713 lines
24 KiB
ReStructuredText
713 lines
24 KiB
ReStructuredText
.. _tut-structures:
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***************
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Data Structures
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***************
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This chapter describes some things you've learned about already in more detail,
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and adds some new things as well.
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.. _tut-morelists:
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More on Lists
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=============
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The list data type has some more methods. Here are all of the methods of list
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objects:
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.. method:: list.append(x)
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:noindex:
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Add an item to the end of the list. Equivalent to ``a[len(a):] = [x]``.
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.. method:: list.extend(iterable)
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:noindex:
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Extend the list by appending all the items from the iterable. Equivalent to
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``a[len(a):] = iterable``.
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.. method:: list.insert(i, x)
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:noindex:
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Insert an item at a given position. The first argument is the index of the
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element before which to insert, so ``a.insert(0, x)`` inserts at the front of
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the list, and ``a.insert(len(a), x)`` is equivalent to ``a.append(x)``.
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.. method:: list.remove(x)
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:noindex:
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Remove the first item from the list whose value is equal to *x*. It raises a
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:exc:`ValueError` if there is no such item.
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.. method:: list.pop([i])
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:noindex:
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Remove the item at the given position in the list, and return it. If no index
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is specified, ``a.pop()`` removes and returns the last item in the list. (The
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square brackets around the *i* in the method signature denote that the parameter
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is optional, not that you should type square brackets at that position. You
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will see this notation frequently in the Python Library Reference.)
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.. method:: list.clear()
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:noindex:
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Remove all items from the list. Equivalent to ``del a[:]``.
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.. method:: list.index(x[, start[, end]])
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:noindex:
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Return zero-based index in the list of the first item whose value is equal to *x*.
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Raises a :exc:`ValueError` if there is no such item.
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The optional arguments *start* and *end* are interpreted as in the slice
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notation and are used to limit the search to a particular subsequence of
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the list. The returned index is computed relative to the beginning of the full
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sequence rather than the *start* argument.
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.. method:: list.count(x)
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:noindex:
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Return the number of times *x* appears in the list.
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.. method:: list.sort(key=None, reverse=False)
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:noindex:
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Sort the items of the list in place (the arguments can be used for sort
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customization, see :func:`sorted` for their explanation).
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.. method:: list.reverse()
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:noindex:
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Reverse the elements of the list in place.
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.. method:: list.copy()
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:noindex:
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Return a shallow copy of the list. Equivalent to ``a[:]``.
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An example that uses most of the list methods::
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>>> fruits = ['orange', 'apple', 'pear', 'banana', 'kiwi', 'apple', 'banana']
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>>> fruits.count('apple')
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2
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>>> fruits.count('tangerine')
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0
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>>> fruits.index('banana')
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3
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>>> fruits.index('banana', 4) # Find next banana starting a position 4
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6
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>>> fruits.reverse()
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>>> fruits
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['banana', 'apple', 'kiwi', 'banana', 'pear', 'apple', 'orange']
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>>> fruits.append('grape')
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>>> fruits
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['banana', 'apple', 'kiwi', 'banana', 'pear', 'apple', 'orange', 'grape']
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>>> fruits.sort()
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>>> fruits
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['apple', 'apple', 'banana', 'banana', 'grape', 'kiwi', 'orange', 'pear']
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>>> fruits.pop()
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'pear'
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You might have noticed that methods like ``insert``, ``remove`` or ``sort`` that
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only modify the list have no return value printed -- they return the default
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``None``. [1]_ This is a design principle for all mutable data structures in
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Python.
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.. _tut-lists-as-stacks:
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Using Lists as Stacks
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---------------------
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.. sectionauthor:: Ka-Ping Yee <ping@lfw.org>
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The list methods make it very easy to use a list as a stack, where the last
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element added is the first element retrieved ("last-in, first-out"). To add an
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item to the top of the stack, use :meth:`append`. To retrieve an item from the
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top of the stack, use :meth:`pop` without an explicit index. For example::
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>>> stack = [3, 4, 5]
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>>> stack.append(6)
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>>> stack.append(7)
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>>> stack
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[3, 4, 5, 6, 7]
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>>> stack.pop()
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7
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>>> stack
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[3, 4, 5, 6]
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>>> stack.pop()
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6
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>>> stack.pop()
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5
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>>> stack
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[3, 4]
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.. _tut-lists-as-queues:
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Using Lists as Queues
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---------------------
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.. sectionauthor:: Ka-Ping Yee <ping@lfw.org>
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It is also possible to use a list as a queue, where the first element added is
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the first element retrieved ("first-in, first-out"); however, lists are not
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efficient for this purpose. While appends and pops from the end of list are
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fast, doing inserts or pops from the beginning of a list is slow (because all
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of the other elements have to be shifted by one).
