A 12 hour workshop on Introduction to Python

1. Programming in Python A Two Day Workshop Satyaki Sikdar Vice Chair ACM Student Chapter Heritage Institute of Technology April 22 2016 Satyaki Sikdar© Programming in Python April 22 2016 1 / 137

2. hour 0: intro whoami whoami Extremely lazy Coding in Python since the summer of 2013 - not an expert by any means An average programmer at best. Comfortable writing 750 - 1000 lines of code In terms of diﬃculty levels in FIFA, I’m stuck somewhere midway between semi-pro and professional I love Python - duh! Working in SNA for the past 15 months under the supervision of Prof. Dr. Partha Basuchowdhuri and Prof. Dr. Subhashis Majumder. Learning Python paid oﬀ big time! Satyaki Sikdar© Programming in Python April 22 2016 2 / 137

8. hour 0: intro expectations and takeaways great expectations Things you can expect from me Clarity No spoon feeding Desperate attempts at making jokes Things I expect from you An open mind Patience - not everything would make perfect sense at ﬁrst Attention - especially to details Cooperation - it’s going to be a long haul Interruptions - feel free to stop me to ask relevant questions Satyaki Sikdar© Programming in Python April 22 2016 3 / 137

16. hour 0: intro expectations and takeaways your takeaways 1 A degree of ﬂuency in reading and writing short - medium Python codes 2 A knowledge of how things work in Python and why certain things are done in a certain way 3 Learning something exciting - might not be only Python 4 A formal but very brief introduction Social Network Analysis, a vibrant ﬁeld of CS Satyaki Sikdar© Programming in Python April 22 2016 4 / 137

20. hour 0: intro lesson plan the lesson plan for day I hour topic 1 introduction, motivation, Guido, awesomeness of Python 2 playing around with the interpreter - variables, simple data types 3 predeﬁned functions, conditionals, lists, loops 4 tuples, user deﬁned functions, basic recursion 5 sets, dictionaries, errors and exceptions, intro to OOP 6 OOP - inheritance, applying OOP Satyaki Sikdar© Programming in Python April 22 2016 5 / 137

21. hour 0: intro Guido Maester Guido van Rossum The Dutch are known for their easygoing nature. And Guido is no diﬀerent. Figure: Guido van Rossum Born: 31 January, 1956 in Haarlem, The Netherlands Studied Computer Science from the University of Amsterdam Started Python as a hobby project in 1989. It grew into a new language in a couple of years. Recognized as a Distinguished Engineer by ACM Xoogler and now works for Dropbox Satyaki Sikdar© Programming in Python April 22 2016 6 / 137

26. hour 0: intro popularity it’s good to be popular It’s true for people and programming languages alike Tons of resources to get help from if you are stuck You’ll get a lot of people to collaborate Employment opportunities open up Satyaki Sikdar© Programming in Python April 22 2016 7 / 137

30. hour 0: intro popularity popularity of Python ©codeeval - hiring demand trends Satyaki Sikdar© Programming in Python April 22 2016 8 / 137

31. hour 0: intro popularity popularity of Python ©PYPL Satyaki Sikdar© Programming in Python April 22 2016 9 / 137

32. hour 0: intro popularity popularity of Python Satyaki Sikdar© Programming in Python April 22 2016 10 / 137

33. hour 0: intro awesomeness of Python what makes Py Awesome? Open source license - right from the early days Very versatile The Zen of Python - you should read it every now and then A strong and active community PyPI - the modules repository has over 30, 000 user created modules for ready use Killer apps like Django and Pandas Lots and lots of reference materials available including books, video lectures and tutorials Satyaki Sikdar© Programming in Python April 22 2016 11 / 137

40. hour 0: intro awesomeness of Python the awesomeness continues! Ease of learning - you’ll see it soon Very teachable - low syntax overhead Rapid development cycle - easy to express yourself Readable, beautiful and brevity - you’ll love to code Interactive prompt Satyaki Sikdar© Programming in Python April 22 2016 12 / 137

45. hour 0: intro awesomeness of Python ©xkcd.com Satyaki Sikdar© Programming in Python April 22 2016 13 / 137

46. hour 0: intro awesomeness of Python places were Py is used From supercomputers to Instagram, from labs to ﬁnancial ﬁrms You can do machine learning, NLP to building web pages and wrangling social networks Really hip among startups Schools like M.I.T., Rice, Stanford, Havard have switched to Python for their programming courses in the CS curriculum Satyaki Sikdar© Programming in Python April 22 2016 14 / 137

50. hour 0: intro indentation indentation continues Pseudocode on the left and Python code on the right Figure: From CLRS def INSERTION_SORT(A): for j in xrange(1, len(A)): key = A[j] i = j - 1 while i >= 0 and A[i] > key: A[i + 1] = A[i] i -= 1 A[i + 1] = key Satyaki Sikdar© Programming in Python April 22 2016 15 / 137

51. hour 0: intro indentation indentation & Python It’s absolutely essential to indent Python code. It seems natural Braces? Yuck!! According to PEP8, you must indent every block of code by 4 spaces Mess up the indentation, and your precious code becomes garbage from __future__ import braces # File "<stdin>", line 1 # SyntaxError: not a chance Satyaki Sikdar© Programming in Python April 22 2016 16 / 137

55. hour 1: Python 101 the interpreter and more .. say hello to the interpreter! Python is an interpreted language. It executes codes line by line It has an interactive prompt with which you can play around without any fear The prompt looks like this >>> You can do everything you want in this interpreter. We’ll play around with it now. Satyaki Sikdar© Programming in Python April 22 2016 17 / 137

59. hour 1: Python 101 the interpreter and more .. the print statement It’s one of the most used statements in Python Does what the name says, it prints stuﬀ It’s very smart - one statement to rule them all - works for every data type - no messy loops needed print 1 + 1 # >>> 2 print 'joe root' # >>> joe root (notice the lack of quotes) print True # >>> True print 'hello', 'world' # >>> hello world (notice the space) print 'hello' print 'world' #hello and world in separate lines print 'hello', print 'world' #hello and world in the same line (notice the comma) Satyaki Sikdar© Programming in Python April 22 2016 18 / 137

62. hour 1: Python 101 the interpreter and more .. the print statement formatter = '%s %s %s %s' print formatter % (1, 2, 3, 4) print formatter % ('one', 'two', 'three', 'four') print formatter % (True, False, False, True) print formatter % (formatter, formatter, formatter, formatter) print formatter % ('I had this thing.', 'That you could type up right.', "But it didn't sing.", 'So I said goodnight.') Satyaki Sikdar© Programming in Python April 22 2016 19 / 137

