuvent voter +1, +0, -0 ou -1 sur un patch; +1 et -1 indiquent une acceptation ou un rejet, tandis que +0 et -0 signifient que le développeur est généralement indifférent au changement, bien qu’il présente une légère inclinaison positive ou négative. Le changement le plus important par rapport au modèle Apache est que le vote est essentiellement consultatif, permettant à Guido van Rossum, détenteur du statut de « dictateur bienveillant à vie », de connaître l’opinion générale. Il peut toujours ignorer le résultat d’un vote et approuver ou rejeter un changement même si la communauté n’est pas d’accord avec lui.

Producing an actual patch is the last step in adding a new feature, and is usually easy compared to the earlier task of coming up with a good design. Discussions of new features can often explode into lengthy mailing list threads, making the discussion hard to follow, and no one can read every posting to python-dev. Therefore, a relatively formal process has been set up to write Python Enhancement Proposals (PEPs), modelled on the internet RFC process. PEPs are draft documents that describe a proposed new feature, and are continually revised until the community reaches a consensus, either accepting or rejecting the proposal. Quoting from the introduction to PEP 1, "PEP Purpose and Guidelines":

> PEP signifie Python Enhancement Proposition. Une PEP est un document de conception fournissant des informations à la communauté Python ou décrivant une nouvelle fonctionnalité de Python. La PEP devrait fournir une spécification technique concise de la fonctionnalité et une justification de celle-ci.

> Nous souhaitons que les PEP soient les principaux mécanismes permettant de proposer de nouvelles fonctionnalités, de recueillir les commentaires de la communauté sur un problème et de documenter les décisions de conception prises dans Python. L’auteur du PPE est chargé de créer un consensus au sein de la communauté et de documenter les opinions divergentes.

Lisez le reste de PEP 1 pour plus de détails sur le processus éditorial, le style et le format de PEP. Les PEP sont conservés dans l’arborescence CVS Python de SourceForge, bien qu’ils ne fassent pas partie de la distribution Python 2.0 et qu’ils soient également disponibles au format HTML à l’adresse https://www.python.org/dev/peps/. En septembre 2000, il existait 25 PEPS, allant de PEP 201, "Lockstep Iteration", à PEP 225, "Elementwise/Objectwise Operators".

## Unicode

La plus grande nouveauté de Python 2.0 est un nouveau type de données fondamental: les chaînes Unicode. Unicode utilise des nombres à 16 bits pour représenter des caractères au lieu du nombre à 8 bits utilisé par ASCII, ce qui signifie que 65 536 caractères distincts peuvent être pris en charge.

La dernière interface de prise en charge Unicode a été mise au point après de nombreuses discussions souvent houleuses sur la liste de diffusion python-dev, et principalement implémentée par Marc-André Lemburg, basée sur une implémentation de type chaîne Unicode de Fredrik Lundh. Une explication détaillée de l’interface a été écrite ainsi PEP 100, "Intégration Python Unicode". Cet article couvrira simplement les points les plus significatifs sur les interfaces Unicode.

Dans le code source Python, les chaînes Unicode sont écrites sous la forme `u"string"`. Les caractères Unicode arbitraires peuvent être écrits en utilisant une nouvelle séquence d'échappement, `\uHHHH`, où HHHH est un nombre hexadécimal à 4 chiffres de 0000 à FFFF. La séquence d'échappement `\xHHHH` peut également être utilisée, et les échappements octaux peuvent être utilisés pour les caractères allant jusqu'à U+01FF, représenté par `\777`.

Les chaînes Unicode, tout comme les chaînes ordinaires, sont un type de séquence immuable. Ils peuvent être indexés et tranchés, mais pas modifiés en place. Les chaînes Unicode ont une méthode `encoder([encoding])` qui renvoie une chaîne de 8 bits dans l’encodage souhaité. Les codages sont nommés par des chaînes, telles que `’ascii’`, `’utf-8’`, `’iso-8859-1’`, ou autre chose. Une API de codec est définie pour l’implémentation et l’enregistrement de nouveaux codages disponibles dans tout un programme Python. Si aucun codage n’est spécifié, le codage par défaut est généralement du code ASCII 7-bits, bien qu’il puisse être modifié pour votre installation Python en appelant la fonction `sys.setdefaultencoding (encoding)` dans une version personnalisée de `site.py`.

La combinaison de chaînes 8 bits et Unicode est toujours forcée en Unicode, à l’aide du codage ASCII par défaut; le résultat de `’a’ + u’bc’` est `u’abc’`.

De nouvelles fonctions primitives ont été ajoutées, et des fonctions existantes ont été modifiées pour supporter Unicode :

- `unichr(ch)` renvoie une chaîne Unicode de longueur 1, contenant le caractère ch.

- `ord(u)`, quand u est une chaîne normale ou Unicode de longueur 1, renvoie un entier représentant le nombre de caractères.

- `unicode(string [, encoding] [, errors] )` crée une chaîne Unicode à partir d’une chaîne de 8 bits. `encoding` est une chaîne nommant le codage à utiliser. Le paramètre `errors` spécifie le traitement des caractères non valides pour l’ encodage en cours; en passant `’strict’` comme valeur, une exception est générée pour toute erreur de codage, alors que `’ignore’` fait en sorte que les erreurs soient ignorées en silence et que `’replace’` utilise U+FFFD, caractère de remplacement officiel, en cas de problème.

- L’instruction `exec` et divers éléments intégrés tels que `eval()`, `getattr()` et `setattr()` accepteront également les chaînes Unicode ainsi que les chaînes ordinaires. (Il est possible que le processus de résolution de ce problème ait échappé à certaines fonctions intégrées ; si vous trouvez une fonction intégrée qui accepte les chaînes mais n’accepte pas les chaînes Unicode, signalez-la comme un bogue.)

