[译]The Python Tutorial#Data Structures-Python Tutorial-php.cn

<h2 id="译the-python-tutorialdata-structures">[Translation]The Python Tutorial#Data Structures</h2> <h2 id="data-structures">5.1 Data Structures</h2> This chapter details some of the previously introduced content, and will also introduce some new content. <h3 id="more-on-lists">5.1 More on Lists</h3> The list data type has more methods, the following are all methods of the list object: <ul> <li><code>list.append(x)</code> Adds a new item to the end of the list, equivalent to <code>a[len(a):] = [x]</code></li> <li><code>list.extend(iterable)</code> Extend the list by adding all items in the iterable object, equivalent to <code>a[len(a):] = iterable</code></li> <li><code>list.insert(i, x)</code> Inserts an item at the specified position. The first parameter is the element index. The new item will be inserted before this index, so <code>a.insert(0, x)</code> will be inserted at the front of the list, <code>a.insert(len(a ), x)</code> is equivalent to <code>a.append(x)</code>. </li> <li><code>list.remove(x)</code> Removes the first item with value <code>x</code> from the list. If <code>x</code> does not exist, the method throws an exception (<code>ValueError</code>Exception)</li> <li><code>list.pop([i])</code> Removes the item at the specified position from the list and returns. If no index is specified, <code>a.pop()</code> removes and returns the last item in the list. (The square brackets surrounding <code>i</code> in the method signature indicate that the parameter is optional, instead of writing a square bracket at this position. This notation is often used in the Python Library Reference) </li> <li><code>list.clear()</code> Removes all items from the list, equivalent to <code>del a[:]</code></li> <li><code>list.index(x[, start[, end]])</code> Returns the 0-based index of the first item with value <code>x</code>, or throws a <code>ValueError</code> exception if <code>x</code> does not exist. The optional arguments start and end are interpreted as slice notation and are used to limit the search to a specific sublist of the list. The index returned is relative to the full list index, not relative to the start parameter. </li> <li><code>list.count(x)</code> Returns the number of times x appears in the list</li> <li><code>list.sort(key=None, reverse=False)</code> Sort all items in the list (parameters are used to customize the sorting, see <code>sorted()</code> for more information)</li> <li><code>list.reverse()</code> Reverse list elements</li> <li><code>list.copy()</code> Returns a shallow copy of the list, equivalent to <code>a[:]</code></li> </ul> The following is an example demonstrating the list method: <div class="sourceCode"><div class="code" style="position:relative; padding:0px; margin:0px;"><pre class="sourceCode python"><code class="sourceCode python">>>> 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'</code></pre><div class="contentsignin">Copy after login</div></div></div> Such as <code>insert, reverse</code> or <code>sort</code> only change the list but no return value is printed. They return the default <code>None</code>[1 ]. This is a design principle applicable to Python's mutable data structures. <h4 id="using-lists-as-stacks">5.1.1 Using Lists as Stacks</h4> The list method makes it very easy to use it as a stack, and the last element added to the stack is the