63. hour 1: Python 101 data types data types in Python There are 6 basic data types in Python 1 integers (int), eg: 1, 23, 1245322 2 long integers (long). These usually end with a L at the end, eg: 1071645932315929337037L 3 floating point numbers (float): Has a decimal point somewhere. Eg: 1.2, 42.0. Even 4. is a valid floating point number. 4 complex numbers (complex): Handles complex numbers. Eg: 1 + 2j is a valid complex number in Python. 5 strings (str): Handles strings, eg: ’jimmy anderson’ and "jimmy anderson" are both valid strings, but ’jimmy anderson" or "jimmy anderson’ is not. 6 booleans (bool): True and False Satyaki Sikdar© Programming in Python April 22 2016 20 / 137

69. hour 1: Python 101 integers integers Supports basic operations as it is expected to. Any of the things shouldn’t come as a surprise to you. + for addition. Eg: 50 + 7 gives 57 * for multiplication. Eg: 50 * 7 gives 350 / for integer division. Eg: 50 / 7 gives 7 (why?) % for modulo operation. Eg: 50 % 7 gives 1 ** for exponentiation. Eg: 2 ** 7 gives 128 >>, <<, ˜ are also allowed Parenthization of expressions is allowed with the help of () Satyaki Sikdar© Programming in Python April 22 2016 21 / 137

75. hour 1: Python 101 floats floating point numbers Supports basic operations as it is expected to. Any of the things shouldn’t come as a surprise to you. + for addition. Eg: 50.2 + 7.4 gives 57.6 * for multiplication. Eg: 50.2 * 5 gives 251.0 / for division. Eg: 50.0 / 7 gives 7.1428 % for modulo operation. Eg: 50.0 % 7.2 gives 6.79999 ** for exponentiation. Eg: 2.2 ** 7 gives 249.43578 Parenthization of expressions is allowed with the help of () Satyaki Sikdar© Programming in Python April 22 2016 22 / 137

80. hour 1: Python 101 complex numbers complex numbers Note: j is used in place of i + for addition. Eg: (2 + 3j)+ (5 + 4j) gives (7 + 7j) * for multiplication. Eg: (2 + 3j) * (5 + 4j) gives (-2 + 23j) / for division. Eg: (5 + 4j) / (1 + 2j) gives (2.6 - 1.2j) % for modulo operation. Eg: (5 + 4j) % (1 + 3j) gives (4 + 1j) ** is for exponentiation. Eg: (5 + 4j) ** 2 gives 9 + 40j Parenthization of expressions is allowed with the help of () Satyaki Sikdar© Programming in Python April 22 2016 23 / 137

85. hour 1: Python 101 strings no strings attached! Can be enclosed within either single or double quotes. DO NOT MIX AND MATCH Bigger strings consist of smaller strings. There’s no such thing as a character in Python. + used for appending two strings together. Eg: ’Alastair’ + ’Cook’ gives ’AlastairCook’ and ’Cook’ + ’Alastair’ gives ’CookAlastair’ * used to append the same string to itself. Eg: ’yay ’ * 3 gives ’yay yay yay’ Satyaki Sikdar© Programming in Python April 22 2016 24 / 137

89. hour 1: Python 101 strings string methods - I Here’s a subset of the many in-built functions and methods that can be used with strings. len(s) :- gives you the length of the string s. Eg: len(’broady’) = 6 indexing :- Starts from 0. So, ’batman’[0] => ’b’, .., up to ’batman’[len(’batman’) - 1] => ’n’ . Negative indexing is also valid! ’batman’[-1] => ’n’, ’batman’[-2] => ’a’ and so on. slicing: Returns a part of the string. So string[start: end] slices the string from index = start, goes all the way to end - 1 and returns the new string. Eg: ’I hate Shane Warne’[2: 6] => ’hate’ Satyaki Sikdar© Programming in Python April 22 2016 25 / 137

92. hour 1: Python 101 strings string methods - II s.count(substring) :- gives you the count of the substring in the string. Eg: ’foster’s home for imaginary friends’ .count (’fo’) = 2 s.ﬁnd(substring) :- gives you the index of the substring in the string. Eg: ’Alastair’.ﬁnd(’stair’) = 3 s.split(delim) :- Splits s using a delimiter delim. Returns a list of substrings. Eg: ’Alastair Cook is the best’.split(’ ’) returns [ Alastair , Cook , is , the , best ] If delim is omitted, whitespace is used by default Satyaki Sikdar© Programming in Python April 22 2016 26 / 137

95. hour 1: Python 101 strings string slicing - revisited So far, we have sliced strings in a manner like st[start: end] You can use a third parameter stride as well! So it can be st[start: end: stride]. It defaults to 1. But what does that mean? Let’s ﬁnd out! st = '0123456789' for c in st[: : 1]: print c, #output: 0 1 2 3 4 5 6 7 8 9 for c in st[: : 2]: print c, #output: 0 2 4 6 8 for c in st[: : 3]: print c, #output: 0 3 6 9 for c in st[: : -1]: print c, #output: 9 8 7 6 5 4 3 2 1 0 WHOA! Really?! # what about st[: : -2]? Find it out! Satyaki Sikdar© Programming in Python April 22 2016 27 / 137

96. hour 1: Python 101 strings string slicing - revisited So far, we have sliced strings in a manner like st[start: end] You can use a third parameter stride as well! So it can be st[start: end: stride]. It defaults to 1. But what does that mean? Let’s ﬁnd out! st = '0123456789' for c in st[: : 1]: print c, #output: 0 1 2 3 4 5 6 7 8 9 for c in st[: : 2]: print c, #output: 0 2 4 6 8 for c in st[: : 3]: print c, #output: 0 3 6 9 for c in st[: : -1]: print c, #output: 9 8 7 6 5 4 3 2 1 0 WHOA! Really?! # what about st[: : -2]? Find it out! Satyaki Sikdar© Programming in Python April 22 2016 27 / 137

97. hour 1: Python 101 strings palindrome checking A palindrome is a word, phrase, number, or other sequence of characters which reads the same backward or forward. Examples: ’race car’, ’taco cat’, ’A man, a plan, a canal, Panama!’ Often it’s allowed to skip over spaces and punctuation marks It’s a classic problem while dealing with strings. And we’ll stick to character-unit palindromes. Example: redivider, noon, radar, madam, refer, etc. We’ll see a simple one liner code that checks if a string is a palindrome. >>> st = 'refer' >>> if st == st[: : -1]: # st == st.reverse() does the same thing print st, 'is a palindrome' else: print st, 'is not a palindrome' Satyaki Sikdar© Programming in Python April 22 2016 28 / 137

100. hour 1: Python 101 strings multi-line strings and escape characters Multi-line strings Multi-line strings are diﬀerent from ordinary strings. As the name suggests, they can span over multiple lines Enclosed by a pair of ”’ or """ They support all the string operations - indexing, slicing and dicing... End of lines are marked with the EOL (n) character Escape characters acts as the escape character "I am 5'11" tall." # escape double-quote inside the string 'I am 5'11" tall.' # escape single-quote inside the string Satyaki Sikdar© Programming in Python April 22 2016 29 / 137

