python_doc_md/Beginner/tutorial/datastructures.md

# Data Structures {#tut-structures}

This chapter describes some things you\'ve learned about already in more detail, and adds some new things as well.

## More on Lists {#tut-morelists}

The `list <typesseq-list>`{.interpreted-text role="ref"} data type has some more methods. Here are all of the methods of list objects:

::: {.method noindex=""}
list.append(x)

Add an item to the end of the list. Similar to `a[len(a):] = [x]`.
:::

::: {.method noindex=""}
list.extend(iterable)

Extend the list by appending all the items from the iterable. Similar to `a[len(a):] = iterable`.
:::

::: {.method noindex=""}
list.insert(i, x)

Insert an item at a given position. The first argument is the index of the element before which to insert, so `a.insert(0, x)` inserts at the front of the list, and `a.insert(len(a), x)` is equivalent to `a.append(x)`.
:::

::: {.method noindex=""}
list.remove(x)

Remove the first item from the list whose value is equal to *x*. It raises a `ValueError`{.interpreted-text role="exc"} if there is no such item.
:::

::: {.method noindex=""}
list.pop(\[i\])

Remove the item at the given position in the list, and return it. If no index is specified, `a.pop()` removes and returns the last item in the list. It raises an `IndexError`{.interpreted-text role="exc"} if the list is empty or the index is outside the list range.
:::

::: {.method noindex=""}
list.clear()

Remove all items from the list. Similar to `del a[:]`.
:::

::: {.method noindex=""}
list.index(x\[, start\[, end\]\])

Return zero-based index of the first occurrence of *x* in the list. Raises a `ValueError`{.interpreted-text role="exc"} if there is no such item.

The optional arguments *start* and *end* are interpreted as in the slice notation and are used to limit the search to a particular subsequence of the list. The returned index is computed relative to the beginning of the full sequence rather than the *start* argument.
:::

::: {.method noindex=""}
list.count(x)

Return the number of times *x* appears in the list.
:::

::: {.method noindex=""}
list.sort(\*, key=None, reverse=False)

Sort the items of the list in place (the arguments can be used for sort customization, see `sorted`{.interpreted-text role="func"} for their explanation).
:::

::: {.method noindex=""}
list.reverse()

Reverse the elements of the list in place.
:::

::: {.method noindex=""}
list.copy()

Return a shallow copy of the list. Similar to `a[:]`.
:::

An example that uses most of the list methods:

    >>> fruits = ['orange', 'apple', 'pear', 'banana', 'kiwi', 'apple', 'banana']
    >>> fruits.count('apple')
    2
    >>> fruits.count('tangerine')
    0
    >>> fruits.index('banana')
    3
    >>> fruits.index('banana', 4)  # Find next banana starting at position 4
    6
    >>> fruits.reverse()
    >>> fruits
    ['banana', 'apple', 'kiwi', 'banana', 'pear', 'apple', 'orange']
    >>> fruits.append('grape')
    >>> fruits
    ['banana', 'apple', 'kiwi', 'banana', 'pear', 'apple', 'orange', 'grape']
    >>> fruits.sort()
    >>> fruits
    ['apple', 'apple', 'banana', 'banana', 'grape', 'kiwi', 'orange', 'pear']
    >>> fruits.pop()
    'pear'

You might have noticed that methods like `insert`, `remove` or `sort` that only modify the list have no return value printed \-- they return the default `None`.[^1] This is a design principle for all mutable data structures in Python.

Another thing you might notice is that not all data can be sorted or compared. For instance, `[None, 'hello', 10]` doesn\'t sort because integers can\'t be compared to strings and `None` can\'t be compared to other types. Also, there are some types that don\'t have a defined ordering relation. For example, `3+4j < 5+7j` isn\'t a valid comparison.

