5. Data Structures
******************

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


5.1. More on Lists
==================

The list data type has some more methods. Here are all of the methods
of list objects:

list.append(x)

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

list.extend(iterable)

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

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)".

list.remove(x)

   Remove the first item from the list whose value is equal to *x*.
   It raises a "ValueError" if there is no such item.

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" if the list is empty or
   the index is outside the list range.

list.clear()

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

list.index(x[, start[, end]])

   Return zero-based index of the first occurrence of *x* in the list.
   Raises a "ValueError" 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.

list.count(x)

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

list.sort(*, key=None, reverse=False)

   Sort the items of the list in place (the arguments can be used for
   sort customization, see "sorted()" for their explanation).

list.reverse()

   Reverse the elements of the list in place.

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.


5.1.1. Using 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 "append()".  To
retrieve an item from the top of the stack, use "pop()" 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]


5.1.2. Using 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" 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'])


5.1.3. List Comprehensions
--------------------------

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" clause, then zero or more "for" or "if" clauses.
The result will be a new list resulting from evaluating the expression
in the context of the "for" and "if" 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" and "if" 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']


5.1.4. 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" 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()" 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 Unpacking Argument Lists for details on the asterisk in this line.


5.2. The "del" statement
========================

There is a way to remove an item from a list given its index instead
of its value: the "del" statement.  This differs from the "pop()"
method which returns a value.  The "del" 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" 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" later.


5.3. Tuples and Sequences
=========================

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 Sequence Types --- list, tuple, range).  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])

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*, 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"). Lists
are *mutable*, 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.


5.4. Sets
=========

Python also includes a data type for sets.  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()" 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, set comprehensions are also
supported:

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


5.5. Dictionaries
=================

Another useful data type built into Python is the *dictionary* (see
Mapping Types --- dict). 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 "append()"
and "extend()".

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". To avoid getting this error when trying to access
a possibly non-existent key, use the "get()" 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" 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()" 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}


5.6. Looping Techniques
=======================

When looping through dictionaries, the key and corresponding value can
be retrieved at the same time using the "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 "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 "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 "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 "sorted()" 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()" on a sequence eliminates duplicate elements. The use of
"sorted()" in combination with "set()" 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]


5.7. 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" 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 ":=". This avoids a common
class of problems encountered in C programs: typing "=" in an
expression when "==" was intended.


5.8. Comparing Sequences and Other Types
========================================

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"
exception.

-[ Footnotes ]-

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