Python & Numpy A tutorial

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1 Python & Numpy A tutorial Devert Alexandre School of Software Engineering of USTC 13 February 2012 Slide 1/38

2 Table of Contents 1 Why Python & Numpy 2 First steps with Python 3 Fun with lists 4 Quick tour of Numpy 5 Conclusion Devert Alexandre (School of Software Engineering of USTC) Python & Numpy Slide 2/38

3 Why Python I will provide code examples in Python All the algorithms I will introduce 150 lines Works on Windows, Linux, Mac,... It s easy to read It s free It s fun Best way to learn it program something for fun with it Devert Alexandre (School of Software Engineering of USTC) Python & Numpy Slide 3/38

4 Why Python Numpy is Python module to do scientific computing Helps to have short & easy to read code With practice, about same speed as C Increasingly popular in comp. sci. Devert Alexandre (School of Software Engineering of USTC) Python & Numpy Slide 4/38

5 Table of Contents 1 Why Python & Numpy 2 First steps with Python 3 Fun with lists 4 Quick tour of Numpy 5 Conclusion Devert Alexandre (School of Software Engineering of USTC) Python & Numpy Slide 5/38

6 Printing messages Your first program in Python p r i n t H e l l o, H e f e i! It will print Hello, Hefei! Devert Alexandre (School of Software Engineering of USTC) Python & Numpy Slide 6/38

7 Printing messages Let s use some variables name = A l e x age = p r i n t name, your age i s, age It will print Alex your age i s 31 Devert Alexandre (School of Software Engineering of USTC) Python & Numpy Slide 7/38

8 Printing messages You can also use something like printf in C name = A l e x age = p r i n t %s, your age i s %d % ( name, age ) It will print Alex, your age i s 31 Devert Alexandre (School of Software Engineering of USTC) Python & Numpy Slide 8/38

9 Variables Python defines a few default types a = I am a s t r i n g! # Those a r e s t r i n g s b = Me too ˆˆ c = 42 # This an i n t e g e r number d = # This i s a f l o a t i n g p o i n t number e = [ 7, 6, 2, 1, ORLY, 5 ] # This i s a l i s t f = ( 7, 6, 2, 1, ORLY, 5) # This i s a t u p l e g = { name : A l e x, age : 31} # This a d i c t i o n a r y Lists, tuples and dictionaries will be introduced later Devert Alexandre (School of Software Engineering of USTC) Python & Numpy Slide 9/38

10 Numbers Numbers works like in the C language a = 3 b = 5 c = ( a + 5 b ) / ( a ) csquare = c 2 # Same as : csquare = c c Devert Alexandre (School of Software Engineering of USTC) Python & Numpy Slide 10/38

11 Math module If you need common math functions, you should use the math module i m p o r t math x = math. s i n ( 0. 7 math. e ) y = math. c o s ( 0. 3 math. p i ) norm = math. s q r t ( x 2 + y 2) A module adds new functions and types Devert Alexandre (School of Software Engineering of USTC) Python & Numpy Slide 11/38

12 Conditionals Conditionals in Python i f b == 4 : p r i n t I do t h i s i f a == b : p r i n t I do t h i s e l s e : p r i n t I do t h a t i f a == c and c < 0. 0 : p r i n t I do t h i s e l i f a > 2. 0 c o r a + c < a 2 : p r i n t I do t h a t e l s e : p r i n t I do what? Note that indentation is important in Python Devert Alexandre (School of Software Engineering of USTC) Python & Numpy Slide 12/38

13 Loops Loops in Python w h i l e math. f a b s ( a ) < 1. 0 : a = b b += c 2 w h i l e b > 0. 0 : b /= c + a i f b == 1. 0 : b r e a k Note that indentation is really important in Python Devert Alexandre (School of Software Engineering of USTC) Python & Numpy Slide 13/38