### Using Lists as Stacks {#tut-lists-as-stacks}

The list methods make it very easy to use a list as a stack, where the last element added is the first element retrieved (\"last-in, first-out\"). To add an item to the top of the stack, use `~list.append`{.interpreted-text role="meth"}. To retrieve an item from the top of the stack, use `~list.pop`{.interpreted-text role="meth"} without an explicit index. For example:

    >>> stack = [3, 4, 5]
    >>> stack.append(6)
    >>> stack.append(7)
    >>> stack
    [3, 4, 5, 6, 7]
    >>> stack.pop()
    7
    >>> stack
    [3, 4, 5, 6]
    >>> stack.pop()
    6
    >>> stack.pop()
    5
    >>> stack
    [3, 4]

### Using Lists as Queues {#tut-lists-as-queues}

It is also possible to use a list as a queue, where the first element added is the first element retrieved (\"first-in, first-out\"); however, lists are not efficient for this purpose. While appends and pops from the end of list are fast, doing inserts or pops from the beginning of a list is slow (because all of the other elements have to be shifted by one).

To implement a queue, use `collections.deque`{.interpreted-text role="class"} which was designed to have fast appends and pops from both ends. For example:

    >>> from collections import deque
    >>> queue = deque(["Eric", "John", "Michael"])
    >>> queue.append("Terry")           # Terry arrives
    >>> queue.append("Graham")          # Graham arrives
    >>> queue.popleft()                 # The first to arrive now leaves
    'Eric'
    >>> queue.popleft()                 # The second to arrive now leaves
    'John'
    >>> queue                           # Remaining queue in order of arrival
    deque(['Michael', 'Terry', 'Graham'])

### List Comprehensions {#tut-listcomps}

List comprehensions provide a concise way to create lists. Common applications are to make new lists where each element is the result of some operations applied to each member of another sequence or iterable, or to create a subsequence of those elements that satisfy a certain condition.

For example, assume we want to create a list of squares, like:

    >>> squares = []
    >>> for x in range(10):
    ...     squares.append(x**2)
    ...
    >>> squares
    [0, 1, 4, 9, 16, 25, 36, 49, 64, 81]

Note that this creates (or overwrites) a variable named `x` that still exists after the loop completes. We can calculate the list of squares without any side effects using:

    squares = list(map(lambda x: x**2, range(10)))

or, equivalently:

    squares = [x**2 for x in range(10)]

which is more concise and readable.

A list comprehension consists of brackets containing an expression followed by a `!for`{.interpreted-text role="keyword"} clause, then zero or more `!for`{.interpreted-text role="keyword"} or `!if`{.interpreted-text role="keyword"} clauses. The result will be a new list resulting from evaluating the expression in the context of the `!for`{.interpreted-text role="keyword"} and `!if`{.interpreted-text role="keyword"} clauses which follow it. For example, this listcomp combines the elements of two lists if they are not equal:

    >>> [(x, y) for x in [1,2,3] for y in [3,1,4] if x != y]
    [(1, 3), (1, 4), (2, 3), (2, 1), (2, 4), (3, 1), (3, 4)]

and it\'s equivalent to:

    >>> combs = []
    >>> for x in [1,2,3]:
    ...     for y in [3,1,4]:
    ...         if x != y:
    ...             combs.append((x, y))
    ...
    >>> combs
    [(1, 3), (1, 4), (2, 3), (2, 1), (2, 4), (3, 1), (3, 4)]

Note how the order of the `for`{.interpreted-text role="keyword"} and `if`{.interpreted-text role="keyword"} statements is the same in both these snippets.

If the expression is a tuple (e.g. the `(x, y)` in the previous example), it must be parenthesized. :