first to be released (last in, first out). Use the <code>append()</code> method to add elements to the top of the stack, and use <code>pop()</code> without parameters to pop the top element from the stack. Example: <div class="sourceCode"><div class="code" style="position:relative; padding:0px; margin:0px;"><pre class="sourceCode python"><code class="sourceCode python">>>> 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]</code></pre><div class="contentsignin">Copy after login</div></div></div> <h4 id="using-lists-as-queues">5.1.2 Using Lists as Queues</h4> It is also possible to use a list as a queue. The elements added first in the queue are released first (first in, first out); however, using a list in this way is very inefficient. Because adding and removing elements from the end of the list is fast, but inserting or removing elements from the beginning of the list is slow (because all other elements have to be shifted by one). The specially designed <code>collections.deque</code> adds and deletes elements quickly at both ends, and can be used to implement queues. Example: <div class="sourceCode"><div class="code" style="position:relative; padding:0px; margin:0px;"><pre class="sourceCode python"><code class="sourceCode python">>>> 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'])</code></pre><div class="contentsignin">Copy after login</div></div></div> <h4 id="list-comprehensions">5.1.3 List Comprehensions</h4> List comprehensions provide a concise way to create lists. The general application methods are: creating a new list, the list elements are the results of operations on other sequences or iterable objects; or creating a subsequence whose elements meet specific conditions. If you want to create a square list: <div class="sourceCode"><div class="code" style="position:relative; padding:0px; margin:0px;"><pre class="sourceCode python"><code class="sourceCode python">>>> squares = [] >>> for x in range(10): ... squares.append(x**2) ... >>> squares [0, 1, 4, 9, 16, 25, 36, 49, 64, 81]</code></pre><div class="contentsignin">Copy after login</div></div></div> Note that the above creates (or rewrites) a variable named <code>x</code>, which still exists after the loop ends. Use the following method to create a squared list without any side effects: <div class="sourceCode"><div class="code" style="position:relative; padding:0px; margin:0px;"><pre class="sourceCode python"><code class="sourceCode python">squares = list(map(lambda x: x**2, range(10)))</code></pre><div class="contentsignin">Copy after login</div></div></div> Or, etc. used for: <div class="sourceCode"><div class="code" style="position:relative; padding:0px; margin:0px;"><pre class="sourceCode python"><code class="sourceCode python">squares = [x**2 for x in range(10)]</code></pre><div class="contentsignin">Copy after login</div></div></div> This method is more concise and readable. Follows a <code>for</code> clause, followed by zero or more expressions in <code>for</code> clauses or <code>if</code> clauses followed by square brackets, Constitutes a list comprehension. The return result is a new list whose elements are the calculation results of the <code>for</code> and <code>if</code> clauses in the expression. For example, the following list comprehension combines unequal elements in two lists: <div class="sourceCode"><div class="code" style="position:relative; padding:0px; margin:0px;"><pre