14 Functions Function in Python def saysomething ( ) : p r i n t do t h i s def harmonicsum ( a, b ) i = a s = 0. 0 w h i l e i < b : s += 1. 0 / ( i + 1) i += 1 r e t u r n s saysomething ( ) p r i n t harmonicsum ( 0, 42) Note that indentation is really, really important in Python Devert Alexandre (School of Software Engineering of USTC) Python & Numpy Slide 14/38

15 Table of Contents 1 Why Python & Numpy 2 First steps with Python 3 Fun with lists 4 Quick tour of Numpy 5 Conclusion Devert Alexandre (School of Software Engineering of USTC) Python & Numpy Slide 15/38

16 Lists Lists will be important for us, as a dataset is made of list of data a = [ ] # Empty l i s t a = [ 1, 2, 3 ] # A l i s t w i t h 3 numbers a = [ [ 1, 2 ], [ 3, 4 ], [ 5, 6, 7 ] ] # A l i s t c o n t a i n i n g l i s t s a = [ H e l l o, 42, [ LOL, OMG ] ] # A l i s t can c o n t a i n a n y t h i n g Devert Alexandre (School of Software Engineering of USTC) Python & Numpy Slide 16/38

17 List of integers The function range build lists of integers p r i n t r a n g e ( 1 0 ) p r i n t r a n g e ( 5, 10) p r i n t r a n g e ( 1 5, 0, 2) It will print [ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9 ] [ 5, 6, 7, 8, 9 ] [ 1 5, 13, 11, 9, 7, 5, 3, 1 ] Devert Alexandre (School of Software Engineering of USTC) Python & Numpy Slide 17/38

18 Iterating You can iterate through a list like this s = [ H e f e i, Suzhou, N a n j i n g, Kunming, Chengdu ] f o r s i n mylist : p r i n t H e l l o,, s,! Exactly like a loop... Devert Alexandre (School of Software Engineering of USTC) Python & Numpy Slide 18/38

19 Indexing Lists can be used as an array s = [ H e f e i, Suzhou, N a n j i n g, Kunming, Chengdu ] p r i n t l e n ( s ) # Length o f a l i s t p r i n t s [ 0 ] # F i r s t e l e m e n t p r i n t s [ 1 ] # Second e l e m e n t p r i n t s [ 2 ] # T h i rd e l e m e n t p r i n t s [ 1] # L a s t e l e m e n t p r i n t s [ 2] # B e f o r e l a s t e l e m e n t Negative indexes can help to have simpler, shorter code Devert Alexandre (School of Software Engineering of USTC) Python & Numpy Slide 19/38

20 Slicing Lists can be cut or assembled s = [ H e f e i, Suzhou, N a n j i n g, Kunming, Chengdu ] p r i n t s [ 1 : 4 ] p r i n t s [ : 4 ] p r i n t s [: 2] p r i n t s [ 1 : ] p r i n t s + [ B e i j i n g, Shanghai ] Will print [ Suzhou, N a n j i n g, Kunming ] [ H e f e i, Suzhou, N a n j i n g, Kunming ] [ H e f e i, Suzhou, N a n j i n g ] [ Suzhou, N a n j i n g, Kunming, Chengdu ] [ H e f e i, Suzhou, N a n j i n g, Kunming, Chengdu, B e i j i n g, Shanghai ] Devert Alexandre (School of Software Engineering of USTC) Python & Numpy Slide 20/38

21 Sorting Sorting is simple too a = [ 8, 3, 0, 2, 4, 1, 5, 9, 6, 7 ] a. s o r t ( ) p r i n t a Will print [ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9 ] Devert Alexandre (School of Software Engineering of USTC) Python & Numpy Slide 21/38

22 List operations Common list operations are of course available a = [ 7, 1 3 ] a. append ( 4 2 ) # Add 42 a t t h e end o f t h e l i s t a. i n s e r t ( i, 9) # I n s e r t 9 b e f o r e p o s i t i o n i a. pop ( i ) # Remove e l e m e n t a p o s i t i o n i and r e t u r n i t a. remove ( 4 2 ) # Remove 42 from t h e l i s t min ( a ) # Return minimum element max ( a ) # Return maximum element sum ( a ) # Return sum of elements i f 13 i n a : # Test i f 13 e x i s t s i n l i s t p r i n t you got 13 i n a Devert Alexandre (School of Software Engineering of USTC) Python & Numpy Slide 22/38