    >>> vec = [-4, -2, 0, 2, 4]
    >>> # create a new list with the values doubled
    >>> [x*2 for x in vec]
    [-8, -4, 0, 4, 8]
    >>> # filter the list to exclude negative numbers
    >>> [x for x in vec if x >= 0]
    [0, 2, 4]
    >>> # apply a function to all the elements
    >>> [abs(x) for x in vec]
    [4, 2, 0, 2, 4]
    >>> # call a method on each element
    >>> freshfruit = ['  banana', '  loganberry ', 'passion fruit  ']
    >>> [weapon.strip() for weapon in freshfruit]
    ['banana', 'loganberry', 'passion fruit']
    >>> # create a list of 2-tuples like (number, square)
    >>> [(x, x**2) for x in range(6)]
    [(0, 0), (1, 1), (2, 4), (3, 9), (4, 16), (5, 25)]
    >>> # the tuple must be parenthesized, otherwise an error is raised
    >>> [x, x**2 for x in range(6)]
      File "<stdin>", line 1
        [x, x**2 for x in range(6)]
         ^^^^^^^
    SyntaxError: did you forget parentheses around the comprehension target?
    >>> # flatten a list using a listcomp with two 'for'
    >>> vec = [[1,2,3], [4,5,6], [7,8,9]]
    >>> [num for elem in vec for num in elem]
    [1, 2, 3, 4, 5, 6, 7, 8, 9]

List comprehensions can contain complex expressions and nested functions:

    >>> from math import pi
    >>> [str(round(pi, i)) for i in range(1, 6)]
    ['3.1', '3.14', '3.142', '3.1416', '3.14159']

### Nested List Comprehensions

The initial expression in a list comprehension can be any arbitrary expression, including another list comprehension.

Consider the following example of a 3x4 matrix implemented as a list of 3 lists of length 4:

    >>> matrix = [
    ...     [1, 2, 3, 4],
    ...     [5, 6, 7, 8],
    ...     [9, 10, 11, 12],
    ... ]

The following list comprehension will transpose rows and columns:

    >>> [[row[i] for row in matrix] for i in range(4)]
    [[1, 5, 9], [2, 6, 10], [3, 7, 11], [4, 8, 12]]

As we saw in the previous section, the inner list comprehension is evaluated in the context of the `for`{.interpreted-text role="keyword"} that follows it, so this example is equivalent to:

    >>> transposed = []
    >>> for i in range(4):
    ...     transposed.append([row[i] for row in matrix])
    ...
    >>> transposed
    [[1, 5, 9], [2, 6, 10], [3, 7, 11], [4, 8, 12]]

which, in turn, is the same as:

    >>> transposed = []
    >>> for i in range(4):
    ...     # the following 3 lines implement the nested listcomp
    ...     transposed_row = []
    ...     for row in matrix:
    ...         transposed_row.append(row[i])
    ...     transposed.append(transposed_row)
    ...
    >>> transposed
    [[1, 5, 9], [2, 6, 10], [3, 7, 11], [4, 8, 12]]

In the real world, you should prefer built-in functions to complex flow statements. The `zip`{.interpreted-text role="func"} function would do a great job for this use case:

    >>> list(zip(*matrix))
    [(1, 5, 9), (2, 6, 10), (3, 7, 11), (4, 8, 12)]

See `tut-unpacking-arguments`{.interpreted-text role="ref"} for details on the asterisk in this line.

### Unpacking in Lists and List Comprehensions

The section on `tut-unpacking-arguments`{.interpreted-text role="ref"} describes the use of `*` to \"unpack\" the elements of an iterable object, providing each one separately as an argument to a function. Unpacking can also be used in other contexts, for example, when creating lists. When specifying elements of a list, prefixing an expression by a `*` will unpack the result of that expression, adding each of its elements to the list we\'re creating:

    >>> x = [1, 2, 3]
    >>> [0, *x, 4, 5, 6]
    [0, 1, 2, 3, 4, 5, 6]

This only works if the expression following the `*` evaluates to an iterable object; trying to unpack a non-iterable object will raise an exception:

    >>> x = 1
    >>> [0, *x, 2, 3, 4]
    Traceback (most recent call last):
      File "<python-input-1>", line 1, in <module>
        [0, *x, 2, 3, 4]
    TypeError: Value after * must be an iterable, not int

Unpacking can also be used in list comprehensions, as a way to build a new list representing the concatenation of an arbitrary number of iterables:

    >>> x = [[1, 2, 3], [4, 5, 6], [], [7], [8, 9]]
    >>> [*element for element in x]
    [1, 2, 3, 4, 5, 6, 7, 8, 9]

Note that the effect is that each element from `x` is unpacked. This works for arbitrary iterable objects, not just lists:

    >>> x = [[1, 2, 3], 'cat', {'spam': 'eggs'}]
    >>> [*element for element in x]
    [1, 2, 3, 'c', 'a', 't', 'spam']

But if the objects in `x` are not iterable, this expression would again raise an exception.

