# Projekt i Historia FAQ

## Dlaczego Python używa akapitów do grupowania instrukcji?

Guido van Rossum uważa, że używanie akapitów do grupowania jest niezwykle eleganckie i przyczynia się bardzo do przejrzystości przeciętnego programu w Pythonie. Wśród większości ludzi właściwość ta z czasem zyskuje sobie sympatyków.

Ze względu na brak początkowych/końcowych nawiasów nie może dojść do rozbieżności pomiędzy grupowaniem odbieranym przez analizator składni a tym odbieranym przez człowieka. Sporadycznie programiści języka C napotykać będą taki fragment kodu:

    if (x <= y)
            x++;
            y--;
    z++;

Tylko polecenie `x++` jest wykonywane, jeśli warunek jest prawdziwy, ale akapit prowadzi wielu do innego przekonania. Nawet doświadczony programista języka C będzie czasem dłużej się temu przyglądał, zastanawiając się dlaczego `y` jest zdekrementowane nawet do `x > y`.

Z powodu braku otwierających/zamykających nawiasów, Python jest o wiele mniej podatny na konflikty wynikające ze stylu kodowania. W języku C jest wiele różnych sposobów wstawiania nawiasów. Przyzwyczaiwszy się do czytania i pisania kodu w określonym stylu, normalną rzeczą jest, że czujemy się trochę nieswojo, gdy czytamy (albo wymaga się od nas pisania) w innym stylu.

Wiele stylów programowania umieszcza otwierające/zamykające nawiasy w linii automatycznie. To czyni programy znaczniej dłuższymi i marnuje cenną przestrzeń ekranu. W ten sposób trudniej jest otrzymać dobry przegląd programu. Idealnie funkcja powinna zmieścić się na jednym ekranie (powiedzmy 20-30 linii). 20 linii w Pythonie może więcej zdziałać niż 20 linii w języku C. Nie jest to wyłącznie wynikiem braku nawiasów otwierających/zamykających – za to odpowiedzialny jest również brak deklaracji oraz wysokopoziomowe typy danych – jednakże składnia oparta o akapity z pewnością pomaga.

## Dlaczego otrzymuję dziwne wyniki w odpowiedzi na proste działania arytmetyczne?

Spójrz na następne pytanie.

## Dlaczego obliczenia zmiennoprzecinkowe są tak niedokładne?

Użytkownicy są często zaskoczeni następującymi wynikami:

    >>> 1.2 - 1.0
    0.19999999999999996

i sądzą, że to błąd w Pythonie. To jednak nie błąd. To ma niewiele wspólnego z Pythonem. To wynika bardziej z tego jak bazowa platforma radzi sobie z liczbami zmiennoprzecinkowymi.

Typ `float` w CPython używa `double` z C do przechowywania. Wartość obiektu `float` jest przechowywana w binarnej liczbie zmiennoprzecinkowej z niezmienną precyzją (typowo 53 bity) a Python używa operacji C, które z kolei bazują na implementacji sprzętu w procesorze, żeby wykonywać działania na liczbach zmiennoprzecinkowych. To oznacza, że jeśli chodzi o działania na liczbach zmiennoprzecinkowych, to Python zachowuje się jak wiele popularnych języków, między innymi C i Java.

Many numbers that can be written easily in decimal notation cannot be expressed exactly in binary floating point. For example, after:

    >>> x = 1.2

wartość przechowywana dla `x` jest (bardzo dobrym) przybliżeniem do dziesiętnej wartości `1.2`, ale nie jest dokładnie jej równa. Na typowym urządzeniu, faktycznie przechowywana wartość wynosi:

    1.0011001100110011001100110011001100110011001100110011 (binary)

która dokładnie równa się:

    1.1999999999999999555910790149937383830547332763671875 (decimal)

Typowa precyzja 53-bitowa zapewnia Pythonowi liczby zmiennoprzecinkowe z dokładnością do 15–16 cyfr dziesiętnych.

For a fuller explanation, please see the floating-point arithmetic chapter in the Python tutorial.

## Dlaczego stringi w Pythonie są niezmienne?

Jest parę zalet.

Jedną z nich jest wydajność: wiedząc, że string jest niezmienny, możemy dla niego przypisać miejsce w trakcie tworzenia a wymagania dotyczące przechowywania są stałe i nie ulegają zmianie. To jest również jeden z powodów rozróżniania między krotkami a listami.

