Numpy, the Python foundation for number crunching

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3 likes•1,737 views

Numpy is the Python foundation for number crunching. It provides a high-level API, interactivity, visualization, and performance for numerical computing while also allowing low-level access. It uses a simple but powerful memory model and array data structure. Numpy powers many scientific computing libraries in Python and is demonstrated through examples of its API, memory management capabilities using strides, and extensions that build on it like TA-Lib for financial analysis.

Numpy, the Python
foundation for number
crunching

Didrik Pinte, Enthought
London Data Science meetup

Monday 22 October 2012

Number crunching?

•High-level api
•Interactivity & visualization
•Performance
•Low-level access

Monday 22 October 2012

Evidence ?
Clyther

PyOpenGL
PyCuda

PyAlgoTrade

PyTrilinos

Joblib numpy-boost

petsc4py

Monday 22 October 2012

Why then?

•The API ...
•Simple but powerful memory model
•Open access to the data

Monday 22 October 2012

Array data structure

Monday 22 October 2012

Let’s look at the code!

•Examples:
– API / interactivity
– memory management with stride_tricks
– pikos
– extensions with talib
–(joblib, memmap/multiprocessing,
ipython //)

Monday 22 October 2012

API / interactivity

Monday 22 October 2012

Memory management

1 2 3 4 5 6 7 8 9 10 11 12

=

1 2 3 4

2 3 4 5

3 4 5 6

4 5 6 7

5 6 7 8

6 7 8 9

7 8 9 10

8 9 10 11
...

Monday 22 October 2012

Memory management

Shape 12,
Strides 8,
1 2 3 4 5 6 7 8 9 10 11 12

=

1 2 3 4
Shape 9,4, 2 3 4 5
Strides 8,8 3 4 5 6

4 5 6 7

5 6 7 8

6 7 8 9

7 8 9 10

8 9 10 11
...

Monday 22 October 2012

Memory management - pikos

Monday 22 October 2012

Memory management - chaco

Monday 22 October 2012

Low level access
%timeit talib.moving_average(adj_close, optInTimePeriod=5)
100000 loops, best of 3: 7.67 us per loop

%timeit as_strided(adj_close, shape=(len(adj_close)-4, 5), strides=(8, 8)).mean
(axis=1)
10000 loops, best of 3: 28.2 us per loop

Monday 22 October 2012

Conclusion

•It’s obvious, no?

Monday 22 October 2012

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Numpy, the Python foundation for number crunching

1. Numpy, the Python foundation for number crunching Didrik Pinte, Enthought London Data Science meetup Monday 22 October 2012

2. Number crunching? •High-level api •Interactivity & visualization •Performance •Low-level access Monday 22 October 2012

3. Evidence ? Clyther PyOpenGL PyCuda PyAlgoTrade PyTrilinos Joblib numpy-boost petsc4py Monday 22 October 2012

4. Evidence ? Clyther PyOpenGL PyCuda PyAlgoTrade PyTrilinos Joblib numpy-boost petsc4py Monday 22 October 2012

5. Why then? •The API ... •Simple but powerful memory model •Open access to the data Monday 22 October 2012

6. Array data structure Monday 22 October 2012

7. Let’s look at the code! •Examples: – API / interactivity – memory management with stride_tricks – pikos – extensions with talib –(joblib, memmap/multiprocessing, ipython //) Monday 22 October 2012

8. API / interactivity Monday 22 October 2012

9. Memory management 1 2 3 4 5 6 7 8 9 10 11 12 = 1 2 3 4 2 3 4 5 3 4 5 6 4 5 6 7 5 6 7 8 6 7 8 9 7 8 9 10 8 9 10 11 ... Monday 22 October 2012

10. Memory management Shape 12, Strides 8, 1 2 3 4 5 6 7 8 9 10 11 12 = 1 2 3 4 Shape 9,4, 2 3 4 5 Strides 8,8 3 4 5 6 4 5 6 7 5 6 7 8 6 7 8 9 7 8 9 10 8 9 10 11 ... Monday 22 October 2012

11. Memory management - pikos Monday 22 October 2012

12. Memory management - chaco Monday 22 October 2012

13. Low level access %timeit talib.moving_average(adj_close, optInTimePeriod=5) 100000 loops, best of 3: 7.67 us per loop %timeit as_strided(adj_close, shape=(len(adj_close)-4, 5), strides=(8, 8)).mean (axis=1) 10000 loops, best of 3: 28.2 us per loop Monday 22 October 2012

14. Conclusion •It’s obvious, no? Monday 22 October 2012

15. Q&A? Monday 22 October 2012

Numpy, the Python foundation for number crunching

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