Un nouveau module, `unicodedata`, fournit une interface aux propriétés de caractère Unicode. Par exemple, `unicodedata.category(u'A')` renvoie la chaîne de 2 caractères « Lu », le « L » désignant une lettre et « u » signifiant qu’il s’agit d’une majuscule. `unicodedata.bidirectional(u'\u0660')` renvoie « AN », ce qui signifie que U+0660 est un nombre arabe.

Le module `codecs` contient des fonctions pour rechercher les codages existants et en enregistrer de nouveaux. À moins que vous ne souhaitiez implémenter un nouvel encodage, vous utiliserez le plus souvent la fonction `codecs.lookup(encoding)`, qui renvoie un quadruplet : `(encode_func, decode_func, stream_reader, stream_writer)`.

- encode_func est une fonction qui prend une chaîne Unicode, et renvoie un n-uplet de longueur 2 `(string, length)`. string est une chaîne de caractères à 8 bits contenant une partie (ou la totalité) de la chaîne Unicode convertie en codage donné, et length vous indique le nombre de caractères de la chaîne qui ont été convertis.

- decode_func est l’opposé de encode_func, en prenant une chaîne de caractères à 8 bits et le retour d’une paire `(ustring, longueur)`, composé de la chaîne Unicode résultante ustring et l’entier length indiquant combien de caractères de la chaîne de caractères à 8 bits ont été consommés.

- stream_reader est une classe qui prend en charge le décodage de l’entrée d’un flux. stream_reader(file_obj) renvoie un objet qui prend en charge les méthodes `read()`, `readline()` et `readlines()`. Ces méthodes se traduisent toutes à partir de l’encodage donné et retourneront une chaînes de caractère Unicode.

- De même, stream_writer est une classe qui prend en charge le codage de sortie d’un flux. stream_writer(file_obj) renvoie un objet qui prend en charge les méthodes `write()` et `writelines()`. Ces méthodes prennent en entrée des chaînes Unicode, qu'elles renvoient, traduites à l'encodage donné, sur la sortie.

Par exemple, le code suivant écrit une chaîne Unicode dans un fichier, en l’encodant en UTF-8 :

    import codecs

    unistr = u'\u0660\u2000ab ...'

    (UTF8_encode, UTF8_decode,
     UTF8_streamreader, UTF8_streamwriter) = codecs.lookup('UTF-8')

    output = UTF8_streamwriter( open( '/tmp/output', 'wb') )
    output.write( unistr )
    output.close()

Le code suivant lirait ensuite le texte UTF-8 du fichier :

    input = UTF8_streamreader( open( '/tmp/output', 'rb') )
    print repr(input.read())
    input.close()

Des expressions rationnelles supportant l’Unicode sont disponibles dans le module `re`, qui a une implémentation sous-jacente appelée SRE écrite par Fredrik Lundh de Secret Labs AB.

Une option de ligne de commande `-U` a été ajoutée, ce qui fait que le compilateur Python interprète toutes les chaînes de caractères comme des chaînes de caractères Unicode. Ceci est destiné à être utilisé dans les tests et rendre votre code Python compatible avec les versions futures, car une version future de Python peut abandonner la prise en charge des chaînes de caractères 8-bits et fournir uniquement des chaînes de caractères Unicode.

## Compréhensions de listes

Les listes sont un type de données crucial dans Python, et de nombreux programmes manipulent une liste à un moment donné. Deux opérations communes sur les listes sont de boucler sur elles, soit de choisir les éléments qui répondent à un certain critère, ou d’appliquer une certaine fonction à chaque élément. Par exemple, à partir d’une liste de chaînes de caractères, vous pouvez retirer toutes les chaînes contenant une sous-chaîne donnée, ou enlever les espaces de chaque ligne.

Les fonctions existantes `map()` et `filter()` peuvent être utilisées à cette fin, mais elles nécessitent une fonction en leurs arguments. C’est très bien s’il y a une fonction intégrée existante qui peut être passé directement, mais s’il n’y a pas, vous devez créer une petite fonction pour faire le travail requis, et les règles de portée de Python rendent le résultat laid si la petite fonction a besoin d’informations supplémentaires. Prenons le premier exemple du paragraphe précédent, en trouvant toutes les chaînes de la liste contenant une sous-chaîne donnée. Vous pouvez écrire ce qui suit pour le faire:

    # Given the list L, make a list of all strings
    # containing the substring S.
    sublist = filter( lambda s, substring=S:
                         string.find(s, substring) != -1,
                      L)

En raison des règles de portée de Python, un argument par défaut est utilisé de sorte que la fonction anonyme créée par l’expression `lambda` sait quelle sous-chaîne est recherchée. Les listes en compréhension rendent ceci plus propre :

    sublist = [ s for s in L if string.find(s, S) != -1 ]

Les compréhensions de liste sont de la forme suivante :

    [ expression for expr in sequence1
                 for expr2 in sequence2 ...
                 for exprN in sequenceN
                 if condition ]

Le `for`…`in` clauses contiennent les séquences à itérer. Les séquences n’ont pas à être de la même longueur, parce qu’elles ne sont pas itérées en parallèle, mais de gauche à droite; cela est expliqué plus clairement dans les paragraphes suivants. Les éléments de la liste générée seront les valeurs successives de l’expression. La clause finale `if` clause est facultative ; si présent, l’expression n’est évaluée et ajoutée au résultat que si la condition est vraie.

Pour que la sémantique soit très claire, une compréhension de liste est équivalente au code Python suivant :

    for expr1 in sequence1:
        for expr2 in sequence2:
        ...
            for exprN in sequenceN:
                 if (condition):
                      # Append the value of
                      # the expression to the
                      # resulting list.

