1. Listcomps (List comprehensions) do everything the map and filter functions do. (map 函数和 filter函数能做的，列表生成式都可以做)
列表生成式和 map, filter 函数运行时间的比较示例：
import timeit TIMES = 10000 SETUP = """ symbols = '$¢£¥€¤' def non_ascii(c): return c > 127 """ timeit.timeit() def clock(label, cmd): res = timeit.repeat(cmd, setup=SETUP, number=TIMES) print(label, *('{:.3f}'.format(x) for x in res)) clock('listcomp :', '[ord(s) for s in symbols if ord(s) > 127]') clock('listcomp + func :', '[ord(s) for s in symbols if non_ascii(ord(s))]') clock('filter + lambda :', 'list(filter(lambda c: c > 127, map(ord, symbols)))') clock('filter + func :', 'list(filter(non_ascii, map(ord, symbols)))') # 测试一段代码的运行时间，可以用 timeit 模块 """ timeit 模块中主要函数有两个： 1. timeit(stmt='pass', setup='pass', timer=<defaulttimer>, number=1000000) 返回： 返回执行stmt这段代码number遍所用的时间，单位为秒，float型 参数： stmt：要执行的那段代码 setup：执行代码的准备工作，不计入时间，一般是import之类的 timer：这个在win32下是time.clock()，linux下是time.time()，默认的，不用管 number：要执行stmt多少遍 2. repeat(stmt='pass', setup='pass', timer=<defaulttimer>, repeat=3, number=1000000) 这个函数比timeit函数多了一个repeat参数而已，表示重复执行timeit这个过程多少遍，返回一个列表，表示执行每遍的时间；repeat默认为3 """
timeit 参考链接：https://www.cnblogs.com/itcomputer/articles/4578769.html
2. Tuple
In [44]: traveler_ids = [('USA','31195855'), ('BRA','CE342567'), ('ESP', 'XDA205856')] In [45]: for passport in sorted(traveler_ids): ...: print('%s/%s' % passport) ...: BRA/CE342567 ESP/XDA205856 USA/31195855 # The % formatting operator understands tuples and treats each item as a separate field. # Tuple Unpacking In [46]: t = (20, 8) In [47]: divmod(*t) Out[47]: (2, 4) In [48]: quotient,remainder = divmod(*t) In [49]: quotient, remainder Out[49]: (2, 4) # Using * to grab excess items In [50]: a, b, *rest = range(5) In [51]: a, b, rest Out[51]: (0, 1, [2, 3, 4]) In [52]: a, b, *rest = range(2) In [53]: a, b, rest Out[53]: (0, 1, []) In [54]: a, *body, c, d = range(5) In [55]: a, body, c, d Out[55]: (0, [1, 2], 3, 4) # In the context of parallel assignment, the * prefix can be applied to exactly one variable, but it can appear in any position # Nested Tuple Unpacking metro_areas = [ ('Tokyo', 'JP', 36.933, (35.689722, 139.691667)), ('Mexico City', 'MX', 20.142, (19.433333, -99.133333)), ('New York-Newark', 'US', 20.104, (40.808611, -74.020386)), ] print('{:15} | {:^9} | {:^9}'.format('', 'lat.', 'long.')) fmt = '{:15} | {:9.4f} | {:9.4f}' # By assigning the last field to a tuple, we unpack the coordinates. for name, cc, pop, (latitude, longitude) in metro_areas: if longitude <= 0: print(fmt.format(name, latitude, longitude)) # Output: """ | lat. | long. Mexico City | 19.4333 | -99.1333 New York-Newark | 40.8086 | -74.0204 """
字符串格式化参考链接： https://www.cnblogs.com/songdanlee/p/11105807.html
3. Named Tuples
from collections import namedtuple # Build a namedtuple example 1: # Card = namedtuple('Card', ['rank', 'suit']) # 【Two parameters】 are required to create a named tuple: 【a class name】 and a 【list of field names】, which can be given by # an 【iterable of strings】 or as 【a single space-delimited string】 In [57]: from collections import namedtuple In [58]: City = namedtuple('City', 'name country population coordinates') # Data must be passed as positional arguments to the constructor. In [59]: tokyo = City('Tokyo', 'JP', 36.933, (35.689722, 139.691667)) In [60]: tokyo Out[60]: City(name='Tokyo', country='JP', population=36.933, coordinates=(35.689722, 139.691667)) In [61]: tokyo.population Out[61]: 36.933 In [62]: tokyo.coordinates Out[62]: (35.689722, 139.691667) # You can access the fields by name or position In [63]: tokyo[1] Out[63]: 'JP' In [64]: In [64]: # named tuple attributes: _fields -- class attribute; _make(iterable) -- class method; _asdict() -- instance method In [64]: City._fields Out[64]: ('name', 'country', 'population', 'coordinates') In [65]: LatLong = namedtuple('LatLong', 'lat long') In [66]: delhi_data = ('Delhi NCR', 'IN', 21.935, LatLong(28.613889, 77.208889)) In [67]: delhi = City._make(delhi_data) In [68]: delhi Out[68]: City(name='Delhi NCR', country='IN', population=21.935, coordinates=LatLong(lat=28.613889, long=77.208889)) In [69]: delhi._asdict() Out[69]: OrderedDict([('name', 'Delhi NCR'), ('country', 'IN'), ('population', 21.935), ('coordinates', LatLong(lat=28.613889, long=77.208889))]) In [70]: for key, val in delhi._asdict().items(): ...: print(key + ':', val) ...: name: Delhi NCR country: IN population: 21.935 coordinates: LatLong(lat=28.613889, long=77.208889) """ _fields is a tuple with the field names of the class. _make() allow you to instantiate a named tuple from an iterable; City(*delhi_data) would do the same. _asdict() returns a collections.OrderDict built from the named tuple instance. """
