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Data Structures


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. Equivalent to a[len(a):] = [x].
list.extend(iterable)
Extend the list by appending all the items from the iterable. Equivalent to a[len(a):] = iterable.
list.insert(ix)
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 x. It is an error 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. (The square brackets around the i in the method signature denote that the parameter is optional, not that you should type square brackets at that position. You will see this notation frequently in the Python Library Reference.)
list.clear()
Remove all items from the list. Equivalent to del a[:].
list.index(x[start[end]])
Return zero-based index in the list of the first item whose value is x. 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=Nonereverse=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. Equivalent to a[:].

An example that uses most of the list methods:

fruits = ['orange', 'apple', 'pear', 'banana', 'kiwi', 'apple', 'banana']
fruits.count('apple')

fruits.count('tangerine')

fruits.index('banana')

fruits.index('banana', 4)  # Find next banana starting a position 4

fruits.reverse()
fruits

fruits.append('grape')
fruits

fruits.sort()
fruits

fruits.pop()

>>> 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 a 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 insertremove 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.

5.1.1. Using Lists as Stacks

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

stack.pop()

stack

stack.pop()

stack.pop()

stack

5.1.2. Using Lists as Queues

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

queue.popleft()                 # The second to arrive now leaves

queue                           # Remaining queue in order of arrival
>>> 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

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

squares

>>> squares = []
>>> for x in range(10):
...     squares.append(x**2)
...
>>> squares
[0, 1, 4, 9, 16, 25, 36, 49, 64, 81]
squares = list(map(lambda x: x**2, range(10)))
squares = [x**2 for x in range(10)]
[(x, y) for x in [1,2,3] for y in [3,1,4] if x != y]
>>> [(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)]
combs = []
for x in [1,2,3]:
    for y in [3,1,4]:
        if x != y:
            combs.append((x, y))

combs
>>> 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)]
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]

# filter the list to exclude negative numbers
[x for x in vec if x >= 0]

# apply a function to all the elements
[abs(x) for x in vec]

# call a method on each element
freshfruit = ['  banana', '  loganberry ', 'passion fruit  ']
[weapon.strip() for weapon in freshfruit]

# create a list of 2-tuples like (number, square)
[(x, x**2) for x in range(6)]

# the tuple must be parenthesized, otherwise an error is raised
[x, x**2 for x in range(6)]




# 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]
>>> 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, in <module>
    [x, x**2 for x in range(6)]
               ^
SyntaxError: invalid syntax
>>> # 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)]
>>> 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

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],
]
>>> 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)]
>>> [[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 nested listcomp 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
>>> 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
>>> 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))

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

					
				

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