Cela signifie que lorsqu’il y a plusieurs `for`…`in` clauses, la liste résultante sera égale au produit des longueurs de toutes les séquences. Si vous avez deux listes de longueur 3, la liste de sortie est de longueur 9:

    seq1 = 'abc'
    seq2 = (1,2,3)
    >>> [ (x,y) for x in seq1 for y in seq2]
    [('a', 1), ('a', 2), ('a', 3), ('b', 1), ('b', 2), ('b', 3), ('c', 1),
    ('c', 2), ('c', 3)]

Afin de ne pas introduire une ambiguïté dans la grammaire de Python, expression doit être encadrée par des parenthèses si elle produit un n-uplet. La première compréhension de liste ci-dessous n'est pas valide syntaxiquement, tandis que la seconde l'est :

    # Syntax error
    [ x,y for x in seq1 for y in seq2]
    # Correct
    [ (x,y) for x in seq1 for y in seq2]

Le concept des liste en compréhension provient à l’origine du langage de programmation fonctionnelle Haskell (https://www.haskell.org). Greg Ewing a plaidé le plus efficacement pour les ajouter à Python et a écrit le patch de compréhension de liste initiale, qui a ensuite été discuté pendant un temps apparemment sans fin sur la liste de diffusion python-dev et maintenu à jour par Skip Montanaro.

## Opérateurs d’affectation augmentées

Les opérateurs d’affectation soudées, une autre fonctionnalité demandée depuis longtemps, ont été ajoutés à Python 2.0. Les opérateurs d’affectations augmentées comprennent `+=`, `-=`, `*=` et ainsi de suite. Par exemple, l’instruction `a += 2` incrémente la valeur de la variable `a` par 2, équivalente à l’opération `a = a + 2` .

La liste complète des opérateurs d’affectations pris en charge est `+=`, `-=`, `*=`, `/=`, `%=`, `**=`, `&=`, `|=`, `^=`, `>>=`, et `<<=`. Les classes Python peuvent remplacer les opérateurs d’affectations augmentées en définissant des méthodes nommées `__iadd__()`, `__isub__()`, etc. Par exemple, la classe `Number` stocke un nombre et prend en charge l’utilisation de += en créant une nouvelle instance avec une valeur incrémentée.

    class Number:
        def __init__(self, value):
            self.value = value
        def __iadd__(self, increment):
            return Number( self.value + increment)

    n = Number(5)
    n += 3
    print n.value

La méthode spéciale `__iadd__()` est appelée avec la valeur de l’incrément, et doit renvoyer une nouvelle instance avec une valeur correctement modifiée ; cette valeur de rendement est liée comme la nouvelle valeur de la variable sur le côté gauche.

Augmented assignment operators were first introduced in the C programming language, and most C-derived languages, such as awk, C++, Java, Perl, and PHP also support them. The augmented assignment patch was implemented by Thomas Wouters.

## Méthodes de chaînes de caractères

Until now string-manipulation functionality was in the `string` module, which was usually a front-end for the `strop` module written in C. The addition of Unicode posed a difficulty for the `strop` module, because the functions would all need to be rewritten in order to accept either 8-bit or Unicode strings. For functions such as `string.replace()`, which takes 3 string arguments, that means eight possible permutations, and correspondingly complicated code.

Instead, Python 2.0 pushes the problem onto the string type, making string manipulation functionality available through methods on both 8-bit strings and Unicode strings.

    >>> 'andrew'.capitalize()
    'Andrew'
    >>> 'hostname'.replace('os', 'linux')
    'hlinuxtname'
    >>> 'moshe'.find('sh')
    2

One thing that hasn't changed, a noteworthy April Fools' joke notwithstanding, is that Python strings are immutable. Thus, the string methods return new strings, and do not modify the string on which they operate.

The old `string` module is still around for backwards compatibility, but it mostly acts as a front-end to the new string methods.

Two methods which have no parallel in pre-2.0 versions, although they did exist in JPython for quite some time, are `startswith()` and `endswith()`. `s.startswith(t)` is equivalent to `s[:len(t)] == t`, while `s.endswith(t)` is equivalent to `s[-len(t):] == t`.

One other method which deserves special mention is `join()`. The `join()` method of a string receives one parameter, a sequence of strings, and is equivalent to the `string.join()` function from the old `string` module, with the arguments reversed. In other words, `s.join(seq)` is equivalent to the old `string.join(seq, s)`.

## Garbage Collection of Cycles

The C implementation of Python uses reference counting to implement garbage collection. Every Python object maintains a count of the number of references pointing to itself, and adjusts the count as references are created or destroyed. Once the reference count reaches zero, the object is no longer accessible, since you need to have a reference to an object to access it, and if the count is zero, no references exist any longer.

Reference counting has some pleasant properties: it's easy to understand and implement, and the resulting implementation is portable, fairly fast, and reacts well with other libraries that implement their own memory handling schemes. The major problem with reference counting is that it sometimes doesn't realise that objects are no longer accessible, resulting in a memory leak. This happens when there are cycles of references.

Consider the simplest possible cycle, a class instance which has a reference to itself:

    instance = SomeClass()
    instance.myself = instance

After the above two lines of code have been executed, the reference count of `instance` is 2; one reference is from the variable named `'instance'`, and the other is from the `myself` attribute of the instance.

If the next line of code is `del instance`, what happens? The reference count of `instance` is decreased by 1, so it has a reference count of 1; the reference in the `myself` attribute still exists. Yet the instance is no longer accessible through Python code, and it could be deleted. Several objects can participate in a cycle if they have references to each other, causing all of the objects to be leaked.

Python 2.0 fixes this problem by periodically executing a cycle detection algorithm which looks for inaccessible cycles and deletes the objects involved. A new `gc` module provides functions to perform a garbage collection, obtain debugging statistics, and tuning the collector's parameters.

