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# NumPyの歴史とPythonの並行処理【PyData.tokyo One-day Conference 2018】

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### NumPyの歴史とPythonの並行処理【PyData.tokyo One-day Conference 2018】

1. 1. 1
2. 2. !2
3. 3. !3
4. 4. !4
5. 5. "Python has arguably become the de facto standard for exploratory, interactive, and computation-driven scientific research” !5
6. 6. !6
7. 7. !7
8. 8.   !8
9. 9. !9
10. 10. •L[i, j, k] •L[1:2, ..., 2:3] L[i][j][k] L[i,j,k] !10
11. 11. A = A*2 A *= 2 !11
12. 12. >>> y = (x >= 0.5) >>> y array([ True, False, False, True, False]) >>> z[y] array([0.23273493, 0.99925692, 0.85635559]) !12
13. 13. >>> A =[1, 2, 3] >>> A[1] 2 >>> A[np.int64(1)] 2 !13
14. 14. !14
15. 15. A @ B np.matmul(A, B) !15
16. 16.
17. 17. Stack Overflow Trends !17
18. 18. !18
19. 19. !19
20. 20. !20
21. 21. from concurrent.futures import * def fib(n): if n < 2: return n return fib(n-2) + fib(n-1) with ProcessPoolExecutor() as e: fibs = e.map(fib, range(10)) for v in fibs: print(v) !21
22. 22. !22
23. 23. !23
24. 24. def func(n): s = 0 for i in range(n): s += i with ThreadPoolExecutor() as e: e.map(func, range(3)) !24
25. 25. ⇛ ⇛ !25
26. 26. def func(): while True: s = 0 for i in range(n): s += i print(s) !26
27. 27. !27
28. 28. • 10*10, 100*100 40000 • 1000*1000 400 • 1~9 !28
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32. 32. • 10*10, 100*100, 1000*1000 200 • 1~9 !32
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50. 50. !50