数据结构：List常用操作

Reference：Python官方Document Tutorial
https://docs.python.org/3/reference/datamodel.html#object.__contains__
This chapter describes some things you’ve learned about already in more detail, and adds some new things as well.

In general：
5.1.0list常用方法， 

5.1.1. Using Lists as Stacks

5.1.2. Using Lists as Queues

5.1.3. List Comprehensions

5.1.4. Nested List Comprehensions

4000

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

5.1.0 List常用方法

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

(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 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=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. Equivalent 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 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 

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.

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)]

# 语法  [expression，for 判断语句  (if 判断语句)]

其中expression为每个迭代对象的返回值， if（如果有）在for后面

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, 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)]
['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 nested listcomp is evaluated in the context of the 

for

that
follows it, so this example is equivalent to:

# 不用列表解析，而是用for循环代替实现：

>>>

>>> 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:

# 特殊：要记住！！

>>>

>>> 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.