## The `!del`{.interpreted-text role="keyword"} statement {#tut-del}

There is a way to remove an item from a list given its index instead of its value: the `del`{.interpreted-text role="keyword"} statement. This differs from the `~list.pop`{.interpreted-text role="meth"} method which returns a value. The `!del`{.interpreted-text role="keyword"} statement can also be used to remove slices from a list or clear the entire list (which we did earlier by assignment of an empty list to the slice). For example:

    >>> a = [-1, 1, 66.25, 333, 333, 1234.5]
    >>> del a[0]
    >>> a
    [1, 66.25, 333, 333, 1234.5]
    >>> del a[2:4]
    >>> a
    [1, 66.25, 1234.5]
    >>> del a[:]
    >>> a
    []

`del`{.interpreted-text role="keyword"} can also be used to delete entire variables:

    >>> del a

Referencing the name `a` hereafter is an error (at least until another value is assigned to it). We\'ll find other uses for `del`{.interpreted-text role="keyword"} later.

## Tuples and Sequences {#tut-tuples}

We saw that lists and strings have many common properties, such as indexing and slicing operations. They are two examples of *sequence* data types (see `typesseq`{.interpreted-text role="ref"}). Since Python is an evolving language, other sequence data types may be added. There is also another standard sequence data type: the *tuple*.

A tuple consists of a number of values separated by commas, for instance:

    >>> t = 12345, 54321, 'hello!'
    >>> t[0]
    12345
    >>> t
    (12345, 54321, 'hello!')
    >>> # Tuples may be nested:
    >>> u = t, (1, 2, 3, 4, 5)
    >>> u
    ((12345, 54321, 'hello!'), (1, 2, 3, 4, 5))
    >>> # Tuples are immutable:
    >>> t[0] = 88888
    Traceback (most recent call last):
      File "<stdin>", line 1, in <module>
    TypeError: 'tuple' object does not support item assignment
    >>> # but they can contain mutable objects:
    >>> v = ([1, 2, 3], [3, 2, 1])
    >>> v
    ([1, 2, 3], [3, 2, 1])
    >>> # they support unpacking just like lists:
    >>> x = [1, 2, 3]
    >>> 0, *x, 4
    (0, 1, 2, 3, 4)

As you see, on output tuples are always enclosed in parentheses, so that nested tuples are interpreted correctly; they may be input with or without surrounding parentheses, although often parentheses are necessary anyway (if the tuple is part of a larger expression). It is not possible to assign to the individual items of a tuple, however it is possible to create tuples which contain mutable objects, such as lists.

Though tuples may seem similar to lists, they are often used in different situations and for different purposes. Tuples are `immutable`{.interpreted-text role="term"}, and usually contain a heterogeneous sequence of elements that are accessed via unpacking (see later in this section) or indexing (or even by attribute in the case of `namedtuples <collections.namedtuple>`{.interpreted-text role="func"}). Lists are `mutable`{.interpreted-text role="term"}, and their elements are usually homogeneous and are accessed by iterating over the list.

A special problem is the construction of tuples containing 0 or 1 items: the syntax has some extra quirks to accommodate these. Empty tuples are constructed by an empty pair of parentheses; a tuple with one item is constructed by following a value with a comma (it is not sufficient to enclose a single value in parentheses). Ugly, but effective. For example:

    >>> empty = ()
    >>> singleton = 'hello',    # <-- note trailing comma
    >>> len(empty)
    0
    >>> len(singleton)
    1
    >>> singleton
    ('hello',)

The statement `t = 12345, 54321, 'hello!'` is an example of *tuple packing*: the values `12345`, `54321` and `'hello!'` are packed together in a tuple. The reverse operation is also possible:

    >>> x, y, z = t

This is called, appropriately enough, *sequence unpacking* and works for any sequence on the right-hand side. Sequence unpacking requires that there are as many variables on the left side of the equals sign as there are elements in the sequence. Note that multiple assignment is really just a combination of tuple packing and sequence unpacking.