Inną zaletą jest to, że w Pythonie stringi są uważane za równie „elementarne” co liczby. Żadna ilość czynności nie zmieni wartości 8 na cokolwiek innego, a w Pythonie żadna ilość czynności nie zmieni stringu „osiem” na cokolwiek innego.

## Dlaczego «self» musi być wyraźnie używane w definicjach metod i wywołaniach?

The idea was borrowed from Modula-3. It turns out to be very useful, for a variety of reasons.

First, it’s more obvious that you are using a method or instance attribute instead of a local variable. Reading `self.x` or `self.meth()` makes it absolutely clear that an instance variable or method is used even if you don’t know the class definition by heart. In C++, you can sort of tell by the lack of a local variable declaration (assuming globals are rare or easily recognizable) – but in Python, there are no local variable declarations, so you’d have to look up the class definition to be sure. Some C++ and Java coding standards call for instance attributes to have an `m_` prefix, so this explicitness is still useful in those languages, too.

Second, it means that no special syntax is necessary if you want to explicitly reference or call the method from a particular class. In C++, if you want to use a method from a base class which is overridden in a derived class, you have to use the `::` operator – in Python you can write `baseclass.methodname(self, <argument list>)`. This is particularly useful for `__init__()` methods, and in general in cases where a derived class method wants to extend the base class method of the same name and thus has to call the base class method somehow.

Finally, for instance variables it solves a syntactic problem with assignment: since local variables in Python are (by definition!) those variables to which a value is assigned in a function body (and that aren’t explicitly declared global), there has to be some way to tell the interpreter that an assignment was meant to assign to an instance variable instead of to a local variable, and it should preferably be syntactic (for efficiency reasons). C++ does this through declarations, but Python doesn’t have declarations and it would be a pity having to introduce them just for this purpose. Using the explicit `self.var` solves this nicely. Similarly, for using instance variables, having to write `self.var` means that references to unqualified names inside a method don’t have to search the instance’s directories. To put it another way, local variables and instance variables live in two different namespaces, and you need to tell Python which namespace to use.

## Dlaczego nie mogę użyć przypisania w wyrażeniu?

Począwszy od Pythona 3.8 możesz!

Przypisanie w wyrażeniu realizujesz poprzez walrus operator `:=` przypisując zmienną w wyrażeniu:

    while chunk := fp.read(200):
       print(chunk)

Zobacz PEP 572 aby uzyskać więcej informacji.

## Why does Python use methods for some functionality (e.g. list.index()) but functions for other (e.g. len(list))?

As Guido said:

> (a) For some operations, prefix notation just reads better than postfix – prefix (and infix!) operations have a long tradition in mathematics which likes notations where the visuals help the mathematician thinking about a problem. Compare the easy with which we rewrite a formula like x*(a+b) into x*a + x*b to the clumsiness of doing the same thing using a raw OO notation.

> (b) When I read code that says len(x) I know that it is asking for the length of something. This tells me two things: the result is an integer, and the argument is some kind of container. To the contrary, when I read x.len(), I have to already know that x is some kind of container implementing an interface or inheriting from a class that has a standard len(). Witness the confusion we occasionally have when a class that is not implementing a mapping has a get() or keys() method, or something that isn’t a file has a write() method.

> —https://mail.python.org/pipermail/python-3000/2006-November/004643.html

## Why is join() a string method instead of a list or tuple method?

Strings became much more like other standard types starting in Python 1.6, when methods were added which give the same functionality that has always been available using the functions of the string module. Most of these new methods have been widely accepted, but the one which appears to make some programmers feel uncomfortable is:

    ", ".join(['1', '2', '4', '8', '16'])

which gives the result:

    "1, 2, 4, 8, 16"

There are two common arguments against this usage.

The first runs along the lines of: „It looks really ugly using a method of a string literal (string constant)”, to which the answer is that it might, but a string literal is just a fixed value. If the methods are to be allowed on names bound to strings there is no logical reason to make them unavailable on literals.

The second objection is typically cast as: „I am really telling a sequence to join its members together with a string constant”. Sadly, you aren’t. For some reason there seems to be much less difficulty with having `split()` as a string method, since in that case it is easy to see that

    "1, 2, 4, 8, 16".split(", ")

is an instruction to a string literal to return the substrings delimited by the given separator (or, by default, arbitrary runs of white space).

`join()` is a string method because in using it you are telling the separator string to iterate over a sequence of strings and insert itself between adjacent elements. This method can be used with any argument which obeys the rules for sequence objects, including any new classes you might define yourself. Similar methods exist for bytes and bytearray objects.