Running the cycle detection algorithm takes some time, and therefore will result in some additional overhead. It is hoped that after we've gotten experience with the cycle collection from using 2.0, Python 2.1 will be able to minimize the overhead with careful tuning. It's not yet obvious how much performance is lost, because benchmarking this is tricky and depends crucially on how often the program creates and destroys objects. The detection of cycles can be disabled when Python is compiled, if you can't afford even a tiny speed penalty or suspect that the cycle collection is buggy, by specifying the `--without-cycle-gc` switch when running the configure script.

Several people tackled this problem and contributed to a solution. An early implementation of the cycle detection approach was written by Toby Kelsey. The current algorithm was suggested by Eric Tiedemann during a visit to CNRI, and Guido van Rossum and Neil Schemenauer wrote two different implementations, which were later integrated by Neil. Lots of other people offered suggestions along the way; the March 2000 archives of the python-dev mailing list contain most of the relevant discussion, especially in the threads titled "Reference cycle collection for Python" and "Finalization again".

## Other Core Changes

Various minor changes have been made to Python's syntax and built-in functions. None of the changes are very far-reaching, but they're handy conveniences.

### Changements mineurs du langage

A new syntax makes it more convenient to call a given function with a tuple of arguments and/or a dictionary of keyword arguments. In Python 1.5 and earlier, you'd use the `apply()` built-in function: `apply(f, args, kw)` calls the function `f()` with the argument tuple args and the keyword arguments in the dictionary kw. `apply()` is the same in 2.0, but thanks to a patch from Greg Ewing, `f(*args, **kw)` is a shorter and clearer way to achieve the same effect. This syntax is symmetrical with the syntax for defining functions:

    def f(*args, **kw):
        # args is a tuple of positional args,
        # kw is a dictionary of keyword args
        ...

The `print` statement can now have its output directed to a file-like object by following the `print` with `>> file`, similar to the redirection operator in Unix shells. Previously you'd either have to use the `write()` method of the file-like object, which lacks the convenience and simplicity of `print`, or you could assign a new value to `sys.stdout` and then restore the old value. For sending output to standard error, it's much easier to write this:

    print >> sys.stderr, "Warning: action field not supplied"

Modules can now be renamed on importing them, using the syntax `import module as name` or `from module import name as othername`. The patch was submitted by Thomas Wouters.

A new format style is available when using the `%` operator; '%r' will insert the `repr()` of its argument. This was also added from symmetry considerations, this time for symmetry with the existing '%s' format style, which inserts the `str()` of its argument. For example, `'%r %s' % ('abc', 'abc')` returns a string containing `'abc' abc`.

Previously there was no way to implement a class that overrode Python's built-in `in` operator and implemented a custom version. `obj in seq` returns true if obj is present in the sequence seq; Python computes this by simply trying every index of the sequence until either obj is found or an `IndexError` is encountered. Moshe Zadka contributed a patch which adds a `__contains__()` magic method for providing a custom implementation for `in`. Additionally, new built-in objects written in C can define what `in` means for them via a new slot in the sequence protocol.

Earlier versions of Python used a recursive algorithm for deleting objects. Deeply nested data structures could cause the interpreter to fill up the C stack and crash; Christian Tismer rewrote the deletion logic to fix this problem. On a related note, comparing recursive objects recursed infinitely and crashed; Jeremy Hylton rewrote the code to no longer crash, producing a useful result instead. For example, after this code:

    a = []
    b = []
    a.append(a)
    b.append(b)

The comparison `a==b` returns true, because the two recursive data structures are isomorphic. See the thread "trashcan and PR#7" in the April 2000 archives of the python-dev mailing list for the discussion leading up to this implementation, and some useful relevant links. Note that comparisons can now also raise exceptions. In earlier versions of Python, a comparison operation such as `cmp(a,b)` would always produce an answer, even if a user-defined `__cmp__()` method encountered an error, since the resulting exception would simply be silently swallowed.

Work has been done on porting Python to 64-bit Windows on the Itanium processor, mostly by Trent Mick of ActiveState. (Confusingly, `sys.platform` is still `'win32'` on Win64 because it seems that for ease of porting, MS Visual C++ treats code as 32 bit on Itanium.) PythonWin also supports Windows CE; see the Python CE page at https://pythonce.sourceforge.net/ for more information.

Another new platform is Darwin/MacOS X; initial support for it is in Python 2.0. Dynamic loading works, if you specify "configure --with-dyld --with-suffix=.x". Consult the README in the Python source distribution for more instructions.

An attempt has been made to alleviate one of Python's warts, the often-confusing `NameError` exception when code refers to a local variable before the variable has been assigned a value. For example, the following code raises an exception on the `print` statement in both 1.5.2 and 2.0; in 1.5.2 a `NameError` exception is raised, while 2.0 raises a new `UnboundLocalError` exception. `UnboundLocalError` is a subclass of `NameError`, so any existing code that expects `NameError` to be raised should still work.

    def f():
        print "i=",i
        i = i + 1
    f()

Two new exceptions, `TabError` and `IndentationError`, have been introduced. They're both subclasses of `SyntaxError`, and are raised when Python code is found to be improperly indented.

### Changements concernant les fonctions primitives

A new built-in, `zip(seq1, seq2, ...)`, has been added. `zip()` returns a list of tuples where each tuple contains the i-th element from each of the argument sequences. The difference between `zip()` and `map(None, seq1, seq2)` is that `map()` pads the sequences with `None` if the sequences aren't all of the same length, while `zip()` truncates the returned list to the length of the shortest argument sequence.

The `int()` and `long()` functions now accept an optional "base" parameter when the first argument is a string. `int('123', 10)` returns 123, while `int('123', 16)` returns 291. `int(123, 16)` raises a `TypeError` exception with the message "can't convert non-string with explicit base".