105. hour 1: Python 101 strings the Boolean data type They represent the logical True and False. The operations that they support are as follows: Negation: Using the word not before True or False negates it. >>> not True yields False and >>> not False yields True Logical and: The result is True if and only if every argument is True. In Python, use the word and Logical or: The result is False if and only if every argument is False. In Python, use the word or Satyaki Sikdar© Programming in Python April 22 2016 30 / 137

108. hour 1: Python 101 strings truth tables for logical and and logical or A B A or B A and B False False False False False True True False True False True False True True True True Satyaki Sikdar© Programming in Python April 22 2016 31 / 137

109. hour 1: Python 101 strings logical comparisons The comparisons yield either True or False Equals (==): 2 == 3 is False Not equal (!=): 2 != 3 is True Greater than (>): 2 > 3 is False Lesser than (<): 2 < 3 is True Greater than or equal to (>=): 2 >= 4 is False Lesser than or equal to (<=): 2 <= 4 is True Satyaki Sikdar© Programming in Python April 22 2016 32 / 137

115. hour 1: Python 101 variables and bindings variables! Variables provide a way to associate names with objects I personally prefer under_bars over camelCase. It’s up to you to decide A variable is just a name. An object can have one, more than one or no names associated with it. Assignment statements are used to assign values to variables. a = 15 Notice, there is no data types being mentioned! So you can do a = 15 and then a = ’bloo’ and it’ll be alright! Note: julia, Julia, and JULIA are all diﬀerent Satyaki Sikdar© Programming in Python April 22 2016 33 / 137

121. hour 1: Python 101 variables and bindings bindings Consider the code pi = 3.14159 radius = 11.2 area = pi * (radius ** 2) radius = 14.3 Figure: Binding of variables to objects Satyaki Sikdar© Programming in Python April 22 2016 34 / 137

122. hour 1: Python 101 variables and bindings meaningful names! a = 3.14159 b = 11.2 c = a * (b ** 2) pi = 3.14159 diameter = 11.2 area = pi * (diameter ** 2) The two codes to Python are exactly the same To a human, they are pretty diﬀerent! The code on the right is better, but there’s a problem?! The variable should have been called radius instead of diameter, or the area formula is wrong! Satyaki Sikdar© Programming in Python April 22 2016 35 / 137

125. hour 1: Python 101 variables and bindings a comic from xkcd.com Satyaki Sikdar© Programming in Python April 22 2016 36 / 137

126. hour 1: Python 101 variables and bindings the names which must not be given! and as assert break class continue def del elif else except exec ﬁnally for from global if import in is lambda not or pass print raise return try while with yield Satyaki Sikdar© Programming in Python April 22 2016 37 / 137

127. hour 1: Python 101 basic i/o taking input from the user! name = raw_input('Enter something!') print name #example output: Edwin print type(name) #example output: <type 'str'> Satyaki Sikdar© Programming in Python April 22 2016 38 / 137

128. hour 1: Python 101 basic i/o raw_input returns a string What if we need to take integer or float inputs? Typecast it! So int(’12’) gives the integer 12 float(’12.4’) gives 12.4 (a float) int(’12.4’) makes it 12 (an integer) float(12) makes it 12.0 (a float) Don’t overdo it! int(’tendo’) makes the interpreter go nuts! Raises ValueError Satyaki Sikdar© Programming in Python April 22 2016 39 / 137

134. hour 2: ifs, buts and repeats conditionals Satyaki Sikdar© Programming in Python April 22 2016 40 / 137

135. hour 2: ifs, buts and repeats conditionals making decisions Making decisions are an integral part of our lives. Thus, it becomes essential for our codes to make decisions too! Python has if, else and elif to help your code make decisions The syntax is as follows if boolean_expression1: do_something elif boolean_expression2: do_something_different else: do_something_else Satyaki Sikdar© Programming in Python April 22 2016 41 / 137

138. hour 2: ifs, buts and repeats conditionals a simple illustration I Comparing the values of two variables x = int(raw_input('Enter 1st number')) y = int(raw_input('Enter 2nd number')) if x == y: print 'the numbers are equal' elif x > y: print 'the first number is bigger' else: print 'the second number is bigger' Satyaki Sikdar© Programming in Python April 22 2016 42 / 137

139. hour 2: ifs, buts and repeats conditionals a simple illustration II Nested conditionals Divisibility by 6 x = int(raw_input("Enter a number: ")) if x % 2 == 0: if x % 3 == 0: print 'Divisible by both 2 and 3' else: print 'Divisible by 2, but not by 3' elif x % 3 == 0: print 'Divisible by 3 and not by 2' else: print 'Divisible by neither 2 nor 3' Satyaki Sikdar© Programming in Python April 22 2016 43 / 137

140. hour 2: ifs, buts and repeats conditionals the in keyword The in keyword is used for containment checking Returns a boolean. Can be used for substring check in strings. Eg: ’bot’ in ’sir ian botham’ returns True, but ’beefy’ in ’sir ian botham’ returns False Thus in can be used in if, elif statements. Satyaki Sikdar© Programming in Python April 22 2016 44 / 137

143. hour 2: ifs, buts and repeats looping constructs loops: the basic idea Satyaki Sikdar© Programming in Python April 22 2016 45 / 137

144. hour 2: ifs, buts and repeats looping constructs say hello to while loops while loops in Python are no diﬀerent than the while loops in other common languages. Phew! It’s similar to a conditional statement. But, no else blocks As long as the condition is valid, the loop body keeps executing. The condition is re-evaluated each time Let’s look at an example. # squaring an integer, the hard way >>> x = 4 >>> ans = 0 >>> iters_left = x >>> while iters_left != 0: ans = ans + x iters_left = iters_left - 1 >>> print x, 'squared is', ansSatyaki Sikdar© Programming in Python April 22 2016 46 / 137

147. hour 2: ifs, buts and repeats looping constructs squaring the hard way taking x = 4 as an example test # x ans iters_left iters_left != 0 1 4 0 4 True 2 4 4 3 True 3 4 8 2 True 4 4 12 1 True 5 4 16 0 False Boom! When iters_left is 0, we have our answer in ans For what values of x will the program terminate? Satyaki Sikdar© Programming in Python April 22 2016 47 / 137

148. hour 2: ifs, buts and repeats looping constructs the correctness of the program It’s very important to check if the procedure works for all x We’ve already proved that it works when x is a positive integer What about when x is 0? It works! The body of the while loop is not executed. So, ans stays at 0! What about when x is negative? Let’s see! Say x = -3. test # x ans iters_left iters_left != 0 1 -3 0 -3 True 2 -3 -3 -4 True 3 -3 -6 -5 True 4 -3 -9 -6 True 5 -3 -12 -7 True 6 -3 -15 -8 True OOPS! It hasn’t stop when it’s supposed to! :( Satyaki Sikdar© Programming in Python April 22 2016 48 / 137