23 List comprehension You can build lists from an other list a = [ 2 i + 1 f o r i i n r a n g e ( 1 0 ) ] p r i n t a a = [ 2 i + 1 f o r i in range (10) i f i % 3!= 0 ] p r i n t a It will print [ 1, 3, 5, 7, 9, 11, 13, 15, 17, 1 9 ] [ 3, 5, 9, 11, 15, 1 7 ] Short, simple & also very efficient on large lists! Devert Alexandre (School of Software Engineering of USTC) Python & Numpy Slide 23/38

24 Tuples comprehension This same powerful trick works for tuples a = (2 i + 1 f o r i i n r a n g e ( 1 0 ) ) p r i n t a a = (2 i + 1 f o r i in range ( 10) i f i % 3!= 0) p r i n t a It will print ( 1, 3, 5, 7, 9, 11, 13, 15, 17, 19) ( 3, 5, 9, 11, 15, 17) Devert Alexandre (School of Software Engineering of USTC) Python & Numpy Slide 24/38

25 Table of Contents 1 Why Python & Numpy 2 First steps with Python 3 Fun with lists 4 Quick tour of Numpy 5 Conclusion Devert Alexandre (School of Software Engineering of USTC) Python & Numpy Slide 25/38

26 Introducing : Numpy Numpy is Python module for scientific computing Written in C Add new types and function in Python Fast vector & matrix operations Python + Numpy = Enhanced Matlab, and for free Devert Alexandre (School of Software Engineering of USTC) Python & Numpy Slide 26/38

27 Vectors Numpy manipulate arrays. 1d array vector i m p o r t numpy p r i n t numpy. zeros (10) # Create vector of 10 zeros p r i n t numpy. ones (10) # Create vector of 10 ones p r i n t numpy. a r r a y ( [ 1. 0, 2. 0, 3. 0 ] ) # C r e a t e v e c t o r from a l i s t p r i n t numpy. l i n s p a c e ( 0. 0, 1. 0, 9) # C r e a t e a v e c t o r o f 10 v a l u e s i n [ 0. 0, 1. 0 ] p r i n t numpy. random. randn ( 5 ) # C r e a t e a v e c t o r o f 10 v a l u e s w i t h norm. d i s t r i b. It will print [ ] [ ] [ ] [ ] [ ] Devert Alexandre (School of Software Engineering of USTC) Python & Numpy Slide 27/38

28 Matrices Numpy manipulate arrays. 2d array matrices i m p o r t numpy p r i n t numpy. z e r o s ( ( 2, 3 ) ) # C r e a t e 2 x3 m a t r i x o f z e r o s p r i n t numpy. ones ( ( 2, 3 ) ) # C r e a t e 2 x3 m a t r i x o f ones p r i n t numpy. random. randn ( 2, 3) # C r e a t e 2 x3 m a t r i x o f norm. d i s t r i b. It will print [ [ ] [ ] ] [ [ ] [ ] ] [[ ] [ ] ] Devert Alexandre (School of Software Engineering of USTC) Python & Numpy Slide 28/38

29 Array shape An array s dimensions can be read and modified i m p o r t numpy A = numpy. ones (6) p r i n t A, A. shape A = A. r e s h a p e ( ( 3, 2 ) ) p r i n t A, A. shape A = A. r e s h a p e ( ( 2, 3 ) ) p r i n t A, A. shape It will print [ ] ( 6, ) [ [ ] [ ] [ ] ] ( 3, 2) [ [ ] [ ] ] ( 2, 3) Devert Alexandre (School of Software Engineering of USTC) Python & Numpy Slide 29/38