4. Slicing（切片）
4.1 Why Slices and Range Exclude the Last Item ?
""" The Pythonic convention of excluding the last item in slices and ranges works well with the zero-based indexing used in Python,C and many other languages. Some convenient features of the convention are : 1. It's easy to see the length of a slice or range when only the stop position is given: range(3) and my_list[:3] both produce three items. 2. It's easy to compute the length of a slice or range when start and stop are given: just subtract stop - start. 3. It's easy to split a sequence in two parts at any index x, without overlapping: simply get my_list[:x] and my_list[x:]. """
4.2 Slice Object
示例： Line items from a flat-file invoice
invoice = """ 0.....6.................................40...........52...55........ 1909 Pimoroni PiBrella $17.50 3 $52.50 1489 6mm Tactile Switch x20 $4.95 2 $9.90 1510 Panavise Jr. -PV -201 $28.00 1 $28.00 1601 PiTFT Mini Kit 320x240 $34.95 1 $34.95 """ # assign name to slices SKU = slice(0, 6) DESCRIPTION = slice(6, 40) UNIT_PRICE = slice(40, 52) QUANTITY = slice(52, 55) ITEM_TOTAL = slice(55, None) line_items = invoice.split(' ') print(line_items) """ Output: ['', '0.....6.................................40...........52...55........', '1909 Pimoroni PiBrella $17.50 3 $52.50', '1489 6mm Tactile Switch x20 $4.95 2 $9.90', '1510 Panavise Jr. -PV -201 $28.00 1 $28.00', '1601 PiTFT Mini Kit 320x240 $34.95 1 $34.95', ''] """ line_items = invoice.split(' ')[2:] for item in line_items: print(item[UNIT_PRICE], item[DESCRIPTION]) # sequence[slice] """ Output: $17.50 Pimoroni PiBrella $4.95 6mm Tactile Switch x20 $28.00 Panavise Jr. -PV -201 $34.95 PiTFT Mini Kit 320x240 """ """ 解释： The notation a:b:c is only valid within [] when used in the indexing or subscript operator, and it produces a slice object:slice(a, b, c). To evaluate the expression seq[start:stop:step], Python calls seq.__getitem__(slice(start, stop, step)) """
4.3 Building Lists of Lists (Using + and * with Sequences)
""" To initialize a list of lists as my_list = [[]] * 3 will result in a list with three references to the same inner list. e.g. In [47]: my_list = [[]] * 3 In [48]: my_list Out[48]: [[], [], []] # Out[48] 中的三个 [] 指向同一个内存地址 In [49]: my_list[0] is my_list[1] and my_list[1] is my_list[2] Out[49]: True """ """ The best way of initializing a list with a certain number of nested lists is with a list comprehension. 生成列表中嵌套列表 最好的方式是 列表生成式 """ # Right example: # Example 2-12 board = [[] for i in range(3)] print(board) # 输出： # [[], [], []] print(board[0] is board[1] and board[1] is board[2]) # 输出： # False # False 表示 生成的三个空列表不是指向同一个内存地址（这正是我们想要的） board[1].append('a') print(board) # 输出： # [[], ['a'], []] # Wrong example: # Example 2-13 weird_board = [[]] * 3 print(weird_board) # 输出： # [[], [], []] print(weird_board[0] is weird_board[1] and weird_board[1] is weird_board[2]) # 输出： # True # 上面的 True 表示 三个空列表指向了同一个内存地址 weird_board[1].append('a') print(weird_board) # 输出： # [['a'], ['a'], ['a']] # 上述的错误例子 Example 2-13 等价于下面的操作： row = [] board_wrong = [] for i in range(3): board_wrong.append(row) # The same row is appended three times to board_wrong. print(board_wrong) # 输出： # [[], [], []] # 上述正确的例子 Example 2-12 等价于下面的操作： board_right = [] for i in range(3): row = [] # 每次 for 循环时都对 row 重新赋值 board_right.append(row) # Each iteration builds a new row and appends it to borad_right. board_right[1].append('b') print(board_right) # 输出： # [[], ['b'], []]
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