151. hour 2: ifs, buts and repeats looping constructs does the code ever end??!! Things start to get a little ﬁshy when x is negative Does the program ever end? NO!! Is it a problem? YES !! Is there any way to ﬁx this? Hell yeah! Let’s see the code. >>> x = -4 >>> ans = 0 >>> iters_left = abs(x) #the absolute value of x >>> while iters_left != 0: ans = ans + abs(x) iters_left = iters_left - 1 >>> print x, 'squared is', ans Does it make things better? Yes indeed! We got a working program that squares all integers! Yay! Satyaki Sikdar© Programming in Python April 22 2016 49 / 137

156. hour 2: ifs, buts and repeats looping constructs for loops In Python, for loops are a little diﬀerent. They are eﬀectively for each loops Syntax: for item in iterable: code block #notice the indentation Strings are iterable. So are lists, tuples, dictionaries, sets, ... Example: >>> st = 'cuts like a knife!' >>> for x in st: print x, >>> cuts like a knife! Satyaki Sikdar© Programming in Python April 22 2016 50 / 137

159. hour 2: ifs, buts and repeats looping constructs how does it work? for item in iterable: code block for each item (x) in the iterable, execute the code block repeatedly as long as the iterable is not exhausted. You may or may not use the item (x). # figuring out the length of a string w/o using len() >>> length = 0 >>> st = 'rocky mountain high' >>> for c in st: length += 1 >>> print 'the length of the string is', length Output: the length of the string is 19 Satyaki Sikdar© Programming in Python April 22 2016 51 / 137

160. hour 2: ifs, buts and repeats looping constructs a few more illustrations This time we’ll use the item in the for loop >>> st = 'rocky mountain high' >>> for c in st: if c == 'o': print c.upper(), else: print c, >>> rOcky mOuntain high >>> st = 'Ryan ten Doeschate' >>> for c in st[: : -1]: print c, >>> etahcseoD net nayR Satyaki Sikdar© Programming in Python April 22 2016 52 / 137

161. hour 2: ifs, buts and repeats looping constructs the xrange function An arithmetic series (AP) has parameters start, stop and step xrange() is used to generate an iterator for an AP in Python xrange(start, stop, step) will generate numbers of the form: start, start + step, start + 2 ∗ step, ..., in the set [start, end) If one argument (x) is passed, start = 0, stop = x, step = 1 >>> for no in xrange(5): print no, #output: 0 1 2 3 4 If two arguments (x, y) are passed, start = x, stop = y, step = 1 >>> for no in xrange(2, 5): print no, #output: 2 3 4 If three (x, y, z) are passed, start = x, stop = y, step = z >>> for no in xrange(1, 6, 2): print no, #output 1 3 5Satyaki Sikdar© Programming in Python April 22 2016 53 / 137

167. hour 2: ifs, buts and repeats looping constructs loop condition evaluation >>> x = 4 >>> for j in range(x): for i in range(x): print i x = 2 0 1 2 3 0 1 0 1 0 1 Seems bizarre? Let’s break it down The ﬁrst range function is evaluated once with x = 4, so come what may, the outer loop will run 4 times Each time, the inner loop would run x times, so the ﬁrst time it runs 4 times But inside, x becomes 2, and then from second time on, the inner loop runs twice Satyaki Sikdar© Programming in Python April 22 2016 54 / 137

171. hour 2: ifs, buts and repeats bisection search bisection search - the hunt for √ x >>> x = int(raw_input("Enter a number")) >>> eps = 0.01; num_guess = 0 >>> low = 0.0 >>> high = x >>> ans = (low + high) / 2.0 >>> while abs(ans ** 2 - x) >= eps: print 'guess:', num_guess, 'low:', low, 'high:', high, 'ans:', ans num_guess += 1 if ans ** 2 < x: low = ans else: high = ans ans = (low + high) / 2.0 >>> print ans, 'is close to sqrt of', x Satyaki Sikdar© Programming in Python April 22 2016 55 / 137

172. hour 2: ifs, buts and repeats floating points floating point woes >>> x = 0.0 >>> for i in xrange(10): x = x + 0.1 >>> if x == 1.0: print x, '= 1.0' >>> else: print x, 'is not 1.0' The output? 1.0 is not 1.0 What on earth is going on? Has Python gone nuts? Let’s dig deeper. In the memory, every number is stored in binary. Let’s try to ﬁgure out what’s the binary representation for (a) (0.5)10 => (0.1)2 and, (b) (0.625)10 => (0.101)2 (0.1)10 = (?)2? What about 3 32 = 0.9375? How about 25 256? Turns out, no matter how many bits you use, you can’t have a perfect 0.1 in memory! So round up!! So, now we know that x at the end is not a perfect 1, so the equality test fails But why does it print 1.0 is not 1.0? Print does some rounding on it’s own! So, it doesn’t alway the actual value Satyaki Sikdar© Programming in Python April 22 2016 56 / 137

177. hour 3: data structures 101 lists lists The list is a container that holds a number of other objects, in a given order. Similar to arrays in C, C++, Java, but it is more ﬂexible. Lists are mutable Lists are enclosed in square brackets [ ] and the elements are separated by commas. Example: [1, 2, 3, 5] is a valid list It can contain anything that’s acceptable in Python. [1, ’two’, 3.0] is also a valid list Lists support indexing and slicing in the exact same manner as strings do. They also support + and *. So, [1, 2] + [3, 4] => [1, 2, 3, 4] and [1, 2] * 3 => [1, 2, 1, 2, 1, 2] Satyaki Sikdar© Programming in Python April 22 2016 57 / 137

183. hour 3: data structures 101 list operations basic functions and operations Empty list is declared as [ ] or list() len() gives you the length of the list. So, len([1, 5, 6]) gives 3 lst[i] returns the item at index i (the ﬁrst item has index 0) Lists, unlike strings, support item assignment >>> lst = [1, 3, 4] >>> lst[1] = 7 >>> print lst >>> print lst [1, 3, 4] [1, 7, 4] Adding a new element to a list is done using append >>> lst = [1, 3, 4] >>> lst.append(10) >>> print lst >>> print lst [1, 3, 4] [1, 3, 4, 10] Satyaki Sikdar© Programming in Python April 22 2016 58 / 137

188. hour 3: data structures 101 list operations Append adds one element at a time. lst.append(1, 2) would raise an error! So, what if we want to append more than one items? It’s called extending a list >>> lst = [1, 5, 9] >>> #what if we use + instead? >>> print lst >>> lst2 = [8, 9, 10] [1, 5, 9] >>> lst3 = lst2 + [3, 4] >>> lst.extend([2, 5, 10]) >>> print lst3 >>> print lst [8, 9, 10, 3, 4] [1, 5, 9, 2, 5, 10] Satyaki Sikdar© Programming in Python April 22 2016 59 / 137

