Выступление на PYCON RUSSIA 2015

1. Numpy: Vectorize your brain
Ekaterina Tuzova

2. K nearest neighbors
https://archive.ics.uci.edu/ml/datasets/Wine

3. NumPy

4. What is NumPy?
Numpy is the fundamental package for
scientific computing with Python.

5. IPython

6. Python and Performance

7. Python is fast

8. Python is slow

9. Euclidian distance

10. “Magic” timeit

11. Euclidian distance. C

12. Euclidian distance. C

13. Euclidian distance

14. line_profiler and “magic” lprun

15. Euclidian distance

16. Compiled languages

17. Interpreted languages

18. What can be done?

19. NumPy

20. Ufuncs

21. Universal functions
Special type of function defined within a
numpy library and it operate element-wise
on arrays.

22. Arithmetic operations

23. Arithmetic operations

24. Arithmetic operations

25. Arithmetic operations

26. Ufuncs available
- Arithmetic
- Bitwise
- Comparison
- Trigonometric
- Floating
…

27. Slicing and indexing

28. Slicing and indexing

29. Slicing and indexing

30. Multidimensional arrays

31. Multidimensional arrays

32. Index arrays

33. Index arrays

34. Index arrays

35. Masking

36. Masking

37. Test train split

38. Test train split

39. Broadcasting

40. Broadcasting
Broadcasting describes how NumPy treats
arrays with different shapes during
arithmetic operations.

41. Array shape and dimension

42. Broadcasting rules
1. If two arrays differ in their number of dimension,
the shape of the array with the fewer dimensions
is padded with ones on it’s leading(left) size.
2. If the shape of two arrays doesn’t match in any
dimension, the array with shape equal to 1 in that
dimension is stretched to match the other shape.
3. If these conditions are not met, raise a
ValueError: operands could not be broadcast
together with shapes

43. Broadcasting. Example

44. np.newaxis

45. np.newaxis

46. np.newaxis

47. Aggregations

48. Aggregations

49. Aggregations

50. NumPy resume
Basic ideas to make you code faster:
- Ufuncs
- Slicing and indexing
- Broadcasting
- Aggregations

51. k-means

52. Algorithm
1. Select k points at random as cluster centers.
2. Assign objects to their closest cluster center
according to the Euclidean distance
function.
3. Calculate the centroid or mean of all objects
in each cluster.
4. Repeat steps 2, 3 and 4 until the same points
are assigned to each cluster in consecutive
rounds.

53. Synthetic data

55. Vectorized euclidian distance

56. k-means

58. Thank you.
@ktisha