## Sets {#tut-sets}

Python also includes a data type for `sets <types-set>`{.interpreted-text role="ref"}. A set is an unordered collection with no duplicate elements. Basic uses include membership testing and eliminating duplicate entries. Set objects also support mathematical operations like union, intersection, difference, and symmetric difference.

Curly braces or the `set`{.interpreted-text role="func"} function can be used to create sets. Note: to create an empty set you have to use `set()`, not `{}`; the latter creates an empty dictionary, a data structure that we discuss in the next section.

Here is a brief demonstration:

    >>> basket = {'apple', 'orange', 'apple', 'pear', 'orange', 'banana'}
    >>> print(basket)                      # show that duplicates have been removed
    {'orange', 'banana', 'pear', 'apple'}
    >>> 'orange' in basket                 # fast membership testing
    True
    >>> 'crabgrass' in basket
    False

    >>> # Demonstrate set operations on unique letters from two words
    >>>
    >>> a = set('abracadabra')
    >>> b = set('alacazam')
    >>> a                                  # unique letters in a
    {'a', 'r', 'b', 'c', 'd'}
    >>> a - b                              # letters in a but not in b
    {'r', 'd', 'b'}
    >>> a | b                              # letters in a or b or both
    {'a', 'c', 'r', 'd', 'b', 'm', 'z', 'l'}
    >>> a & b                              # letters in both a and b
    {'a', 'c'}
    >>> a ^ b                              # letters in a or b but not both
    {'r', 'd', 'b', 'm', 'z', 'l'}

Similarly to `list comprehensions <tut-listcomps>`{.interpreted-text role="ref"}, set comprehensions are also supported, including comprehensions with unpacking:

    >>> a = {x for x in 'abracadabra' if x not in 'abc'}
    >>> a
    {'r', 'd'}

    >>> fruits = [{'apple', 'avocado', 'apricot'}, {'banana', 'blueberry'}]
    >>> {*fruit for fruit in fruits}
    {'blueberry', 'banana', 'avocado', 'apple', 'apricot'}

## Dictionaries {#tut-dictionaries}

Another useful data type built into Python is the *dictionary* (see `typesmapping`{.interpreted-text role="ref"}). Dictionaries are sometimes found in other languages as \"associative memories\" or \"associative arrays\". Unlike sequences, which are indexed by a range of numbers, dictionaries are indexed by *keys*, which can be any immutable type; strings and numbers can always be keys. Tuples can be used as keys if they contain only strings, numbers, or tuples; if a tuple contains any mutable object either directly or indirectly, it cannot be used as a key. You can\'t use lists as keys, since lists can be modified in place using index assignments, slice assignments, or methods like `~list.append`{.interpreted-text role="meth"} and `~list.extend`{.interpreted-text role="meth"}.

It is best to think of a dictionary as a set of *key: value* pairs, with the requirement that the keys are unique (within one dictionary). A pair of braces creates an empty dictionary: `{}`. Placing a comma-separated list of key:value pairs within the braces adds initial key:value pairs to the dictionary; this is also the way dictionaries are written on output.

The main operations on a dictionary are storing a value with some key and extracting the value given the key. It is also possible to delete a key:value pair with `del`. If you store using a key that is already in use, the old value associated with that key is forgotten.

Extracting a value for a non-existent key by subscripting (`d[key]`) raises a `KeyError`{.interpreted-text role="exc"}. To avoid getting this error when trying to access a possibly non-existent key, use the `~dict.get`{.interpreted-text role="meth"} method instead, which returns `None` (or a specified default value) if the key is not in the dictionary.

Performing `list(d)` on a dictionary returns a list of all the keys used in the dictionary, in insertion order (if you want it sorted, just use `sorted(d)` instead). To check whether a single key is in the dictionary, use the `in`{.interpreted-text role="keyword"} keyword.