189. hour 3: data structures 101 list operations Append adds one element at a time. lst.append(1, 2) would raise an error! So, what if we want to append more than one items? It’s called extending a list >>> lst = [1, 5, 9] >>> #what if we use + instead? >>> print lst >>> lst2 = [8, 9, 10] [1, 5, 9] >>> lst3 = lst2 + [3, 4] >>> lst.extend([2, 5, 10]) >>> print lst3 >>> print lst [8, 9, 10, 3, 4] [1, 5, 9, 2, 5, 10] Satyaki Sikdar© Programming in Python April 22 2016 59 / 137

190. hour 3: data structures 101 list operations Append adds a new element only to the end Insert gives more ﬂexibility. It allows you to specify the position as well Syntax: lst.insert(pos, new_element). So, insert at the beginning can be done by lst.insert(0, x), and at the end by lst.insert(len(lst), x) What about removing things? lst.pop() removes and returns the last element. lst.pop(pos) removes the element from a speciﬁc position. >>> lst = [1, 2, 3, 4, 5] >>> print lst.pop() >>> print lst 5 [1, 2, 3, 4, 5] >>> print lst >>> lst.insert(2, 10) [1, 2, 10, 3, 4] >>> print lst >>> print lst.pop(3) [1, 2, 10, 3, 4, 5] 3 Satyaki Sikdar© Programming in Python April 22 2016 60 / 137

194. hour 3: data structures 101 list operations some more functions of lists list.remove(x): Remove the ﬁrst item from the list whose value is x list.index(x): Return the index in the list of the ﬁrst item whose value is x. list.count(x): Return the number of times x appears in the list. list.reverse(): Reverses the list in place list.sort(): Sorts the list in place in ascending order. For descending order, do list.sort(reverse = True) Satyaki Sikdar© Programming in Python April 22 2016 61 / 137

199. hour 3: data structures 101 list comprehension list comprehension and range() range() works exactly in the same fashion as xrange(), just with one diﬀerence; range() produces a list with all the elements in the sequence >>> l = range(2, 10, 3) >>> print l [2, 5, 8] List comprehensions are a short-hand way of making lists >>> squares = [] >>> squares = [x ** 2 for x in xrange(6)] >>> for x in xrange(6): >>> print squares squares.append(x ** 2) [0, 1, 4, 9, 16, 25] >>> print squares [0, 1, 4, 9, 16, 25] Satyaki Sikdar© Programming in Python April 22 2016 62 / 137

200. hour 3: data structures 101 list comprehension list comprehension and range() range() works exactly in the same fashion as xrange(), just with one diﬀerence; range() produces a list with all the elements in the sequence >>> l = range(2, 10, 3) >>> print l [2, 5, 8] List comprehensions are a short-hand way of making lists >>> squares = [] >>> squares = [x ** 2 for x in xrange(6)] >>> for x in xrange(6): >>> print squares squares.append(x ** 2) [0, 1, 4, 9, 16, 25] >>> print squares [0, 1, 4, 9, 16, 25] Satyaki Sikdar© Programming in Python April 22 2016 62 / 137

201. hour 3: data structures 101 tuples A tuple is a sequence of values. Enclosed by parentheses The values can be any type, and they are indexed by integers, so, tuples are a lot like lists However, tuples are immutable >>> t = 'a', 'b', 'c', 'd', 'e' >>> print t ('a', 'b', 'c', 'd', 'e') >>> t1 = ('a', 'b', 'c', 'd', 'e') #common practise >>> t2 = 'a', #to create a tuple with one element, use the comma >>> print t2 ('a') Another way to create a tuple is the built-in function tuple >>> t = tuple() >>> t1 = tuple('dutch') >>> print t >>> print t1 () ('d', 'u', 't', 'c', 'h') Satyaki Sikdar© Programming in Python April 22 2016 63 / 137

205. hour 3: data structures 101 tuple operations Most list operators also work on tuples. The bracket operator indexes an element >>> t = ('a', 'b', 'c', 'd', 'e') >>> print t[0] a Slice operator selects a range of elements. >>> print t[1: 3] ('b', 'c') Try to modify one of the elements of the tuple, you get an error >>> t[0] = 'A' TypeError: object doesn't support item assignment Satyaki Sikdar© Programming in Python April 22 2016 64 / 137

208. hour 3: data structures 101 tuple operations packing and unpacking tuples With conventional assignments, you have to use a temp variable >>> temp = a >>> a = b >>> b = temp This solution is cumbersome; tuple assignment is more elegant >>> a, b = b, a The left side is a tuple of variables, the right side is a tuple of expressions The number of variables on the left and the number of values on the right have to be the same: >>> a, b = 1, 2, 3 ValueError: too many values to unpack Satyaki Sikdar© Programming in Python April 22 2016 65 / 137

212. hour 3: data structures 101 tuple operations lists and tuples zip takes two or more sequences and “zips” them into a list of tuples where each tuple contains one element from each sequence >>> s = 'abc' >>> t = [0, 1, 2] >>> zip(s, t) [('a, 0), ('b', 1), ('c', 2)] If the sequences are not the same length, the result has the length of the shorter one >>> zip('Ned', 'Sansa', 'Bran') [('N', 'S', 'B'), ('e', 'a', 'r'), ('d', 'n', 'a')] Satyaki Sikdar© Programming in Python April 22 2016 66 / 137

213. hour 3: data structures 101 tuple operations lists and tuples zip takes two or more sequences and “zips” them into a list of tuples where each tuple contains one element from each sequence >>> s = 'abc' >>> t = [0, 1, 2] >>> zip(s, t) [('a, 0), ('b', 1), ('c', 2)] If the sequences are not the same length, the result has the length of the shorter one >>> zip('Ned', 'Sansa', 'Bran') [('N', 'S', 'B'), ('e', 'a', 'r'), ('d', 'n', 'a')] Satyaki Sikdar© Programming in Python April 22 2016 66 / 137

214. hour 3: data structures 101 tuple operations You can use tuple assignment in a for loop to traverse a list of tuples: t = [('a', 0), ('b', 1), ('c', 2)] for letter, number in t: print number, letter 0 a 1 b 2 c Combine zip, for and tuple assignment to traverse two (or more) sequences def has_match(t1, t2): for x, y in zip(t1, t2): if x == y: return True return False To traverse the elements of a sequence and their indices, use enumerate: >>> for idx, ele in enumerate('abc'): print idx, ele Satyaki Sikdar© Programming in Python April 22 2016 67 / 137

217. hour 3: data structures 101 tuple operations tuple comparisons The relational operators work with tuples and other seqs Python starts by comparing the ﬁrst element from each seq. If they are equal, it goes on to the next elements, and so on >>> (0, 1, 2) < (0, 3, 4) True >>> (0, 1, 2000000) < (0, 3, 4) True The sort function works the same way >>> l = [(4, 0, 0), (0, 1, 2), (2, 3, 5), (2, 3, 4)] >>> l.sort() >>> print l [(0, 1, 2), (2, 3, 4), (2, 3, 5), (4, 0, 0)] Satyaki Sikdar© Programming in Python April 22 2016 68 / 137