A new variable holding more detailed version information has been added to the `sys` module. `sys.version_info` is a tuple `(major, minor, micro, level, serial)` For example, in a hypothetical 2.0.1beta1, `sys.version_info` would be `(2, 0, 1, 'beta', 1)`. level is a string such as `"alpha"`, `"beta"`, or `"final"` for a final release.

Dictionaries have an odd new method, `setdefault(key, default)`, which behaves similarly to the existing `get()` method. However, if the key is missing, `setdefault()` both returns the value of default as `get()` would do, and also inserts it into the dictionary as the value for key. Thus, the following lines of code:

    if dict.has_key( key ): return dict[key]
    else:
        dict[key] = []
        return dict[key]

can be reduced to a single `return dict.setdefault(key, [])` statement.

The interpreter sets a maximum recursion depth in order to catch runaway recursion before filling the C stack and causing a core dump or GPF.. Previously this limit was fixed when you compiled Python, but in 2.0 the maximum recursion depth can be read and modified using `sys.getrecursionlimit()` and `sys.setrecursionlimit()`. The default value is 1000, and a rough maximum value for a given platform can be found by running a new script, `Misc/find_recursionlimit.py`.

## Porting to 2.0

New Python releases try hard to be compatible with previous releases, and the record has been pretty good. However, some changes are considered useful enough, usually because they fix initial design decisions that turned out to be actively mistaken, that breaking backward compatibility can't always be avoided. This section lists the changes in Python 2.0 that may cause old Python code to break.

The change which will probably break the most code is tightening up the arguments accepted by some methods. Some methods would take multiple arguments and treat them as a tuple, particularly various list methods such as `append()` and `insert()`. In earlier versions of Python, if `L` is a list, `L.append( 1,2 )` appends the tuple `(1,2)` to the list. In Python 2.0 this causes a `TypeError` exception to be raised, with the message: 'append requires exactly 1 argument; 2 given'. The fix is to simply add an extra set of parentheses to pass both values as a tuple: `L.append( (1,2) )`.

The earlier versions of these methods were more forgiving because they used an old function in Python's C interface to parse their arguments; 2.0 modernizes them to use `PyArg_ParseTuple()`, the current argument parsing function, which provides more helpful error messages and treats multi-argument calls as errors. If you absolutely must use 2.0 but can't fix your code, you can edit `Objects/listobject.c` and define the preprocessor symbol `NO_STRICT_LIST_APPEND` to preserve the old behaviour; this isn't recommended.

Some of the functions in the `socket` module are still forgiving in this way. For example, `socket.connect( ('hostname', 25) )()` is the correct form, passing a tuple representing an IP address, but `socket.connect( 'hostname', 25 )()` also works. `socket.connect_ex()` and `socket.bind()` are similarly easy-going. 2.0alpha1 tightened these functions up, but because the documentation actually used the erroneous multiple argument form, many people wrote code which would break with the stricter checking. GvR backed out the changes in the face of public reaction, so for the `socket` module, the documentation was fixed and the multiple argument form is simply marked as deprecated; it will be tightened up again in a future Python version.

The `\x` escape in string literals now takes exactly 2 hex digits. Previously it would consume all the hex digits following the 'x' and take the lowest 8 bits of the result, so `\x123456` was equivalent to `\x56`.

The `AttributeError` and `NameError` exceptions have a more friendly error message, whose text will be something like `'Spam' instance has no attribute 'eggs'` or `name 'eggs' is not defined`. Previously the error message was just the missing attribute name `eggs`, and code written to take advantage of this fact will break in 2.0.

Some work has been done to make integers and long integers a bit more interchangeable. In 1.5.2, large-file support was added for Solaris, to allow reading files larger than 2 GiB; this made the `tell()` method of file objects return a long integer instead of a regular integer. Some code would subtract two file offsets and attempt to use the result to multiply a sequence or slice a string, but this raised a `TypeError`. In 2.0, long integers can be used to multiply or slice a sequence, and it'll behave as you'd intuitively expect it to; `3L * 'abc'` produces 'abcabcabc', and `(0,1,2,3)[2L:4L]` produces (2,3). Long integers can also be used in various contexts where previously only integers were accepted, such as in the `seek()` method of file objects, and in the formats supported by the `%` operator (`%d`, `%i`, `%x`, etc.). For example, `"%d" % 2L**64` will produce the string `18446744073709551616`.

The subtlest long integer change of all is that the `str()` of a long integer no longer has a trailing 'L' character, though `repr()` still includes it. The 'L' annoyed many people who wanted to print long integers that looked just like regular integers, since they had to go out of their way to chop off the character. This is no longer a problem in 2.0, but code which does `str(longval)[:-1]` and assumes the 'L' is there, will now lose the final digit.

Taking the `repr()` of a float now uses a different formatting precision than `str()`. `repr()` uses `%.17g` format string for C's `sprintf()`, while `str()` uses `%.12g` as before. The effect is that `repr()` may occasionally show more decimal places than `str()`, for certain numbers. For example, the number 8.1 can't be represented exactly in binary, so `repr(8.1)` is `'8.0999999999999996'`, while str(8.1) is `'8.1'`.

The `-X` command-line option, which turned all standard exceptions into strings instead of classes, has been removed; the standard exceptions will now always be classes. The `exceptions` module containing the standard exceptions was translated from Python to a built-in C module, written by Barry Warsaw and Fredrik Lundh.

## Extending/Embedding Changes

Some of the changes are under the covers, and will only be apparent to people writing C extension modules or embedding a Python interpreter in a larger application. If you aren't dealing with Python's C API, you can safely skip this section.