30 Indexing Access to coefficients of an array i m p o r t numpy A = numpy. l i n s p a c e ( 0, 1, 5) p r i n t A p r i n t A [ 1 ] M = numpy. a r r a y ( r a n g e ( 1, 7 ) ). r e s h a p e ( ( 2, 3 ) ) p r i n t M p r i n t M[ 1, 2 ] It will print [ ] [ [ ] [ ] ] 6 Devert Alexandre (School of Software Engineering of USTC) Python & Numpy Slide 30/38

31 Dot product Dot product between vectors/matrices i m p o r t numpy A = numpy. array ( range (1, 3 ) ) B = numpy. array ( range (3, 5 ) ) M = numpy. ones ( ( 3, 2 ) ) N = numpy. ones ( ( 2, 7 ) ) p r i n t A. dot (B) # Computes A. B p r i n t M. dot (A) # Computes M x A p r i n t M. dot (N) # Computes M x N It will print 11 [ ] [ [ ] [ ] [ ] ] Devert Alexandre (School of Software Engineering of USTC) Python & Numpy Slide 31/38

32 Data file loading i m p o r t numpy data = numpy. l o a d t x t ( s t u f f s. dat ) p r i n t data Figure: Program It will print Figure: Input file [ [ e e e e e+00] [ e e e e e+00] [ e e e e e+00] [ e e e e e 01] [ e e e e e 01] [ e e e e e 01]] Devert Alexandre (School of Software Engineering of USTC) Python & Numpy Slide 32/38

33 Statistics Computing some basic statistics, per column i m p o r t numpy data = numpy. l o a d t x t ( s t u f f s. dat ) p r i n t numpy. mean ( data, a x i s = 0) # mean f o r each column p r i n t numpy. s t d ( data, a x i s = 0) # s t d. dev. f o r each column p r i n t numpy. median ( data, a x i s = 0) # median f o r each column It s much faster than coding it yourself in Python Devert Alexandre (School of Software Engineering of USTC) Python & Numpy Slide 33/38

34 Deleting We often want to remove rows or columns i m p o r t numpy data = numpy. l o a d t x t ( l o l. dat ) data = numpy. d e l e t e ( data, 2, a x i s =0) p r i n t data data = numpy. l o a d t x t ( l o l. dat ) data = numpy. d e l e t e ( data, 2, a x i s =1) p r i n t data Figure: Program It will print Figure: Input file [ [ ] [ ] ] [ [ ] [ ] [ ] ] Devert Alexandre (School of Software Engineering of USTC) Python & Numpy Slide 34/38

35 Slicing Working with one column of a 2d array i m p o r t numpy data = numpy. l o a d t x t ( s t u f f s. dat ) p r i n t data [ 2 ] # P r i n t t h e 2nd column Figure: Program It will print Figure: Input file [ e e e e e+00] Devert Alexandre (School of Software Engineering of USTC) Python & Numpy Slide 35/38

36 Slicing Working with one row of a 2d array i m p o r t numpy data = numpy. l o a d t x t ( s t u f f s. dat ) p r i n t data [ :, 2 ] # P r i n t t h e 2nd row Figure: Program It will print Figure: Input file [ ] Devert Alexandre (School of Software Engineering of USTC) Python & Numpy Slide 36/38

37 Table of Contents 1 Why Python & Numpy 2 First steps with Python 3 Fun with lists 4 Quick tour of Numpy 5 Conclusion Devert Alexandre (School of Software Engineering of USTC) Python & Numpy Slide 37/38

38 Conclusion This is only a quick introduction! More things: eigen values, linear solver,... Memorizing this is USELESS Practising this is USEFUL Many tutorial and examples on the web Try to program some algorithms you know, and you will master all this very quickly Devert Alexandre (School of Software Engineering of USTC) Python & Numpy Slide 38/38

Scripting Languages Fast development, extensible programs

Scripting Languages Fast development, extensible programs Devert Alexandre School of Software Engineering of USTC November 30, 2012 Slide 1/60 Table of Contents 1 Introduction 2 Dynamic languages A Python