## How fast are exceptions?

A `try`/`except` block is extremely efficient if no exceptions are raised. Actually catching an exception is expensive. In versions of Python prior to 2.0 it was common to use this idiom:

    try:
        value = mydict[key]
    except KeyError:
        mydict[key] = getvalue(key)
        value = mydict[key]

This only made sense when you expected the dict to have the key almost all the time. If that wasn’t the case, you coded it like this:

    if key in mydict:
        value = mydict[key]
    else:
        value = mydict[key] = getvalue(key)

For this specific case, you could also use `value = dict.setdefault(key, getvalue(key))`, but only if the `getvalue()` call is cheap enough because it is evaluated in all cases.

## Why isn’t there a switch or case statement in Python?

In general, structured switch statements execute one block of code when an expression has a particular value or set of values. Since Python 3.10 one can easily match literal values, or constants within a namespace, with a `match ... case` statement. An older alternative is a sequence of `if... elif... elif... else`.

For cases where you need to choose from a very large number of possibilities, you can create a dictionary mapping case values to functions to call. For example:

    functions = {'a': function_1,
                 'b': function_2,
                 'c': self.method_1}

    func = functions[value]
    func()

For calling methods on objects, you can simplify yet further by using the `getattr()` built-in to retrieve methods with a particular name:

    class MyVisitor:
        def visit_a(self):
            ...

        def dispatch(self, value):
            method_name = 'visit_' + str(value)
            method = getattr(self, method_name)
            method()

It’s suggested that you use a prefix for the method names, such as `visit_` in this example. Without such a prefix, if values are coming from an untrusted source, an attacker would be able to call any method on your object.

Imitating switch with fallthrough, as with C’s switch-case-default, is possible, much harder, and less needed.

## Can’t you emulate threads in the interpreter instead of relying on an OS-specific thread implementation?

Answer 1: Unfortunately, the interpreter pushes at least one C stack frame for each Python stack frame. Also, extensions can call back into Python at almost random moments. Therefore, a complete threads implementation requires thread support for C.

Answer 2: Fortunately, there is Stackless Python, which has a completely redesigned interpreter loop that avoids the C stack.

## Why can’t lambda expressions contain statements?

Python lambda expressions cannot contain statements because Python’s syntactic framework can’t handle statements nested inside expressions. However, in Python, this is not a serious problem. Unlike lambda forms in other languages, where they add functionality, Python lambdas are only a shorthand notation if you’re too lazy to define a function.

Functions are already first class objects in Python, and can be declared in a local scope. Therefore the only advantage of using a lambda instead of a locally defined function is that you don’t need to invent a name for the function – but that’s just a local variable to which the function object (which is exactly the same type of object that a lambda expression yields) is assigned!

## Can Python be compiled to machine code, C or some other language?

Cython compiles a modified version of Python with optional annotations into C extensions. Nuitka is an up-and-coming compiler of Python into C++ code, aiming to support the full Python language.

## How does Python manage memory?

The details of Python memory management depend on the implementation. The standard implementation of Python, CPython, uses reference counting to detect inaccessible objects, and another mechanism to collect reference cycles, periodically executing a cycle detection algorithm which looks for inaccessible cycles and deletes the objects involved. The `gc` module provides functions to perform a garbage collection, obtain debugging statistics, and tune the collector’s parameters.

Other implementations (such as Jython or PyPy), however, can rely on a different mechanism such as a full-blown garbage collector. This difference can cause some subtle porting problems if your Python code depends on the behavior of the reference counting implementation.

In some Python implementations, the following code (which is fine in CPython) will probably run out of file descriptors:

    for file in very_long_list_of_files:
        f = open(file)
        c = f.read(1)

Indeed, using CPython’s reference counting and destructor scheme, each new assignment to `f` closes the previous file. With a traditional GC, however, those file objects will only get collected (and closed) at varying and possibly long intervals.

If you want to write code that will work with any Python implementation, you should explicitly close the file or use the `with` statement; this will work regardless of memory management scheme:

    for file in very_long_list_of_files:
        with open(file) as f:
            c = f.read(1)

## Why doesn’t CPython use a more traditional garbage collection scheme?

For one thing, this is not a C standard feature and hence it’s not portable. (Yes, we know about the Boehm GC library. It has bits of assembler code for most common platforms, not for all of them, and although it is mostly transparent, it isn’t completely transparent; patches are required to get Python to work with it.)