220. hour 3: data structures 101 functions functions We’ve already used some pre-deﬁned functions already! len(), range(), abs(), count(), and so on.. Python allows you to make your own functions! YAY! Each function deﬁnition is of the form def name of function (list_of_parameters): body of the function return things A function can return things too! And in Python, every function returns something. You can tell the function to return something that you want! By default it’s None Notice there’s no mention of the return type Satyaki Sikdar© Programming in Python April 22 2016 69 / 137

225. hour 3: data structures 101 an example An easy example def maximum(x, y): ''' returns the maximum of the two arguments ''' if x > y: return x else: return y >>> print maximum(10, 20) 20 A function can have any name (barring the reserved words) x and y are called formal parameters. The values (10, 20) are called actual parameters. Function calls expect you to feed in the arguments, throws a nasty TypeError otherwise Satyaki Sikdar© Programming in Python April 22 2016 70 / 137

226. hour 3: data structures 101 an example An easy example def maximum(x, y): ''' returns the maximum of the two arguments ''' if x > y: return x else: return y >>> print maximum(10, 20) 20 A function can have any name (barring the reserved words) x and y are called formal parameters. The values (10, 20) are called actual parameters. Function calls expect you to feed in the arguments, throws a nasty TypeError otherwise Satyaki Sikdar© Programming in Python April 22 2016 70 / 137

227. hour 3: data structures 101 flow of control flow of control in a function 1 The expressions that make up the actual parameters are evaluated. If the call is maximum(5 + 7, 7 * 2), the arguments are evaluated ﬁrst 2 The point of execution moves to the ﬁrst line of the function 3 The code in the body of the function is executed until a return statement is encountered or when there are no more lines to execute in the function body 4 The value of invocation is returned back to the point of calling 5 The point of execution transfers back to the point of invocation It’s up to the user to use the value that’s returned. Satyaki Sikdar© Programming in Python April 22 2016 71 / 137

228. hour 3: data structures 101 keyword arguments Arguments and default values There are two ways how formal parameters get bound to actual parameters 1 Positional: the ﬁrst formal parameter is bound to the actual parameter, the second formal to the second actual and so on 2 Keyword arguments: formals are bound to actuals using the name of the formal parameter def print_name(first_name, last_name, reverse): if reverse: print last_name + ', ' + first_name else: print first_name, last_name >>> print_name('Hugh', 'Jackman', False) #prints Hugh Jackman >>> print_name('Hugh', 'Jackman', reverse = False) >>> print_name('Hugh', last_name = 'Jackman', reverse = False) Satyaki Sikdar© Programming in Python April 22 2016 72 / 137

229. hour 3: data structures 101 keyword arguments arguments contd. The order of the keyword arguments does not matter. So, print_name(last_name = ’Jackman’, ﬁrst_name = ’Hugh’, reverse = False) is valid But, you can’t follow up a keyword argument with a non keyword argument. That confuses the interpreter, and it throws an error. So, print_name(ﬁrst_name = ’Hugh’, ’Jackman’, False) is not valid Satyaki Sikdar© Programming in Python April 22 2016 73 / 137

230. hour 3: data structures 101 keyword arguments arguments contd. The order of the keyword arguments does not matter. So, print_name(last_name = ’Jackman’, ﬁrst_name = ’Hugh’, reverse = False) is valid But, you can’t follow up a keyword argument with a non keyword argument. That confuses the interpreter, and it throws an error. So, print_name(ﬁrst_name = ’Hugh’, ’Jackman’, False) is not valid Satyaki Sikdar© Programming in Python April 22 2016 73 / 137

231. hour 3: data structures 101 scope scope of variables in functions def f(x): #x is a formal parameter y = 1 x = x + y print 'x = ', x return x >>> x = 3 >>> y = 2 >>> z = f(x) #x is an actual parameter >>> print ';z = %s, x = %s, y = %s' % (z, x, y) x = 4; z = 4, x = 3, y = 2 Satyaki Sikdar© Programming in Python April 22 2016 74 / 137

232. hour 3: data structures 101 scope so what’s going on here? Surely, x is surely misbehaving?! Or is it? the two x’s are not really the same! They might have the same name, but they are different! Kinda like how you find someone on facebook with your name but then that person has nothing to do with you! The concept of name space comes into play The variables in the function f exist only when f is executing. When the control returns back from the function, it goes back to the variables defined previously. Satyaki Sikdar© Programming in Python April 22 2016 75 / 137

236. hour 3: data structures 101 scope returning a bunch of things Python allows functions to return more than one thing def funky_func(x): return x ** 2, x ** 3 >>> funky_func(3) (9, 27) #things are returned as a tuple >>> a, b = funky_func(3) #unpacking the tuple >>> print a 9 >>> print b 27 This paradigm, though simple, is very powerful Satyaki Sikdar© Programming in Python April 22 2016 76 / 137

237. hour 3: data structures 101 scope returning a bunch of things Python allows functions to return more than one thing def funky_func(x): return x ** 2, x ** 3 >>> funky_func(3) (9, 27) #things are returned as a tuple >>> a, b = funky_func(3) #unpacking the tuple >>> print a 9 >>> print b 27 This paradigm, though simple, is very powerful Satyaki Sikdar© Programming in Python April 22 2016 76 / 137

238. hour 3: data structures 101 sets set A set is an unordered collection of distinct, immutable values { Alice , 3.1415, Carol } is a set of 3 elements { Dean } is a singleton set >>> cset = {11, 11, 22} >>> cset set([11, 22]) Sets are mutable - can add or remove things on the ﬂy Insertion using add Deletion using remove or discard >>> st = {'Jon', 'Arya'} >>> st.add('Jaime') >>> st.remove('Jon') >>> print st >>> print st set(['Jon', 'Arya', 'Jaime']) set(['Arya', 'Jaime']) Satyaki Sikdar© Programming in Python April 22 2016 77 / 137

244. hour 3: data structures 101 set operations set operations Sets in Python support all common set operations >>> test_team = {'Cook', 'Root', 'Stokes', 'Ali', 'Broad'} >>> odi_team = {'Hales', 'Roy', 'Root', 'Ali', 'Stokes'} >>> >>> print test_team | odi_team #set union set(['Stokes', 'Hales', 'Ali', 'Broad', 'Roy', 'Cook', 'Root']) >>> print test_team & odi_team #set intersection set(['Stokes', 'Root', 'Ali']) >>> test_team - odi_team #set differnce set(['Cook', 'Broad']) >>> test_team ^ odi_team #symmetric difference set(['Hales', 'Broad', 'Roy', 'Cook']) Satyaki Sikdar© Programming in Python April 22 2016 78 / 137