Here is a small example using a dictionary:

    >>> tel = {'jack': 4098, 'sape': 4139}
    >>> tel['guido'] = 4127
    >>> tel
    {'jack': 4098, 'sape': 4139, 'guido': 4127}
    >>> tel['jack']
    4098
    >>> tel['irv']
    Traceback (most recent call last):
      File "<stdin>", line 1, in <module>
    KeyError: 'irv'
    >>> print(tel.get('irv'))
    None
    >>> del tel['sape']
    >>> tel['irv'] = 4127
    >>> tel
    {'jack': 4098, 'guido': 4127, 'irv': 4127}
    >>> list(tel)
    ['jack', 'guido', 'irv']
    >>> sorted(tel)
    ['guido', 'irv', 'jack']
    >>> 'guido' in tel
    True
    >>> 'jack' not in tel
    False

The `dict`{.interpreted-text role="func"} constructor builds dictionaries directly from sequences of key-value pairs:

    >>> dict([('sape', 4139), ('guido', 4127), ('jack', 4098)])
    {'sape': 4139, 'guido': 4127, 'jack': 4098}

In addition, dict comprehensions can be used to create dictionaries from arbitrary key and value expressions:

    >>> {x: x**2 for x in (2, 4, 6)}
    {2: 4, 4: 16, 6: 36}

And dictionary unpacking (via `**`) can be used to merge multiple dictionaries:

    >>> odds = {i: i**2 for i in (1, 3, 5)}
    >>> evens = {i: i**2 for i in (2, 4, 6)}
    >>> {**odds, **evens}
    {1: 1, 3: 9, 5: 25, 2: 4, 4: 16, 6: 36}

    >>> all_values = [odds, evens, {0: 0}]
    >>> {**i for i in all_values}
    {1: 1, 3: 9, 5: 25, 2: 4, 4: 16, 6: 36, 0: 0}

When the keys are simple strings, it is sometimes easier to specify pairs using keyword arguments:

    >>> dict(sape=4139, guido=4127, jack=4098)
    {'sape': 4139, 'guido': 4127, 'jack': 4098}

## Looping Techniques {#tut-loopidioms}

When looping through dictionaries, the key and corresponding value can be retrieved at the same time using the `~dict.items`{.interpreted-text role="meth"} method. :

    >>> knights = {'gallahad': 'the pure', 'robin': 'the brave'}
    >>> for k, v in knights.items():
    ...     print(k, v)
    ...
    gallahad the pure
    robin the brave

When looping through a sequence, the position index and corresponding value can be retrieved at the same time using the `enumerate`{.interpreted-text role="func"} function. :

    >>> for i, v in enumerate(['tic', 'tac', 'toe']):
    ...     print(i, v)
    ...
    0 tic
    1 tac
    2 toe

To loop over two or more sequences at the same time, the entries can be paired with the `zip`{.interpreted-text role="func"} function. :

    >>> questions = ['name', 'quest', 'favorite color']
    >>> answers = ['lancelot', 'the holy grail', 'blue']
    >>> for q, a in zip(questions, answers):
    ...     print('What is your {0}?  It is {1}.'.format(q, a))
    ...
    What is your name?  It is lancelot.
    What is your quest?  It is the holy grail.
    What is your favorite color?  It is blue.

To loop over a sequence in reverse, first specify the sequence in a forward direction and then call the `reversed`{.interpreted-text role="func"} function. :

    >>> for i in reversed(range(1, 10, 2)):
    ...     print(i)
    ...
    9
    7
    5
    3
    1

To loop over a sequence in sorted order, use the `sorted`{.interpreted-text role="func"} function which returns a new sorted list while leaving the source unaltered. :

    >>> basket = ['apple', 'orange', 'apple', 'pear', 'orange', 'banana']
    >>> for i in sorted(basket):
    ...     print(i)
    ...
    apple
    apple
    banana
    orange
    orange
    pear

Using `set`{.interpreted-text role="func"} on a sequence eliminates duplicate elements. The use of `sorted`{.interpreted-text role="func"} in combination with `set`{.interpreted-text role="func"} over a sequence is an idiomatic way to loop over unique elements of the sequence in sorted order. :