Traditional GC also becomes a problem when Python is embedded into other applications. While in a standalone Python it’s fine to replace the standard `malloc()` and `free()` with versions provided by the GC library, an application embedding Python may want to have its own substitute for `malloc()` and `free()`, and may not want Python’s. Right now, CPython works with anything that implements `malloc()` and `free()` properly.

## Why isn’t all memory freed when CPython exits?

Objects referenced from the global namespaces of Python modules are not always deallocated when Python exits. This may happen if there are circular references. There are also certain bits of memory that are allocated by the C library that are impossible to free (e.g. a tool like Purify will complain about these). Python is, however, aggressive about cleaning up memory on exit and does try to destroy every single object.

If you want to force Python to delete certain things on deallocation use the `atexit` module to run a function that will force those deletions.

## Why are there separate tuple and list data types?

Lists and tuples, while similar in many respects, are generally used in fundamentally different ways. Tuples can be thought of as being similar to Pascal `records` or C `structs`; they’re small collections of related data which may be of different types which are operated on as a group. For example, a Cartesian coordinate is appropriately represented as a tuple of two or three numbers.

Lists, on the other hand, are more like arrays in other languages. They tend to hold a varying number of objects all of which have the same type and which are operated on one-by-one. For example, `os.listdir('.')` returns a list of strings representing the files in the current directory. Functions which operate on this output would generally not break if you added another file or two to the directory.

Tuples are immutable, meaning that once a tuple has been created, you can’t replace any of its elements with a new value. Lists are mutable, meaning that you can always change a list’s elements. Only hashable objects can be used as dictionary keys. Most immutable types are hashable, which is why tuples, but not lists, can be used as keys. Note, however, that a tuple is only hashable if all of its elements are hashable.

## How are lists implemented in CPython?

CPython’s lists are really variable-length arrays, not Lisp-style linked lists. The implementation uses a contiguous array of references to other objects, and keeps a pointer to this array and the array’s length in a list head structure.

This makes indexing a list `a[i]` an operation whose cost is independent of the size of the list or the value of the index.

When items are appended or inserted, the array of references is resized. Some cleverness is applied to improve the performance of appending items repeatedly; when the array must be grown, some extra space is allocated so the next few times don’t require an actual resize.

## How are dictionaries implemented in CPython?

CPython’s dictionaries are implemented as resizable hash tables. Compared to B-trees, this gives better performance for lookup (the most common operation by far) under most circumstances, and the implementation is simpler.

Dictionaries work by computing a hash code for each key stored in the dictionary using the `hash()` built-in function. The hash code varies widely depending on the key and a per-process seed; for example, `'Python'` could hash to `-539294296` while `'python'`, a string that differs by a single bit, could hash to `1142331976`. The hash code is then used to calculate a location in an internal array where the value will be stored. Assuming that you’re storing keys that all have different hash values, this means that dictionaries take constant time – O(1), in Big-O notation – to retrieve a key.

## Why must dictionary keys be immutable?

The hash table implementation of dictionaries uses a hash value calculated from the key value to find the key. If the key were a mutable object, its value could change, and thus its hash could also change. But since whoever changes the key object can’t tell that it was being used as a dictionary key, it can’t move the entry around in the dictionary. Then, when you try to look up the same object in the dictionary it won’t be found because its hash value is different. If you tried to look up the old value it wouldn’t be found either, because the value of the object found in that hash bin would be different.

If you want a dictionary indexed with a list, simply convert the list to a tuple first; the function `tuple(L)` creates a tuple with the same entries as the list `L`. Tuples are immutable and can therefore be used as dictionary keys.

Some unacceptable solutions that have been proposed:

- Hash lists by their address (object ID). This doesn’t work because if you construct a new list with the same value it won’t be found; e.g.:

      mydict = {[1, 2]: '12'}
      print(mydict[[1, 2]])

  would raise a `KeyError` exception because the id of the `[1, 2]` used in the second line differs from that in the first line. In other words, dictionary keys should be compared using `==`, not using `is`.

- Make a copy when using a list as a key. This doesn’t work because the list, being a mutable object, could contain a reference to itself, and then the copying code would run into an infinite loop.

- Allow lists as keys but tell the user not to modify them. This would allow a class of hard-to-track bugs in programs when you forgot or modified a list by accident. It also invalidates an important invariant of dictionaries: every value in `d.keys()` is usable as a key of the dictionary.

- Mark lists as read-only once they are used as a dictionary key. The problem is that it’s not just the top-level object that could change its value; you could use a tuple containing a list as a key. Entering anything as a key into a dictionary would require marking all objects reachable from there as read-only – and again, self-referential objects could cause an infinite loop.