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To implement a queue, use :class:`collections.deque` which was designed to
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have fast appends and pops from both ends. For example::
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>>> from collections import deque
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>>> queue = deque(["Eric", "John", "Michael"])
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>>> queue.append("Terry") # Terry arrives
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>>> queue.append("Graham") # Graham arrives
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>>> queue.popleft() # The first to arrive now leaves
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'Eric'
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>>> queue.popleft() # The second to arrive now leaves
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'John'
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>>> queue # Remaining queue in order of arrival
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deque(['Michael', 'Terry', 'Graham'])
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.. _tut-listcomps:
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List Comprehensions
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-------------------
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List comprehensions provide a concise way to create lists.
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Common applications are to make new lists where each element is the result of
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some operations applied to each member of another sequence or iterable, or to
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create a subsequence of those elements that satisfy a certain condition.
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For example, assume we want to create a list of squares, like::
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>>> squares = []
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>>> for x in range(10):
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... squares.append(x**2)
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...
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>>> squares
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[0, 1, 4, 9, 16, 25, 36, 49, 64, 81]
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Note that this creates (or overwrites) a variable named ``x`` that still exists
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after the loop completes. We can calculate the list of squares without any
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side effects using::
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squares = list(map(lambda x: x**2, range(10)))
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or, equivalently::
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squares = [x**2 for x in range(10)]
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which is more concise and readable.
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A list comprehension consists of brackets containing an expression followed
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by a :keyword:`for` clause, then zero or more :keyword:`for` or :keyword:`if`
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clauses. The result will be a new list resulting from evaluating the expression
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in the context of the :keyword:`for` and :keyword:`if` clauses which follow it.
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For example, this listcomp combines the elements of two lists if they are not
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equal::
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>>> [(x, y) for x in [1,2,3] for y in [3,1,4] if x != y]
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[(1, 3), (1, 4), (2, 3), (2, 1), (2, 4), (3, 1), (3, 4)]
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and it's equivalent to::
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>>> combs = []
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>>> for x in [1,2,3]:
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... for y in [3,1,4]:
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... if x != y:
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... combs.append((x, y))
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...
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>>> combs
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[(1, 3), (1, 4), (2, 3), (2, 1), (2, 4), (3, 1), (3, 4)]
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Note how the order of the :keyword:`for` and :keyword:`if` statements is the
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same in both these snippets.
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If the expression is a tuple (e.g. the ``(x, y)`` in the previous example),
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it must be parenthesized. ::
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>>> vec = [-4, -2, 0, 2, 4]
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>>> # create a new list with the values doubled
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>>> [x*2 for x in vec]
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[-8, -4, 0, 4, 8]
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>>> # filter the list to exclude negative numbers
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>>> [x for x in vec if x >= 0]
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[0, 2, 4]
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>>> # apply a function to all the elements
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>>> [abs(x) for x in vec]
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[4, 2, 0, 2, 4]
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>>> # call a method on each element
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>>> freshfruit = [' banana', ' loganberry ', 'passion fruit ']
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>>> [weapon.strip() for weapon in freshfruit]
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['banana', 'loganberry', 'passion fruit']
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>>> # create a list of 2-tuples like (number, square)
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>>> [(x, x**2) for x in range(6)]
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[(0, 0), (1, 1), (2, 4), (3, 9), (4, 16), (5, 25)]
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>>> # the tuple must be parenthesized, otherwise an error is raised
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>>> [x, x**2 for x in range(6)]
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File "<stdin>", line 1, in <module>
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[x, x**2 for x in range(6)]
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^
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SyntaxError: invalid syntax
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>>> # flatten a list using a listcomp with two 'for'
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>>> vec = [[1,2,3], [4,5,6], [7,8,9]]
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>>> [num for elem in vec for num in elem]
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[1, 2, 3, 4, 5, 6, 7, 8, 9]
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List comprehensions can contain complex expressions and nested functions::
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>>> from math import pi
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>>> [str(round(pi, i)) for i in range(1, 6)]
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['3.1', '3.14', '3.142', '3.1416', '3.14159']
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Nested List Comprehensions
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--------------------------
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The initial expression in a list comprehension can be any arbitrary expression,
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including another list comprehension.
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Consider the following example of a 3x4 matrix implemented as a list of
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3 lists of length 4::
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>>> matrix = [
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... [1, 2, 3, 4],
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... [5, 6, 7, 8],
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... [9, 10, 11, 12],
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... ]
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The following list comprehension will transpose rows and columns::
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>>> [[row[i] for row in matrix] for i in range(4)]
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[[1, 5, 9], [2, 6, 10], [3, 7, 11], [4, 8, 12]]
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As we saw in the previous section, the nested listcomp is evaluated in
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the context of the :keyword:`for` that follows it, so this example is
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equivalent to::
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>>> transposed = []
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>>> for i in range(4):
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... transposed.append([row[i] for row in matrix])
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...