The version number of the Python C API was incremented, so C extensions compiled for 1.5.2 must be recompiled in order to work with 2.0. On Windows, it's not possible for Python 2.0 to import a third party extension built for Python 1.5.x due to how Windows DLLs work, so Python will raise an exception and the import will fail.

Users of Jim Fulton's ExtensionClass module will be pleased to find out that hooks have been added so that ExtensionClasses are now supported by `isinstance()` and `issubclass()`. This means you no longer have to remember to write code such as `if type(obj) == myExtensionClass`, but can use the more natural `if isinstance(obj, myExtensionClass)`.

The `Python/importdl.c` file, which was a mass of #ifdefs to support dynamic loading on many different platforms, was cleaned up and reorganised by Greg Stein. `importdl.c` is now quite small, and platform-specific code has been moved into a bunch of `Python/dynload_*.c` files. Another cleanup: there were also a number of `my*.h` files in the Include/ directory that held various portability hacks; they've been merged into a single file, `Include/pyport.h`.

Vladimir Marangozov's long-awaited malloc restructuring was completed, to make it easy to have the Python interpreter use a custom allocator instead of C's standard `malloc()`. For documentation, read the comments in `Include/pymem.h` and `Include/objimpl.h`. For the lengthy discussions during which the interface was hammered out, see the web archives of the 'patches' and 'python-dev' lists at python.org.

Recent versions of the GUSI development environment for MacOS support POSIX threads. Therefore, Python's POSIX threading support now works on the Macintosh. Threading support using the user-space GNU `pth` library was also contributed.

Threading support on Windows was enhanced, too. Windows supports thread locks that use kernel objects only in case of contention; in the common case when there's no contention, they use simpler functions which are an order of magnitude faster. A threaded version of Python 1.5.2 on NT is twice as slow as an unthreaded version; with the 2.0 changes, the difference is only 10%. These improvements were contributed by Yakov Markovitch.

Python 2.0's source now uses only ANSI C prototypes, so compiling Python now requires an ANSI C compiler, and can no longer be done using a compiler that only supports K&R C.

Previously the Python virtual machine used 16-bit numbers in its bytecode, limiting the size of source files. In particular, this affected the maximum size of literal lists and dictionaries in Python source; occasionally people who are generating Python code would run into this limit. A patch by Charles G. Waldman raises the limit from `2**16` to `2**32`.

Three new convenience functions intended for adding constants to a module's dictionary at module initialization time were added: `PyModule_AddObject()`, `PyModule_AddIntConstant()`, and `PyModule_AddStringConstant()`. Each of these functions takes a module object, a null-terminated C string containing the name to be added, and a third argument for the value to be assigned to the name. This third argument is, respectively, a Python object, a C long, or a C string.

A wrapper API was added for Unix-style signal handlers. `PyOS_getsig()` gets a signal handler and `PyOS_setsig()` will set a new handler.

## Distutils: Making Modules Easy to Install

Before Python 2.0, installing modules was a tedious affair -- there was no way to figure out automatically where Python is installed, or what compiler options to use for extension modules. Software authors had to go through an arduous ritual of editing Makefiles and configuration files, which only really work on Unix and leave Windows and MacOS unsupported. Python users faced wildly differing installation instructions which varied between different extension packages, which made administering a Python installation something of a chore.

The SIG for distribution utilities, shepherded by Greg Ward, has created the Distutils, a system to make package installation much easier. They form the `distutils` package, a new part of Python's standard library. In the best case, installing a Python module from source will require the same steps: first you simply mean unpack the tarball or zip archive, and the run "`python setup.py install`". The platform will be automatically detected, the compiler will be recognized, C extension modules will be compiled, and the distribution installed into the proper directory. Optional command-line arguments provide more control over the installation process, the distutils package offers many places to override defaults -- separating the build from the install, building or installing in non-default directories, and more.

In order to use the Distutils, you need to write a `setup.py` script. For the simple case, when the software contains only .py files, a minimal `setup.py` can be just a few lines long:

    from distutils.core import setup
    setup (name = "foo", version = "1.0",
           py_modules = ["module1", "module2"])

The `setup.py` file isn't much more complicated if the software consists of a few packages:

    from distutils.core import setup
    setup (name = "foo", version = "1.0",
           packages = ["package", "package.subpackage"])

A C extension can be the most complicated case; here's an example taken from the PyXML package:

    from distutils.core import setup, Extension

    expat_extension = Extension('xml.parsers.pyexpat',
         define_macros = [('XML_NS', None)],
         include_dirs = [ 'extensions/expat/xmltok',
                          'extensions/expat/xmlparse' ],
         sources = [ 'extensions/pyexpat.c',
                     'extensions/expat/xmltok/xmltok.c',
                     'extensions/expat/xmltok/xmlrole.c', ]
           )
    setup (name = "PyXML", version = "0.5.4",
           ext_modules =[ expat_extension ] )

The Distutils can also take care of creating source and binary distributions. The "sdist" command, run by "`python setup.py sdist`', builds a source distribution such as `foo-1.0.tar.gz`. Adding new commands isn't difficult, "bdist_rpm" and "bdist_wininst" commands have already been contributed to create an RPM distribution and a Windows installer for the software, respectively. Commands to create other distribution formats such as Debian packages and Solaris `.pkg` files are in various stages of development.

All this is documented in a new manual, Distributing Python Modules, that joins the basic set of Python documentation.

## Modules XML

Python 1.5.2 included a simple XML parser in the form of the `xmllib` module, contributed by Sjoerd Mullender. Since 1.5.2's release, two different interfaces for processing XML have become common: SAX2 (version 2 of the Simple API for XML) provides an event-driven interface with some similarities to `xmllib`, and the DOM (Document Object Model) provides a tree-based interface, transforming an XML document into a tree of nodes that can be traversed and modified. Python 2.0 includes a SAX2 interface and a stripped-down DOM interface as part of the `xml` package. Here we will give a brief overview of these new interfaces; consult the Python documentation or the source code for complete details. The Python XML SIG is also working on improved documentation.