There is a trick to get around this if you need to, but use it at your own risk: You can wrap a mutable structure inside a class instance which has both a `__eq__()` and a `__hash__()` method. You must then make sure that the hash value for all such wrapper objects that reside in a dictionary (or other hash based structure), remain fixed while the object is in the dictionary (or other structure).

    class ListWrapper:
        def __init__(self, the_list):
            self.the_list = the_list

        def __eq__(self, other):
            return self.the_list == other.the_list

        def __hash__(self):
            l = self.the_list
            result = 98767 - len(l)*555
            for i, el in enumerate(l):
                try:
                    result = result + (hash(el) % 9999999) * 1001 + i
                except Exception:
                    result = (result % 7777777) + i * 333
            return result

Note that the hash computation is complicated by the possibility that some members of the list may be unhashable and also by the possibility of arithmetic overflow.

Furthermore it must always be the case that if `o1 == o2` (ie `o1.__eq__(o2) is True`) then `hash(o1) == hash(o2)` (ie, `o1.__hash__() == o2.__hash__()`), regardless of whether the object is in a dictionary or not. If you fail to meet these restrictions dictionaries and other hash based structures will misbehave.

In the case of `ListWrapper`, whenever the wrapper object is in a dictionary the wrapped list must not change to avoid anomalies. Don’t do this unless you are prepared to think hard about the requirements and the consequences of not meeting them correctly. Consider yourself warned.

## Why doesn’t list.sort() return the sorted list?

In situations where performance matters, making a copy of the list just to sort it would be wasteful. Therefore, `list.sort()` sorts the list in place. In order to remind you of that fact, it does not return the sorted list. This way, you won’t be fooled into accidentally overwriting a list when you need a sorted copy but also need to keep the unsorted version around.

If you want to return a new list, use the built-in `sorted()` function instead. This function creates a new list from a provided iterable, sorts it and returns it. For example, here’s how to iterate over the keys of a dictionary in sorted order:

    for key in sorted(mydict):
        ...  # do whatever with mydict[key]...

## How do you specify and enforce an interface spec in Python?

An interface specification for a module as provided by languages such as C++ and Java describes the prototypes for the methods and functions of the module. Many feel that compile-time enforcement of interface specifications helps in the construction of large programs.

Python 2.6 adds an `abc` module that lets you define Abstract Base Classes (ABCs). You can then use `isinstance()` and `issubclass()` to check whether an instance or a class implements a particular ABC. The `collections.abc` module defines a set of useful ABCs such as `Iterable`, `Container`, and `MutableMapping`.

For Python, many of the advantages of interface specifications can be obtained by an appropriate test discipline for components.

A good test suite for a module can both provide a regression test and serve as a module interface specification and a set of examples. Many Python modules can be run as a script to provide a simple „self test.” Even modules which use complex external interfaces can often be tested in isolation using trivial „stub” emulations of the external interface. The `doctest` and `unittest` modules or third-party test frameworks can be used to construct exhaustive test suites that exercise every line of code in a module.

An appropriate testing discipline can help build large complex applications in Python as well as having interface specifications would. In fact, it can be better because an interface specification cannot test certain properties of a program. For example, the `list.append()` method is expected to add new elements to the end of some internal list; an interface specification cannot test that your `list.append()` implementation will actually do this correctly, but it’s trivial to check this property in a test suite.

Writing test suites is very helpful, and you might want to design your code to make it easily tested. One increasingly popular technique, test-driven development, calls for writing parts of the test suite first, before you write any of the actual code. Of course Python allows you to be sloppy and not write test cases at all.

## Why is there no goto?

In the 1970s people realized that unrestricted goto could lead to messy „spaghetti” code that was hard to understand and revise. In a high-level language, it is also unneeded as long as there are ways to branch (in Python, with `if` statements and `or`, `and`, and `if`/`else` expressions) and loop (with `while` and `for` statements, possibly containing `continue` and `break`).

One can also use exceptions to provide a „structured goto” that works even across function calls. Many feel that exceptions can conveniently emulate all reasonable uses of the `go` or `goto` constructs of C, Fortran, and other languages. For example:

    class label(Exception): pass  # declare a label

    try:
        ...
        if condition: raise label()  # goto label
        ...
    except label:  # where to goto
        pass
    ...

This doesn’t allow you to jump into the middle of a loop, but that’s usually considered an abuse of `goto` anyway. Use sparingly.