class="sourceCode python"><code class="sourceCode python">>>> [(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)]</code></pre><div class="contentsignin">Copy after login</div></div></div> Equivalent to: <div class="sourceCode"><div class="code" style="position:relative; padding:0px; margin:0px;"><pre class="sourceCode python"><code class="sourceCode python">>>> 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)]</code></pre><div class="contentsignin">Copy after login</div></div></div> 注意上面两个代码段中<code>for</code>和<code>if</code>语句的顺序是相同的。 如果表达式是一个元组（如上所示的(x, y)），必须将其加上括号。 <div class="sourceCode"><div class="code" style="position:relative; padding:0px; margin:0px;"><pre class="sourceCode python"><code class="sourceCode python">>>> 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]</code></pre><div class="contentsignin">Copy after login</div></div></div> 列表推导式可以包含复杂的表达式甚至嵌套函数： <div class="sourceCode"><div class="code" style="position:relative; padding:0px; margin:0px;"><pre class="sourceCode python"><code class="sourceCode python">>>> from math import pi >>> [str(round(pi, i)) for i in range(1, 6)] ['3.1', '3.14', '3.142', '3.1416', '3.14159']</code></pre><div class="contentsignin">Copy after login</div></div></div> <h4 id="nested-list-comprehensions">5.1.4 Nested List Comprehensions</h4> 列表推导式开头的表达式可以是任意表达式，包括另一个列表推导式。 考虑以下示例，一个包含3个长度为4的列表的列表实现了3x4的矩阵： <div class="sourceCode"><div class="code" style="position:relative; padding:0px; margin:0px;"><pre class="sourceCode python"><code class="sourceCode python">matrix = [ [1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12], ]</code></pre><div class="contentsignin">Copy after login</div></div></div> 以下列表推导式反转行列： <div class="sourceCode"><div class="code" style="position:relative; padding:0px; margin:0px;"><pre class="sourceCode python"><code class="sourceCode python">>>> [[row[i] for row in matrix] for i in range(4)] [[1, 5, 9], [2, 6, 10], [3, 7, 11], [4, 8, 12]]</code></pre><div class="contentsignin">Copy after login</div></div></div> 前面的章节提到，嵌套的列表推导式是在其后跟随的<code>for</code>的上下文中求值的，因此这个示例等同于： <div class="sourceCode"><div class="code" style="position:relative; padding:0px; margin:0px;"><pre class="sourceCode python"><code class="sourceCode python">>>> 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]]</code></pre><div class="contentsignin">Copy after login</div></div></div> 依次等同于： <div class="sourceCode"><div class="code" style="position:relative; padding:0px; margin:0px;"><pre class="sourceCode python"><code class="sourceCode python">>>> 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]]</code></pre><div class="contentsignin">Copy after login</div></div></div> 在实践中，应该选择built-in函数来复合流程语句。在以上的用例中<code>zip()</code>函数更有用： <div class="sourceCode"><div class="code" style="position:relative; padding:0px; margin:0px;"><pre class="sourceCode python"><code class="sourceCode python">>>> list(zip(*matrix)) [(1, 5, 9), (2, 6, 10), (3, 7, 11), (4, 8, 12)]</code></pre><div class="contentsignin">Copy after login</div></div></div> 参见 Unpacking Argument Lists了解关于上面<code>*</code>使用的更多详细信息。 <h3 id="the-del-statement">5.2 The del statement</h3> 在提供列表索引而不是值的情况下，有一种方法可以移除列表中的元素：<code>del</code>语句。这种方式与返回值的<code>pop()</code>方法不同。<code>del</code>语句也可以用来移除部分列表或者清除整个列表（之前使用将空的列表赋值给列表片段的方式实现）。