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>>> transposed
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[[1, 5, 9], [2, 6, 10], [3, 7, 11], [4, 8, 12]]
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which, in turn, is the same as::
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>>> transposed = []
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>>> for i in range(4):
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... # the following 3 lines implement the nested listcomp
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... transposed_row = []
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... for row in matrix:
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... transposed_row.append(row[i])
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... transposed.append(transposed_row)
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...
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>>> transposed
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[[1, 5, 9], [2, 6, 10], [3, 7, 11], [4, 8, 12]]
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In the real world, you should prefer built-in functions to complex flow statements.
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The :func:`zip` function would do a great job for this use case::
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>>> list(zip(*matrix))
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[(1, 5, 9), (2, 6, 10), (3, 7, 11), (4, 8, 12)]
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See :ref:`tut-unpacking-arguments` for details on the asterisk in this line.
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.. _tut-del:
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The :keyword:`del` statement
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============================
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There is a way to remove an item from a list given its index instead of its
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value: the :keyword:`del` statement. This differs from the :meth:`pop` method
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which returns a value. The :keyword:`del` statement can also be used to remove
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slices from a list or clear the entire list (which we did earlier by assignment
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of an empty list to the slice). For example::
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>>> a = [-1, 1, 66.25, 333, 333, 1234.5]
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>>> del a[0]
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>>> a
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[1, 66.25, 333, 333, 1234.5]
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>>> del a[2:4]
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>>> a
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[1, 66.25, 1234.5]
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>>> del a[:]
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>>> a
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[]
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:keyword:`del` can also be used to delete entire variables::
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>>> del a
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Referencing the name ``a`` hereafter is an error (at least until another value
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is assigned to it). We'll find other uses for :keyword:`del` later.
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.. _tut-tuples:
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Tuples and Sequences
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====================
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We saw that lists and strings have many common properties, such as indexing and
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slicing operations. They are two examples of *sequence* data types (see
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:ref:`typesseq`). Since Python is an evolving language, other sequence data
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types may be added. There is also another standard sequence data type: the
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*tuple*.
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A tuple consists of a number of values separated by commas, for instance::
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>>> t = 12345, 54321, 'hello!'
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>>> t[0]
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12345
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>>> t
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(12345, 54321, 'hello!')
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>>> # Tuples may be nested:
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... u = t, (1, 2, 3, 4, 5)
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>>> u
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((12345, 54321, 'hello!'), (1, 2, 3, 4, 5))
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>>> # Tuples are immutable:
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... t[0] = 88888
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Traceback (most recent call last):
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File "<stdin>", line 1, in <module>
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TypeError: 'tuple' object does not support item assignment
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>>> # but they can contain mutable objects:
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... v = ([1, 2, 3], [3, 2, 1])
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>>> v
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([1, 2, 3], [3, 2, 1])
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As you see, on output tuples are always enclosed in parentheses, so that nested
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tuples are interpreted correctly; they may be input with or without surrounding
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parentheses, although often parentheses are necessary anyway (if the tuple is
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part of a larger expression). It is not possible to assign to the individual
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items of a tuple, however it is possible to create tuples which contain mutable
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objects, such as lists.
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Though tuples may seem similar to lists, they are often used in different
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situations and for different purposes.
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Tuples are :term:`immutable`, and usually contain a heterogeneous sequence of
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elements that are accessed via unpacking (see later in this section) or indexing
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(or even by attribute in the case of :func:`namedtuples <collections.namedtuple>`).
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Lists are :term:`mutable`, and their elements are usually homogeneous and are
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accessed by iterating over the list.
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A special problem is the construction of tuples containing 0 or 1 items: the
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syntax has some extra quirks to accommodate these. Empty tuples are constructed
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by an empty pair of parentheses; a tuple with one item is constructed by
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following a value with a comma (it is not sufficient to enclose a single value
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in parentheses). Ugly, but effective. For example::
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>>> empty = ()
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>>> singleton = 'hello', # <-- note trailing comma
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>>> len(empty)
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0
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>>> len(singleton)
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1
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>>> singleton
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('hello',)
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The statement ``t = 12345, 54321, 'hello!'`` is an example of *tuple packing*:
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the values ``12345``, ``54321`` and ``'hello!'`` are packed together in a tuple.
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The reverse operation is also possible::
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>>> x, y, z = t
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This is called, appropriately enough, *sequence unpacking* and works for any
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sequence on the right-hand side. Sequence unpacking requires that there are as
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many variables on the left side of the equals sign as there are elements in the
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sequence. Note that multiple assignment is really just a combination of tuple
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packing and sequence unpacking.