## Why can’t raw strings (r-strings) end with a backslash?

More precisely, they can’t end with an odd number of backslashes: the unpaired backslash at the end escapes the closing quote character, leaving an unterminated string.

Raw strings were designed to ease creating input for processors (chiefly regular expression engines) that want to do their own backslash escape processing. Such processors consider an unmatched trailing backslash to be an error anyway, so raw strings disallow that. In return, they allow you to pass on the string quote character by escaping it with a backslash. These rules work well when r-strings are used for their intended purpose.

If you’re trying to build Windows pathnames, note that all Windows system calls accept forward slashes too:

    f = open("/mydir/file.txt")  # works fine!

If you’re trying to build a pathname for a DOS command, try e.g. one of

    dir = r"\this\is\my\dos\dir" "\\"
    dir = r"\this\is\my\dos\dir\ "[:-1]
    dir = "\\this\\is\\my\\dos\\dir\\"

## Why doesn’t Python have a „with” statement for attribute assignments?

Python has a `with` statement that wraps the execution of a block, calling code on the entrance and exit from the block. Some languages have a construct that looks like this:

    with obj:
        a = 1               # equivalent to obj.a = 1
        total = total + 1   # obj.total = obj.total + 1

In Python, such a construct would be ambiguous.

Other languages, such as Object Pascal, Delphi, and C++, use static types, so it’s possible to know, in an unambiguous way, what member is being assigned to. This is the main point of static typing – the compiler always knows the scope of every variable at compile time.

Python uses dynamic types. It is impossible to know in advance which attribute will be referenced at runtime. Member attributes may be added or removed from objects on the fly. This makes it impossible to know, from a simple reading, what attribute is being referenced: a local one, a global one, or a member attribute?

For instance, take the following incomplete snippet:

    def foo(a):
        with a:
            print(x)

The snippet assumes that `a` must have a member attribute called `x`. However, there is nothing in Python that tells the interpreter this. What should happen if `a` is, let us say, an integer? If there is a global variable named `x`, will it be used inside the `with` block? As you see, the dynamic nature of Python makes such choices much harder.

The primary benefit of `with` and similar language features (reduction of code volume) can, however, easily be achieved in Python by assignment. Instead of:

    function(args).mydict[index][index].a = 21
    function(args).mydict[index][index].b = 42
    function(args).mydict[index][index].c = 63

write this:

    ref = function(args).mydict[index][index]
    ref.a = 21
    ref.b = 42
    ref.c = 63

This also has the side-effect of increasing execution speed because name bindings are resolved at run-time in Python, and the second version only needs to perform the resolution once.

Similar proposals that would introduce syntax to further reduce code volume, such as using a «leading dot», have been rejected in favour of explicitness (see https://mail.python.org/pipermail/python-ideas/2016-May/040070.html).

## Why don’t generators support the with statement?

For technical reasons, a generator used directly as a context manager would not work correctly. When, as is most common, a generator is used as an iterator run to completion, no closing is needed. When it is, wrap it as `contextlib.closing(generator)` in the `with` statement.

## Why are colons required for the if/while/def/class statements?

The colon is required primarily to enhance readability (one of the results of the experimental ABC language). Consider this:

    if a == b
        print(a)

versus

    if a == b:
        print(a)

Notice how the second one is slightly easier to read. Notice further how a colon sets off the example in this FAQ answer; it’s a standard usage in English.

Another minor reason is that the colon makes it easier for editors with syntax highlighting; they can look for colons to decide when indentation needs to be increased instead of having to do a more elaborate parsing of the program text.

## Why does Python allow commas at the end of lists and tuples?

Python lets you add a trailing comma at the end of lists, tuples, and dictionaries:

    [1, 2, 3,]
    ('a', 'b', 'c',)
    d = {
        "A": [1, 5],
        "B": [6, 7],  # last trailing comma is optional but good style
    }

There are several reasons to allow this.

When you have a literal value for a list, tuple, or dictionary spread across multiple lines, it’s easier to add more elements because you don’t have to remember to add a comma to the previous line. The lines can also be reordered without creating a syntax error.

Accidentally omitting the comma can lead to errors that are hard to diagnose. For example:

    x = [
      "fee",
      "fie"
      "foo",
      "fum"
    ]

This list looks like it has four elements, but it actually contains three: „fee”, „fiefoo” and „fum”. Always adding the comma avoids this source of error.

Allowing the trailing comma may also make programmatic code generation easier.