245. hour 3: data structures 101 set operations set operations Sets do not support indexing! >>> myset = {'Amsterdam', 'Winnipeg', 'Toronto'} >>> myset set(['Toronto', 'Amsterdam', 'Winnipeg']) >>> myset[0] TypeError: 'set' object does not support indexing + operator doesn’t work with sets >>> myset1 = {'Amsterdam', 'Winnipeg', 'Toronto'} >>> myset2 = {'Brussels', 'Hamilton'} >>> myset1 + myset2 TypeError: unsupported operand type(s) for +: 'set' and 'set' Satyaki Sikdar© Programming in Python April 22 2016 79 / 137

246. hour 3: data structures 101 set operations set operations Sets do not support indexing! >>> myset = {'Amsterdam', 'Winnipeg', 'Toronto'} >>> myset set(['Toronto', 'Amsterdam', 'Winnipeg']) >>> myset[0] TypeError: 'set' object does not support indexing + operator doesn’t work with sets >>> myset1 = {'Amsterdam', 'Winnipeg', 'Toronto'} >>> myset2 = {'Brussels', 'Hamilton'} >>> myset1 + myset2 TypeError: unsupported operand type(s) for +: 'set' and 'set' Satyaki Sikdar© Programming in Python April 22 2016 79 / 137

247. hour 3: data structures 101 dictionaries dictionary A dictionary is like a list, but more general - a personal favorite In a list, the indices have to be integers; in a dictionary they can be (almost) any type The keys must be immutable Let’s build a dictionary that maps from English to Spanish words. Keys and values are both strings >>> eng2sp = dict() # or eng2sp = {} >>> print eng2sp {} Satyaki Sikdar© Programming in Python April 22 2016 80 / 137

251. hour 3: data structures 101 dictionary manipulations To add items to the dictionary, use square brackets >>> eng2sp['one'] = 'uno' Creates an item that maps from the key ’one’ to the value ’uno’ >>> print eng2sp {'one': 'uno'} Instantiate a dictionary with multiple items like >>> eng2sp = {'one': 'uno', 'two': 'dos', 'three': 'tres'} >>> print eng2sp {'one': 'uno', 'three': 'tres', 'two': 'dos'} The order of key-value pairs is not the same Satyaki Sikdar© Programming in Python April 22 2016 81 / 137

255. hour 3: data structures 101 dictionary manipulations constraints on keys and values Keys in a dictionary must be immutable. So, lists, sets, dictionaries cannot be keys Ints, ﬂoats, strings, tuples, frozensets are ﬁne! There’s no constraint on the nature of values >>> house_members = {'Stark': ['Ned', 'Robb', 'Jon', 'Sansa', 'Arya'], 'Lannister': ['Tyrion', 'Tywin', 'Cersei', 'Jaime'], 'Greyjoy': ['Theon', 'Yara', 'Balon']} >>> print house_members['Greyjoy'] ['Theon', 'Yara', 'Balon'] Satyaki Sikdar© Programming in Python April 22 2016 82 / 137

258. hour 3: data structures 101 dictionary manipulations Indexing is done solely using the keys >>> print eng2sp['two'] dos If the key isn’t in the dictionary, you get an exception >>> print eng2sp['four'] KeyError: 'four' len function returns the number of key-value pairs >>> len(eng2sp) 3 in tells you whether something appears as a key in the dictionary (appearing as a value is not good enough) >>> 'one' in eng2sp >>> 'uno' in eng2sp True False Satyaki Sikdar© Programming in Python April 22 2016 83 / 137

262. hour 3: data structures 101 dictionary manipulations deleting things A speciﬁc key can be deleted from a dictionary by using pop >>> eng2sp {'one': 'uno', 'two': 'dos', 'three': 'tres'} >>> eng2sp.pop('two') 'dos' >>> eng2sp {'one': 'uno', 'three': 'tres'} A key at random can be deleted by using popitem >>> eng2sp {'one': 'uno', 'two': 'dos', 'three': 'tres'} >>> eng2sp.popitem() ('three', 'tres') Satyaki Sikdar© Programming in Python April 22 2016 84 / 137

263. hour 3: data structures 101 dictionary manipulations deleting things A speciﬁc key can be deleted from a dictionary by using pop >>> eng2sp {'one': 'uno', 'two': 'dos', 'three': 'tres'} >>> eng2sp.pop('two') 'dos' >>> eng2sp {'one': 'uno', 'three': 'tres'} A key at random can be deleted by using popitem >>> eng2sp {'one': 'uno', 'two': 'dos', 'three': 'tres'} >>> eng2sp.popitem() ('three', 'tres') Satyaki Sikdar© Programming in Python April 22 2016 84 / 137

264. hour 3: data structures 101 dictionary manipulations Dicts have a method called items that returns a list of tuples of key-value pairs >>> d = {'a': 0, 'b': 1, 'c': 2} >>> t = d.items() >>> print t [('a', 0), ('c', 2), ('b', 1)] Going in the other way, you can use a list of tuples to init a new dict >>> t = [('a', 0), ('c', 2), ('b', 1)] >>> d = dict(t) >>> print d {'a': 0, 'c': 2, 'b': 1} Combining dict with zip yields a concise way >>> d = dict(zip('abc', range(3))) >>> print d {'a': 0, 'c': 2, 'b': 1} Satyaki Sikdar© Programming in Python April 22 2016 85 / 137

267. hour 3: data structures 101 counters counters Given a string and you want to count how many times each letter appears 1 create 26 variables, one for each letter of the alphabet 2 create a list with 26 elements. Then convert each character to a number and use it as an index into the list 3 create a dict with characters as keys and counters as the values def hist(st): counter = dict() for c in st: if c not in counter: counter[c] = 1 else: counter[c] += 1 return counter >>> print hist('brontosaurus') {'a': 1, 'b': 1, 'o': 2, 'n': 1, 's': 2, 'r': 2, 'u': 2, 't': 1}Satyaki Sikdar© Programming in Python April 22 2016 86 / 137

271. hour 3: data structures 101 counters inverting a dictionary Given a dict that maps letters to freq, create a dict that maps freq to letters Several letters may have the same freq, so each value must be a list def inv_dict(d): inv = {} for key in d: val = d[key] if val not in inv: #we haven't seen the value so far inv[val] = [key] else: inv[val].append(key) return val >>> h = hist('parrot') >>> print inv_dict(h) >>> print h {1: ['a', 'p', 't', 'o'], 2: ['r']} {'a': 1, 'p': 1, 'r': 2, 't': 1, 'o': 1} Satyaki Sikdar© Programming in Python April 22 2016 87 / 137