### Support de SAX2

SAX defines an event-driven interface for parsing XML. To use SAX, you must write a SAX handler class. Handler classes inherit from various classes provided by SAX, and override various methods that will then be called by the XML parser. For example, the `startElement()` and `endElement()` methods are called for every starting and end tag encountered by the parser, the `characters()` method is called for every chunk of character data, and so forth.

The advantage of the event-driven approach is that the whole document doesn't have to be resident in memory at any one time, which matters if you are processing really huge documents. However, writing the SAX handler class can get very complicated if you're trying to modify the document structure in some elaborate way.

For example, this little example program defines a handler that prints a message for every starting and ending tag, and then parses the file `hamlet.xml` using it:

    from xml import sax

    class SimpleHandler(sax.ContentHandler):
        def startElement(self, name, attrs):
            print 'Start of element:', name, attrs.keys()

        def endElement(self, name):
            print 'End of element:', name

    # Create a parser object
    parser = sax.make_parser()

    # Tell it what handler to use
    handler = SimpleHandler()
    parser.setContentHandler( handler )

    # Parse a file!
    parser.parse( 'hamlet.xml' )

For more information, consult the Python documentation, or the XML HOWTO at http://pyxml.sourceforge.net/topics/howto/xml-howto.html.

### Support du DOM

The Document Object Model is a tree-based representation for an XML document. A top-level `Document` instance is the root of the tree, and has a single child which is the top-level `Element` instance. This `Element` has children nodes representing character data and any sub-elements, which may have further children of their own, and so forth. Using the DOM you can traverse the resulting tree any way you like, access element and attribute values, insert and delete nodes, and convert the tree back into XML.

The DOM is useful for modifying XML documents, because you can create a DOM tree, modify it by adding new nodes or rearranging subtrees, and then produce a new XML document as output. You can also construct a DOM tree manually and convert it to XML, which can be a more flexible way of producing XML output than simply writing `<tag1>`...`</tag1>` to a file.

The DOM implementation included with Python lives in the `xml.dom.minidom` module. It's a lightweight implementation of the Level 1 DOM with support for XML namespaces. The `parse()` and `parseString()` convenience functions are provided for generating a DOM tree:

    from xml.dom import minidom
    doc = minidom.parse('hamlet.xml')

`doc` is a `Document` instance. `Document`, like all the other DOM classes such as `Element` and `Text`, is a subclass of the `Node` base class. All the nodes in a DOM tree therefore support certain common methods, such as `toxml()` which returns a string containing the XML representation of the node and its children. Each class also has special methods of its own; for example, `Element` and `Document` instances have a method to find all child elements with a given tag name. Continuing from the previous 2-line example:

    perslist = doc.getElementsByTagName( 'PERSONA' )
    print perslist[0].toxml()
    print perslist[1].toxml()

For the Hamlet XML file, the above few lines output:

    <PERSONA>CLAUDIUS, king of Denmark. </PERSONA>
    <PERSONA>HAMLET, son to the late, and nephew to the present king.</PERSONA>

The root element of the document is available as `doc.documentElement`, and its children can be easily modified by deleting, adding, or removing nodes:

    root = doc.documentElement

    # Remove the first child
    root.removeChild( root.childNodes[0] )

    # Move the new first child to the end
    root.appendChild( root.childNodes[0] )

    # Insert the new first child (originally,
    # the third child) before the 20th child.
    root.insertBefore( root.childNodes[0], root.childNodes[20] )

Again, I will refer you to the Python documentation for a complete listing of the different `Node` classes and their various methods.

### Relationship to PyXML

The XML Special Interest Group has been working on XML-related Python code for a while. Its code distribution, called PyXML, is available from the SIG's web pages at https://www.python.org/community/sigs/current/xml-sig. The PyXML distribution also used the package name `xml`. If you've written programs that used PyXML, you're probably wondering about its compatibility with the 2.0 `xml` package.

The answer is that Python 2.0's `xml` package isn't compatible with PyXML, but can be made compatible by installing a recent version PyXML. Many applications can get by with the XML support that is included with Python 2.0, but more complicated applications will require that the full PyXML package will be installed. When installed, PyXML versions 0.6.0 or greater will replace the `xml` package shipped with Python, and will be a strict superset of the standard package, adding a bunch of additional features. Some of the additional features in PyXML include:

- 4DOM, a full DOM implementation from FourThought, Inc.

- The xmlproc validating parser, written by Lars Marius Garshol.

- The `sgmlop` parser accelerator module, written by Fredrik Lundh.

## Module changes

Lots of improvements and bugfixes were made to Python's extensive standard library; some of the affected modules include `readline`, `ConfigParser`, `cgi`, `calendar`, `posix`, `readline`, `xmllib`, `aifc`, `chunk, wave`, `random`, `shelve`, and `nntplib`. Consult the CVS logs for the exact patch-by-patch details.

Brian Gallew contributed OpenSSL support for the `socket` module. OpenSSL is an implementation of the Secure Socket Layer, which encrypts the data being sent over a socket. When compiling Python, you can edit `Modules/Setup` to include SSL support, which adds an additional function to the `socket` module: `socket.ssl(socket, keyfile, certfile)`, which takes a socket object and returns an SSL socket. The `httplib` and `urllib` modules were also changed to support `https://` URLs, though no one has implemented FTP or SMTP over SSL.

The `httplib` module has been rewritten by Greg Stein to support HTTP/1.1. Backward compatibility with the 1.5 version of `httplib` is provided, though using HTTP/1.1 features such as pipelining will require rewriting code to use a different set of interfaces.