    >>> basket = ['apple', 'orange', 'apple', 'pear', 'orange', 'banana']
    >>> for f in sorted(set(basket)):
    ...     print(f)
    ...
    apple
    banana
    orange
    pear

It is sometimes tempting to change a list while you are looping over it; however, it is often simpler and safer to create a new list instead. :

    >>> import math
    >>> raw_data = [56.2, float('NaN'), 51.7, 55.3, 52.5, float('NaN'), 47.8]
    >>> filtered_data = []
    >>> for value in raw_data:
    ...     if not math.isnan(value):
    ...         filtered_data.append(value)
    ...
    >>> filtered_data
    [56.2, 51.7, 55.3, 52.5, 47.8]

## More on Conditions {#tut-conditions}

The conditions used in `while` and `if` statements can contain any operators, not just comparisons.

The comparison operators `in` and `not in` are membership tests that determine whether a value is in (or not in) a container. The operators `is` and `is not` compare whether two objects are really the same object. All comparison operators have the same priority, which is lower than that of all numerical operators.

Comparisons can be chained. For example, `a < b == c` tests whether `a` is less than `b` and moreover `b` equals `c`.

Comparisons may be combined using the Boolean operators `and` and `or`, and the outcome of a comparison (or of any other Boolean expression) may be negated with `not`. These have lower priorities than comparison operators; between them, `not` has the highest priority and `or` the lowest, so that `A and not B or C` is equivalent to `(A and (not B)) or C`. As always, parentheses can be used to express the desired composition.

The Boolean operators `and` and `or` are so-called *short-circuit* operators: their arguments are evaluated from left to right, and evaluation stops as soon as the outcome is determined. For example, if `A` and `C` are true but `B` is false, `A and B and C` does not evaluate the expression `C`. When used as a general value and not as a Boolean, the return value of a short-circuit operator is the last evaluated argument.

It is possible to assign the result of a comparison or other Boolean expression to a variable. For example, :

    >>> string1, string2, string3 = '', 'Trondheim', 'Hammer Dance'
    >>> non_null = string1 or string2 or string3
    >>> non_null
    'Trondheim'

Note that in Python, unlike C, assignment inside expressions must be done explicitly with the `walrus operator <why-can-t-i-use-an-assignment-in-an-expression>`{.interpreted-text role="ref"} `:=`. This avoids a common class of problems encountered in C programs: typing `=` in an expression when `==` was intended.

## Comparing Sequences and Other Types {#tut-comparing}

Sequence objects typically may be compared to other objects with the same sequence type. The comparison uses *lexicographical* ordering: first the first two items are compared, and if they differ this determines the outcome of the comparison; if they are equal, the next two items are compared, and so on, until either sequence is exhausted. If two items to be compared are themselves sequences of the same type, the lexicographical comparison is carried out recursively. If all items of two sequences compare equal, the sequences are considered equal. If one sequence is an initial sub-sequence of the other, the shorter sequence is the smaller (lesser) one. Lexicographical ordering for strings uses the Unicode code point number to order individual characters. Some examples of comparisons between sequences of the same type:

    (1, 2, 3)              < (1, 2, 4)
    [1, 2, 3]              < [1, 2, 4]
    'ABC' < 'C' < 'Pascal' < 'Python'
    (1, 2, 3, 4)           < (1, 2, 4)
    (1, 2)                 < (1, 2, -1)
    (1, 2, 3)             == (1.0, 2.0, 3.0)
    (1, 2, ('aa', 'ab'))   < (1, 2, ('abc', 'a'), 4)

Note that comparing objects of different types with `<` or `>` is legal provided that the objects have appropriate comparison methods. For example, mixed numeric types are compared according to their numeric value, so 0 equals 0.0, etc. Otherwise, rather than providing an arbitrary ordering, the interpreter will raise a `TypeError`{.interpreted-text role="exc"} exception.

**Footnotes**

[^1]: Other languages may return the mutated object, which allows method chaining, such as `d->insert("a")->remove("b")->sort();`.