示例： <div class="sourceCode"><div class="code" style="position:relative; padding:0px; margin:0px;"><pre class="sourceCode python"><code class="sourceCode python">>>> 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 []</code></pre><div class="contentsignin">Copy after login</div></div></div> <code>del</code>也可以用来删除整个变量： <div class="sourceCode"><div class="code" style="position:relative; padding:0px; margin:0px;"><pre class="sourceCode python"><code class="sourceCode python">>>> del a</code></pre><div class="contentsignin">Copy after login</div></div></div> 此后引用名字<code>a</code>会抛出异常（至少在其他值赋值给名字<code>a</code>之前）。接下来会有更多<code>del</code>的使用 <h3 id="tuples-and-sequences">5.3 Tuples and Sequences</h3> 列表和字符串有很多常用属性，比如索引和切片操作。它们是序列数据类型（参见 Sequence Types - list, tuple, range）的两种。Python是一种不断进化的语言，其他的序列类型也可以加入。元组是另一种标准的序列数据类型。 元组包含若干由逗号分隔的值，示例： <div class="sourceCode"><div class="code" style="position:relative; padding:0px; margin:0px;"><pre class="sourceCode python"><code class="sourceCode python">>>> 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])</code></pre><div class="contentsignin">Copy after login</div></div></div> 可见，输出的元组总是放在圆括号中，以便于嵌套的元组可以被正确解析；虽然圆括号总是必须的（如果元组是其他更大表达式的一部分），但是在输入元组的时候可以选择使用圆括号。不能对元组的单个项赋值，但是可以创建包含如列表的可变对象的元组。 虽然元组和列表有些相似，但是他们通常以不同的目的，用于不同的场景。元组是不可变的，通常包含不同类型的元素，可以通过拆包操作（参见后续章节）或者索引（或者当元组是命名元组时，甚至可以通过属性来访问）来访问。列表是可变的，通常其元素也是不同类型的，可以通过对列表的迭代访问元素。 构建包含零个或者1个项的元组比较特殊：一种额外的奇怪语法可以适用于这种情况。空元组由一对空的圆括号创建；一个元素的元组由一个跟着逗号的值创建（在圆括号中放入单个值是不够的。译注：这种情况：(1)表示整数而不是元组，使用(1, )表示元组也是可行的）。丑陋但是有效。示例： <div class="sourceCode"><div class="code" style="position:relative; padding:0px; margin:0px;"><pre class="sourceCode python"><code class="sourceCode python">>>> empty = () >>> singleton = 'hello', # <-- note trailing comma >>> len(empty) 0 >>> len(singleton) 1 >>> singleton ('hello',)</code></pre><div class="contentsignin">Copy after login</div></div></div> 语句<code>t = 12345, 54321, 'hello!'</code>是封装元组的一个示例：值<code>12345, 54321</code>和<code>hello!</code>被封装到了一个元组中。逆向操作也是可行的： <div class="sourceCode"><div class="code" style="position:relative; padding:0px; margin:0px;"><pre class="sourceCode python"><code class="sourceCode python">>>> x, y, z = t</code></pre><div class="contentsignin">Copy after login</div></div></div> 非常恰当地称之为序列解包，适用于任何在等号右边的序列（译注：等号右操作数）。序列解包要求等号左边待赋值的变量数量与序列包含元素数目相同。注意多重赋值只是封装元组和序列解包的结合（译注：多重赋值：<code>i, j = 1, 2</code>） <h3 id="set">5.4 Set</h3> Python也包含实现了集合的数据类型。集合是无序不重复的元素集。基本功能包括成员关系测试和重复实体消除。集合对象也支持并集，交集，差集以及对称差集等数学操作。 可以使用花括号和<code>set()</code>函数创建集合。谨记：创建空集合必须使用<code>set</code>函数，不能使用<code>{}</code>，后者用于创建空字典。 以下是简单示范： <div class="sourceCode"><div class="code" style="position:relative; padding:0px; margin:0px;"><pre class="sourceCode python"><code class="sourceCode python">>>> 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'}</code></pre><div class="contentsignin">Copy after login</div></div></div> 与列表推导式相同，Python也支持集合推导式： <div class="sourceCode"><div class="code" style="position:relative; padding:0px; margin:0px;"><pre class="sourceCode python"><code class="sourceCode python">>>> a = {x for x in 'abracadabra' if x not in 'abc'} >>> a {'r', 'd'}</code></pre><div class="contentsignin">Copy after login</div></div></div> <h3 id="dictionaries">5.5 Dictionaries</h3> 另一个内嵌入Python中的数据结构是字典（参见 Mapping Types - dict）。字典在其他一些语言中被称为“联合存储”或者“联合数组”。与序列不同，序列以一系列数字作索引，字典以键作索引，键可以是任何不可变类型；通常使用字符串和数字作为键。只包含字符串，数字或者其他元组的元组也可以作为键；直接或者间接包含可变对象的元组不能作为键。因为列表可以使用索引赋值，切片赋值或者<code>append()</code>以及<code>extend()</code>等方法改变自身，所以列表不能作为键。 