計算名字 list 中開頭字母的人數

所謂小題大作就是這個概念？我真的不知道簡介要寫什麼。

如果你真的想知道為什麼 .get 很慢，請看 http://www.slideshare.net/ya790026/why-is-a1-fast-than-aget1

Transcript

2. Python 尝剣 switch
3. Guido: 欽 dict
4. counts  =  {}   for  name  in  namelist:          if  name[0]  not  in  counts:                  counts[name[0]]  =  1          else:                  counts[name[0]]  +=  1   for  key  in  counts:          count  =  counts[key]
5. LBYL v. EAFP
6. Easier to ask for forgiveness than permission.
7. counts  =  {}   for  name  in  namelist:          try:                  counts[name[0]]  +=  1          except  KeyError:                  counts[name[0]]  =  1   for  key  in  counts:          count  =  counts[key]
8. counts  =  {}   for  name  in  namelist:          val  =  counts.get(name[0],  0)          counts[name[0]]  =  val  +  1   for  key  in  counts:          count  =  counts[key]
9. from  collections  import  defaultdict   counts  =  defaultdict(lambda:  0)   for  name  in  namelist:          counts[name[0]]  +=  1   for  key  in  counts:          count  =  counts[key]
10. from  collections  import  Counter   counts  =  Counter(          name[0]  for  name  in  namelist   )   for  key  in  counts:          count  =  counts[key]
11. from  itertools  import  groupby   for  k,  v  in  groupby(                  sorted(namelist),                    key=lambda  n:  n[0]):          count  =  len(list(v))
12. from  itertools  import  groupby   for  k,  v  in  groupby(                  sorted(namelist),                    key=lambda  n:  n[0]):          count  =  len(list(v)) 馄䌃
13. Not Really.
14. counts  =  {}   for  name  in  namelist:          if  name[0]  not  in  counts:                  counts[name[0]]  =  1          else:                  counts[name[0]]  +=  1   for  key  in  counts:          count  =  counts[key] Loop
15. for  k,  v  in  groupby(                  sorted(namelist),                    key=lambda  n:  n[0]):          count  =  len(list(v)) Loop Implicit loop
16. Let’s Measure!
17. • name = str(random.random())[2:] • Exclude import overhead • Benchmarking against dict + LBYL
19. ♶䠐㢩
20. 侶ꆀ䖍㢳Counter 䩝剙㥨欽
21. ⡎〪銲欽ⵋ EAFP 莃 Ⰹ䒉䊨Ⱗ穠卓鿪䊴♶㢳
22. EAFP ⮚倳 LBYL
23. .get 㥨䢧
24. WHY?
25. • try is cheap, except is (more) expensive • If you hit often, use try-except • If you fail often, use .get
26. WHY?
27. Use the source, Luke!
28. .__getitem__ • Check argument type (string optimisation) • Value lookup • Return value on success • Extra checks and KeyError on failure .get • Argument unpacking
 • Value lookup • Return the value on success • Return default value on failure
29. .__getitem__ • Check argument type (string optimisation) • Value lookup • Return value on success • Extra checks and KeyError on failure .get • Argument unpacking
 • Value lookup • Return the value on success • Return default value on failure Super slow! Super fast!
30. • .get is generally much slower, due to argument unpacking • If you fail often, .get’s default value might make up speed
31. Python function calls are slow.
32. Fin.