The `Tkinter` module now supports Tcl/Tk version 8.1, 8.2, or 8.3, and support for the older 7.x versions has been dropped. The Tkinter module now supports displaying Unicode strings in Tk widgets. Also, Fredrik Lundh contributed an optimization which makes operations like `create_line` and `create_polygon` much faster, especially when using lots of coordinates.

The `curses` module has been greatly extended, starting from Oliver Andrich's enhanced version, to provide many additional functions from ncurses and SYSV curses, such as colour, alternative character set support, pads, and mouse support. This means the module is no longer compatible with operating systems that only have BSD curses, but there don't seem to be any currently maintained OSes that fall into this category.

As mentioned in the earlier discussion of 2.0's Unicode support, the underlying implementation of the regular expressions provided by the `re` module has been changed. SRE, a new regular expression engine written by Fredrik Lundh and partially funded by Hewlett Packard, supports matching against both 8-bit strings and Unicode strings.

## Nouveaux modules

A number of new modules were added. We'll simply list them with brief descriptions; consult the 2.0 documentation for the details of a particular module.

- `atexit`: For registering functions to be called before the Python interpreter exits. Code that currently sets `sys.exitfunc` directly should be changed to use the `atexit` module instead, importing `atexit` and calling `atexit.register()` with the function to be called on exit. (Contributed by Skip Montanaro.)

- `codecs`, `encodings`, `unicodedata`: Added as part of the new Unicode support.

- `filecmp`: Supersedes the old `cmp`, `cmpcache` and `dircmp` modules, which have now become deprecated. (Contributed by Gordon MacMillan and Moshe Zadka.)

- `gettext`: This module provides internationalization (I18N) and localization (L10N) support for Python programs by providing an interface to the GNU gettext message catalog library. (Integrated by Barry Warsaw, from separate contributions by Martin von Löwis, Peter Funk, and James Henstridge.)

- `linuxaudiodev`: Support for the `/dev/audio` device on Linux, a twin to the existing `sunaudiodev` module. (Contributed by Peter Bosch, with fixes by Jeremy Hylton.)

- `mmap`: An interface to memory-mapped files on both Windows and Unix. A file's contents can be mapped directly into memory, at which point it behaves like a mutable string, so its contents can be read and modified. They can even be passed to functions that expect ordinary strings, such as the `re` module. (Contributed by Sam Rushing, with some extensions by A.M. Kuchling.)

- `pyexpat`: An interface to the Expat XML parser. (Contributed by Paul Prescod.)

- `robotparser`: Parse a `robots.txt` file, which is used for writing web spiders that politely avoid certain areas of a web site. The parser accepts the contents of a `robots.txt` file, builds a set of rules from it, and can then answer questions about the fetchability of a given URL. (Contributed by Skip Montanaro.)

- `tabnanny`: A module/script to check Python source code for ambiguous indentation. (Contributed by Tim Peters.)

- `UserString`: A base class useful for deriving objects that behave like strings.

- `webbrowser`: A module that provides a platform independent way to launch a web browser on a specific URL. For each platform, various browsers are tried in a specific order. The user can alter which browser is launched by setting the BROWSER environment variable. (Originally inspired by Eric S. Raymond's patch to `urllib` which added similar functionality, but the final module comes from code originally implemented by Fred Drake as `Tools/idle/BrowserControl.py`, and adapted for the standard library by Fred.)

- `_winreg`: An interface to the Windows registry. `_winreg` is an adaptation of functions that have been part of PythonWin since 1995, but has now been added to the core distribution, and enhanced to support Unicode. `_winreg` was written by Bill Tutt and Mark Hammond.

- `zipfile`: A module for reading and writing ZIP-format archives. These are archives produced by PKZIP on DOS/Windows or zip on Unix, not to be confused with gzip-format files (which are supported by the `gzip` module) (Contributed by James C. Ahlstrom.)

- `imputil`: A module that provides a simpler way for writing customized import hooks, in comparison to the existing `ihooks` module. (Implemented by Greg Stein, with much discussion on python-dev along the way.)

## IDLE Improvements

IDLE is the official Python cross-platform IDE, written using Tkinter. Python 2.0 includes IDLE 0.6, which adds a number of new features and improvements. A partial list:

- UI improvements and optimizations, especially in the area of syntax highlighting and auto-indentation.

- The class browser now shows more information, such as the top level functions in a module.

- Tab width is now a user settable option. When opening an existing Python file, IDLE automatically detects the indentation conventions, and adapts.

- There is now support for calling browsers on various platforms, used to open the Python documentation in a browser.

- IDLE now has a command line, which is largely similar to the vanilla Python interpreter.

- Call tips were added in many places.

- IDLE can now be installed as a package.

- In the editor window, there is now a line/column bar at the bottom.

- Three new keystroke commands: Check module (``Alt`-`F5``), Import module (`F5`) and Run script (``Ctrl`-`F5``).

## Deleted and Deprecated Modules

A few modules have been dropped because they're obsolete, or because there are now better ways to do the same thing. The `stdwin` module is gone; it was for a platform-independent windowing toolkit that's no longer developed.

A number of modules have been moved to the `lib-old` subdirectory: `cmp`, `cmpcache`, `dircmp`, `dump`, `find`, `grep`, `packmail`, `poly`, `util`, `whatsound`, `zmod`. If you have code which relies on a module that's been moved to `lib-old`, you can simply add that directory to `sys.path` to get them back, but you're encouraged to update any code that uses these modules.

## Remerciements

The authors would like to thank the following people for offering suggestions on various drafts of this article: David Bolen, Mark Hammond, Gregg Hauser, Jeremy Hylton, Fredrik Lundh, Detlef Lannert, Aahz Maruch, Skip Montanaro, Vladimir Marangozov, Tobias Polzin, Guido van Rossum, Neil Schemenauer, and Russ Schmidt.