最好的理解字典的方式是将其认为是键值对的无序集合，同一集合中键唯一。一对花括号创建空字典：<code>{}</code>。在花括号中放置由逗号分隔键值对列表可以为字典添加初始键值对；这也是字典输出的格式。 字典提供的主要操作是：使用键存储值以及取值。可以使用<code>del</code>删除一个键值对。如果使用已经存在的键来存储值，那么与键关联的旧值会被重写。使用不存在的键来取值会抛出异常。 在字典上执行<code>list(d.keys())</code>返回字典所有键的无序列表（使用<code>sorted(d.keys())</code>使其有序）[2]。使用关键字<code>in</code>检查键在字典中是否存在。 以下是使用字典的示例： <div class="sourceCode"><div class="code" style="position:relative; padding:0px; margin:0px;"><pre class="sourceCode python"><code class="sourceCode python">>>> tel = {'jack': 4098, 'sape': 4139} >>> tel['guido'] = 4127 >>> tel {'sape': 4139, 'guido': 4127, 'jack': 4098} >>> tel['jack'] 4098 >>> del tel['sape'] >>> tel['irv'] = 4127 >>> tel {'guido': 4127, 'irv': 4127, 'jack': 4098} >>> list(tel.keys()) ['irv', 'guido', 'jack'] >>> sorted(tel.keys()) ['guido', 'irv', 'jack'] >>> 'guido' in tel True >>> 'jack' not in tel False</code></pre><div class="contentsignin">Copy after login</div></div></div> <code>dict()</code>构造器直接使用键值对序列构造字典： <div class="sourceCode"><div class="code" style="position:relative; padding:0px; margin:0px;"><pre class="sourceCode python"><code class="sourceCode python">>>> dict([('sape', 4139), ('guido', 4127), ('jack', 4098)]) {'sape': 4139, 'jack': 4098, 'guido': 4127}</code></pre><div class="contentsignin">Copy after login</div></div></div> 此外，字典推导式可以从任意键值表达式中创建字典： <div class="sourceCode"><div class="code" style="position:relative; padding:0px; margin:0px;"><pre class="sourceCode python"><code class="sourceCode python">>>> {x: x**2 for x in (2, 4, 6)} {2: 4, 4: 16, 6: 36}</code></pre><div class="contentsignin">Copy after login</div></div></div> 当键是简单的字符串时，可以使用关键字参数来指定键值对： <div class="sourceCode"><div class="code" style="position:relative; padding:0px; margin:0px;"><pre class="sourceCode python"><code class="sourceCode python">>>> dict(sape=4139, guido=4127, jack=4098) {'sape': 4139, 'jack': 4098, 'guido': 4127}</code></pre><div class="contentsignin">Copy after login</div></div></div> <h3 id="looping-techniques">5.6 Looping Techniques</h3> 遍历字典时，使用<code>items()</code>方法可以同时检索键及其对应的值。 <div class="sourceCode"><div class="code" style="position:relative; padding:0px; margin:0px;"><pre class="sourceCode python"><code class="sourceCode python">>>> knights = {'gallahad': 'the pure', 'robin': 'the brave'} >>> for k, v in knights.items(): ... print(k, v) ... gallahad the pure robin the brave</code></pre><div class="contentsignin">Copy after login</div></div></div> 遍历序列时，使用<code>enumerate()</code>函数可以同时检索位置索引及其对应的值： <div class="sourceCode"><div class="code" style="position:relative; padding:0px; margin:0px;"><pre class="sourceCode python"><code class="sourceCode python">>>> for i, v in enumerate(['tic', 'tac', 'toe']): ... print(i, v) ... 0 tic 1 tac 2 toe</code></pre><div class="contentsignin">Copy after login</div></div></div> 同时遍历两个或者更多序列时，使用<code>zip()</code>函数可以将元素组成对： <div class="sourceCode"><div class="code" style="position:relative; padding:0px; margin:0px;"><pre class="sourceCode python"><code class="sourceCode python">>>> 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.</code></pre><div class="contentsignin">Copy after login</div></div></div> 需要逆序遍历序列时，首先指定一个正向的序列，然后调用<code>reversed()</code>函数： <div class="sourceCode"><div class="code" style="position:relative; padding:0px; margin:0px;"><pre class="sourceCode python"><code class="sourceCode python">>>> for i in reversed(range(1, 10, 2)): ... print(i) ... 