lis中的平均时间增量

2024-03-29 08:08:40 发布

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我想计算列表中日期之间的平均时间增量。 尽管下面的方法很好,但我想知道是否有更聪明的方法?

delta = lambda last, next: (next - last).seconds + (next - last).days * 86400   
total = sum(delta(items[i-1], items[i]) for i in range(1, len(items)))
average = total / (len(items) - 1)

Tags: 方法lambda列表forlen时间itemsdays
2条回答

顺便说一句,如果你有一个时间增量或日期时间的列表,你为什么还要自己做数学计算呢?

datetimes = [ ... ]

# subtracting datetimes gives timedeltas
timedeltas = [datetimes[i-1]-datetimes[i] for i in range(1, len(datetimes))]

# giving datetime.timedelta(0) as the start value makes sum work on tds 
average_timedelta = sum(timedeltas, datetime.timedelta(0)) / len(timedeltas)

试试这个:

from itertools import izip

def average(items):   
    total = sum((next - last).seconds + (next - last).days * 86400
                for next, last in izip(items[1:], items))
     return total / (len(items) - 1)

在我看来,这样做更有可读性。对不太喜欢数学的代码读者的评论可能有助于解释如何计算每个增量。值得一提的是,一个生成器表达式的操作码指令最少(我认为最慢)。

  # The way in your question compiles to....
  3           0 LOAD_CONST               1 (<code object <lambda> at 0xb7760ec0, file 

"scratch.py", line 3>)
              3 MAKE_FUNCTION            0
              6 STORE_DEREF              1 (delta)

  4           9 LOAD_GLOBAL              0 (sum)
             12 LOAD_CLOSURE             0 (items)
             15 LOAD_CLOSURE             1 (delta)
             18 BUILD_TUPLE              2
             21 LOAD_CONST               2 (<code object <genexpr> at 0xb77c0a40, file "scratch.py", line 4>)
             24 MAKE_CLOSURE             0
             27 LOAD_GLOBAL              1 (range)
             30 LOAD_CONST               3 (1)
             33 LOAD_GLOBAL              2 (len)
             36 LOAD_DEREF               0 (items)
             39 CALL_FUNCTION            1
             42 CALL_FUNCTION            2
             45 GET_ITER            
             46 CALL_FUNCTION            1
             49 CALL_FUNCTION            1
             52 STORE_FAST               1 (total)

  5          55 LOAD_FAST                1 (total)
             58 LOAD_GLOBAL              2 (len)
             61 LOAD_DEREF               0 (items)
             64 CALL_FUNCTION            1
             67 LOAD_CONST               3 (1)
             70 BINARY_SUBTRACT     
             71 BINARY_DIVIDE       
             72 STORE_FAST               2 (average)
             75 LOAD_CONST               0 (None)
             78 RETURN_VALUE        
None
#
#doing it with just one generator expression and itertools...

  4           0 LOAD_GLOBAL              0 (sum)
              3 LOAD_CONST               1 (<code object <genexpr> at 0xb777eec0, file "scratch.py", line 4>)
              6 MAKE_FUNCTION            0

  5           9 LOAD_GLOBAL              1 (izip)
             12 LOAD_FAST                0 (items)
             15 LOAD_CONST               2 (1)
             18 SLICE+1             
             19 LOAD_FAST                0 (items)
             22 CALL_FUNCTION            2
             25 GET_ITER            
             26 CALL_FUNCTION            1
             29 CALL_FUNCTION            1
             32 STORE_FAST               1 (total)

  6          35 LOAD_FAST                1 (total)
             38 LOAD_GLOBAL              2 (len)
             41 LOAD_FAST                0 (items)
             44 CALL_FUNCTION            1
             47 LOAD_CONST               2 (1)
             50 BINARY_SUBTRACT     
             51 BINARY_DIVIDE       
             52 RETURN_VALUE        
None

特别是,除去lambda允许我们避免创建闭包、构建元组和加载两个闭包。无论哪种方式都会调用五个函数。当然,这种对表演的关注有点荒谬,但很高兴知道幕后发生了什么。最重要的是可读性,我认为这样做的得分也很高。

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