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.. _tut-sets:
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Sets
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====
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Python also includes a data type for *sets*. A set is an unordered collection
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with no duplicate elements. Basic uses include membership testing and
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eliminating duplicate entries. Set objects also support mathematical operations
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like union, intersection, difference, and symmetric difference.
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Curly braces or the :func:`set` function can be used to create sets. Note: to
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create an empty set you have to use ``set()``, not ``{}``; the latter creates an
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empty dictionary, a data structure that we discuss in the next section.
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Here is a brief demonstration::
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>>> basket = {'apple', 'orange', 'apple', 'pear', 'orange', 'banana'}
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>>> print(basket) # show that duplicates have been removed
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{'orange', 'banana', 'pear', 'apple'}
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>>> 'orange' in basket # fast membership testing
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True
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>>> 'crabgrass' in basket
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False
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>>> # Demonstrate set operations on unique letters from two words
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...
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>>> a = set('abracadabra')
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>>> b = set('alacazam')
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>>> a # unique letters in a
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{'a', 'r', 'b', 'c', 'd'}
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>>> a - b # letters in a but not in b
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{'r', 'd', 'b'}
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>>> a | b # letters in a or b or both
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{'a', 'c', 'r', 'd', 'b', 'm', 'z', 'l'}
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>>> a & b # letters in both a and b
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{'a', 'c'}
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>>> a ^ b # letters in a or b but not both
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{'r', 'd', 'b', 'm', 'z', 'l'}
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Similarly to :ref:`list comprehensions <tut-listcomps>`, set comprehensions
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are also supported::
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>>> a = {x for x in 'abracadabra' if x not in 'abc'}
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>>> a
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{'r', 'd'}
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.. _tut-dictionaries:
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Dictionaries
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============
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Another useful data type built into Python is the *dictionary* (see
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:ref:`typesmapping`). Dictionaries are sometimes found in other languages as
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"associative memories" or "associative arrays". Unlike sequences, which are
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indexed by a range of numbers, dictionaries are indexed by *keys*, which can be
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any immutable type; strings and numbers can always be keys. Tuples can be used
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as keys if they contain only strings, numbers, or tuples; if a tuple contains
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any mutable object either directly or indirectly, it cannot be used as a key.
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You can't use lists as keys, since lists can be modified in place using index
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assignments, slice assignments, or methods like :meth:`append` and
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:meth:`extend`.
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It is best to think of a dictionary as a set of *key: value* pairs,
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with the requirement that the keys are unique (within one dictionary). A pair of
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braces creates an empty dictionary: ``{}``. Placing a comma-separated list of
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key:value pairs within the braces adds initial key:value pairs to the
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dictionary; this is also the way dictionaries are written on output.
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The main operations on a dictionary are storing a value with some key and
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extracting the value given the key. It is also possible to delete a key:value
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pair with ``del``. If you store using a key that is already in use, the old
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value associated with that key is forgotten. It is an error to extract a value
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using a non-existent key.
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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 :keyword:`in` 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
|
|
>>> 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 :func:`dict` 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}
|
|
|
|
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}
|
|
|
|
|
|
.. _tut-loopidioms:
|
|
|
|
Looping Techniques
|
|
==================
|
|
|
|
When looping through dictionaries, the key and corresponding value can be
|
|
retrieved at the same time using the :meth:`items` 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 :func:`enumerate` 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 :func:`zip` 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 :func:`reversed` 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 :func:`sorted` function which
|
|
returns a new sorted list while leaving the source unaltered. ::
|
|
|
|
>>> 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]
|
|
|
|
|
|
.. _tut-conditions:
|
|
|
|
More on Conditions
|
|
==================
|
|
|
|
The conditions used in ``while`` and ``if`` statements can contain any
|
|
operators, not just comparisons.
|
|
|
|
The comparison operators ``in`` and ``not in`` check whether a value occurs
|
|
(does not occur) in a sequence. The operators ``is`` and ``is not`` compare
|
|
whether two objects are really the same object; this only matters for mutable
|
|
objects like lists. 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 cannot occur inside expressions. C
|
|
programmers may grumble about this, but it avoids a common class of problems
|
|
encountered in C programs: typing ``=`` in an expression when ``==`` was
|
|
intended.
|
|
|
|
|
|
.. _tut-comparing:
|
|
|
|
Comparing Sequences and Other Types
|
|
===================================
|
|
|
|
Sequence objects 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 :exc:`TypeError` exception.
|
|
|
|
|
|
.. rubric:: Footnotes
|
|
|
|
.. [1] Other languages may return the mutated object, which allows method
|
|
chaining, such as ``d->insert("a")->remove("b")->sort();``.
|