9 7 5 3 1</code></pre><div class="contentsignin">Copy after login</div></div></div> 需要以特定顺序遍历序列时，使用<code>sorted()</code>函数返回新的有序序列，原序列不会改动： <div class="sourceCode"><div class="code" style="position:relative; padding:0px; margin:0px;"><pre class="sourceCode python"><code class="sourceCode python">>>> basket = ['apple', 'orange', 'apple', 'pear', 'orange', 'banana'] >>> for f in sorted(set(basket)): ... print(f) ... apple banana orange pear</code></pre><div class="contentsignin">Copy after login</div></div></div> 有时需要在遍历序列的同时修改序列，创建新的替代序列更加简单并且安全： <div class="sourceCode"><div class="code" style="position:relative; padding:0px; margin:0px;"><pre class="sourceCode python"><code class="sourceCode python">>>> 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]</code></pre><div class="contentsignin">Copy after login</div></div></div> <h3 id="more-on-conditions">5.7 More on Conditions</h3> <code>while</code>和<code>if</code>语句中使用的条件可以包含任意操作符，不仅仅是比较运算符。 比较运算符<code>in</code>和<code>not in</code>检查指定值在序列中是否存在（不存在）。操作符<code>is</code>和<code>is not</code>比较两个对象是否真正相同（内存地址比较）；这两个操作符只对像列表那样的可变对象重要。所有的比较运算符拥有相同的优先级，并且都低于数字运算符。 比较运算符可以链接起来。例如<code>a 测试<code>b</code>是否大于<code>a</code>同时<code>b</code>等于<code>c</code>（译注：同其他高级语言的：<code>a ） 比较运算符可以结合布尔运算符<code>and</code>和<code>or</code>使用，比较的结果（或者其他任何布尔表达式）可以使用<code>not</code>来作否定。<code>and</code>,<code>or</code>和<code>not</code>优先级比比较运算符低；其中<code>not</code>的优先级最高而<code>or</code>优先级最低，因此<code>A and not B or C</code>等同于<code>(A and (not B)) or C</code>。一如既往，可以使用圆括号表述想要的优先级顺序。 布尔运算符<code>and</code>和<code>or</code>号称短路运算符：它们的参数从左向右求值，一旦结果确定，求值过程就会停止。例如，如果<code>A</code>和<code>C</code>是真，<code>B</code>是假，<code>A and B and C</code>不会对表达式<code>C</code>求值（译注：<code>A and B</code>为假，已经确定了整个表达式<code>A and B and C</code>的值为假，表达式<code>C</code>的值对结果不会造成影响，因此不会对其求值）。当用作一般值而不是布尔值时，短路操作的返回值是最后一个求值的参数。 可以将比较运算或者布尔表达式赋值给变量： <div class="sourceCode"><div class="code" style="position:relative; padding:0px; margin:0px;"><pre class="sourceCode python"><code class="sourceCode python">>>> string1, string2, string3 = '', 'Trondheim', 'Hammer Dance' >>> non_null = string1 or string2 or string3 >>> non_null 'Trondheim'</code></pre><div class="contentsignin">Copy after login</div></div></div> 注意在Python中，赋值操作不能像C语言一样在表达式内发生。C程序员也许会抱怨，但是这避免了C程序中遇到的一个普遍问题：当想要表示<code>==</code>时候可能误用了<code>=</code>。 <h3 id="comparing-sequences-and-other-types">5.8 Comparing Sequences and Other Types</h3> 相同序列类型之间的序列对象可以相互比较。比较使用字典序：首先比较两个序列的第一项，如果它们不同，比较运算的结果就可确定了；如果它们不同，比较两个序列中的下一个项，以此类推，直到其中一个序列耗尽。如果被比较的两个项是同一类型的，那么使用字典序递归比较。如果两个序列的所有项都是相等的，那么他们相等。如果其中一个序列是另一个序列的子序列，那么短的一个序列较小。字符串的字典序使用Unicode代码点数字排序单个字符。 以下是相同类型的序列对象之间的比较示例： <div class="sourceCode"><div class="code" style="position:relative; padding:0px; margin:0px;"><pre class="sourceCode python"><code class="sourceCode python">(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)</code></pre><div class="contentsignin">Copy after login</div></div></div> 注意当不同类型对象之间有合适的比较方式时，使用<code><</code>或者<code>></code>比较不同类型的对象是合法的。例如，混合数字类型之间的比较是根据其数字上的值，0等于0.0。否则，解释器会抛出<code>TypeException</code>异常，而不是随意提供结果 <h3 id="footnotes">Footnotes</h3> [1] 其他语言可能返回改变后的对象，从而允许方法链接，如：<code>d->insert("a")>remove("b")->sort();</code> [2] 调用<code>d.keys()</code>返回一个dictionary view对象。从而支持如成员关系测试和迭代之类的操作，但是其内容并不是独立于原始字典的，只是一个视图

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[译]The Python Tutorial#Data Structures