272. hour 3: data structures 101 counters inverting a dictionary Given a dict that maps letters to freq, create a dict that maps freq to letters Several letters may have the same freq, so each value must be a list def inv_dict(d): inv = {} for key in d: val = d[key] if val not in inv: #we haven't seen the value so far inv[val] = [key] else: inv[val].append(key) return val >>> h = hist('parrot') >>> print inv_dict(h) >>> print h {1: ['a', 'p', 't', 'o'], 2: ['r']} {'a': 1, 'p': 1, 'r': 2, 't': 1, 'o': 1} Satyaki Sikdar© Programming in Python April 22 2016 87 / 137

273. hour 4: confusion 101 recursion Satyaki Sikdar© Programming in Python April 22 2016 88 / 137

274. hour 4: confusion 101 introduction what is it anyway? It’s a method where the solution to a problem depends on solutions to smaller instances of the same problem Some problems are inherently recursive Let’s see an example. The legal code of the US deﬁnes a natural born citizen roughly as follows: Any child born inside the United States Any child born in wedlock outside the United States both of whose parents are citizens of the US, as long as one parent has lived in the US prior to the birth of the child, and Any child born in wedlock outside the US one of whose parents is a US citizen who has lived at least ﬁve years, .... so on Satyaki Sikdar© Programming in Python April 22 2016 89 / 137

279. hour 4: confusion 101 the structure The structure of a generic recursive code def rec_function(parameters): if bool_expr1: # base case 1 return something if bool_expr2: # base case 2 return something_else do_something_funky inductive_case #call rec_function() with reduced parameters IMPORTANT: 1 The base cases must terminate the ﬂow of code and return something meaningful 2 The inductive case must be called for the reduced problem, such that it reaches the base case sometime Satyaki Sikdar© Programming in Python April 22 2016 90 / 137

280. hour 4: confusion 101 illustrations example 1 Mathematically, n! = n ∗ (n − 1)! if n > 1 1 otherwise def factR(n): ''' computes n! recursively n >=0 ''' if n <= 1: return 1 else: return n * factorial(n - 1) def factI(n): ''' computes n! iteratively n >=0 ''' result = 1 while n > 1: result = result * n n -= 1 return result Satyaki Sikdar© Programming in Python April 22 2016 91 / 137

281. hour 4: confusion 101 illustrations how does it work? Say you are calling factorial(3). Let’s see what happens! factorial(3) is called. It returns 3 x factorial(2). factorial(3) => factorial(2) factorial(2) is called. It returns 2 x factorial(1). factorial(2) => factorial(1) factorial(1) is called. Hits the base case! No further calls to factorial. Time to track back. factorial(1) returns 1 factorial(2) is calculated as 2 x factorial(1) and we know what factorial is, so factorial(2) returns 2 x 1 factorial(3) can be calculated as we know factorial(2) and it returns 3 x factorial(2) => 3 x 2 => 6 And we are done! Satyaki Sikdar© Programming in Python April 22 2016 92 / 137

286. hour 4: confusion 101 recursion woes base cases gone wrong! n! = (n+1)! n+1 if n > 0 1 otherwise Although this seems valid, but there lies a problem. Look at the base case! Will it ever be reached? If we implement this function, we will get a run time error. Let’s see what it is. Satyaki Sikdar© Programming in Python April 22 2016 93 / 137

287. hour 4: confusion 101 recursion woes implementation def factorial(n): if n == 0: return 1 else: return factorial(n + 1) / (n + 1) >>> factorial(4) => Something nasty! RuntimeError: maximum recursion depth exceeded It’s no surprise that factorial(4) fails. The same thing happens whenever the argument is non zero. So, who’s to blame? The innocent looking recurrence relation? Yeah Satyaki Sikdar© Programming in Python April 22 2016 94 / 137

288. hour 4: confusion 101 recursion woes implementation def factorial(n): if n == 0: return 1 else: return factorial(n + 1) / (n + 1) >>> factorial(4) => Something nasty! RuntimeError: maximum recursion depth exceeded It’s no surprise that factorial(4) fails. The same thing happens whenever the argument is non zero. So, who’s to blame? The innocent looking recurrence relation? Yeah Satyaki Sikdar© Programming in Python April 22 2016 94 / 137

289. hour 4: confusion 101 recursion woes how to fix this mess?! Can the base case ever be reached with n > 0 ? NO! So, as a consequence, the program doesn’t stop until you run out of stack space! Stack overﬂow is the recursive equivalent to an inﬁnite loop in iteration Fix the recurrence relation to get the correct results. Satyaki Sikdar© Programming in Python April 22 2016 95 / 137

293. hour 4: confusion 101 fibonacci numbers they breed like bunnies! Leonardo of Pisa, known as Fibonacci quantiﬁed this notion, albeit with some terrible assumptions The assumptions Bunnies are able to mate when they are 1 month old They have a 1 month gestation period They are immortal Each female gives birth to one male and one female bunnies every time Two bunnies, a male and a female are put in an exhibit The question is, how many pregnant bunnies would there be at the end of say n months? Month n Females Fn 0 1 1 1 2 2 3 3 4 5 5 8 6 13 Satyaki Sikdar© Programming in Python April 22 2016 96 / 137

301. hour 4: confusion 101 fibonacci numbers Fibonacci numbers Fn =    Fn−1 + Fn−2 n 2 1 n = 1 0 n = 0 def fibo(n): if n == 0: return 0 elif n == 1: return 1 else: return fibo(n - 1) + fibo(n - 2) Satyaki Sikdar© Programming in Python April 22 2016 97 / 137

302. hour 4: confusion 101 recursion tree recursion tree Figure: Recursion tree for ﬁb(7) Satyaki Sikdar© Programming in Python April 22 2016 98 / 137

303. hour 4: confusion 101 speeding up speed of computation Try to print the 40th Fibonacci number. It takes time doesn’t it? Can you ﬁgure out why? Let’s take another look at the recursion tree Aren’t we doing the same calculations in between? Is there a better way? Hell yeah! Satyaki Sikdar© Programming in Python April 22 2016 99 / 137

306. hour 4: confusion 101 speeding up a bottom-up approach def fib_v2(n): ans = [0, 1] for i in xrange(2, n + 1): ans.append(ans[i - 1] + ans[i - 2]) return ans[n] Is this approach better? YES! Are we saving computations? Yeah, the ans list stores the previous results. It takes only 2 calls to the ans list. Try calculating the 40th Fibonacci number again! Much faster, isn’t it? Satyaki Sikdar© Programming in Python April 22 2016 100 / 137

309. hour 5: oops-a-daisy intro to OOP object oriented programming So far, we have seen a lot of Python’s own data structures and types. What if we need specialized structures which are not natively provided in Py? We have the power of deﬁning our own type! Let’s see an example. Say we want to represent a point in 2-D space. What are our options? Store the coordinates x and y in two variables x and y Store the coordinates as elements in a list or tuple Create a new type to represent points as specialized things called objects Creating a new type is (a little) more complicated than the other options, but it has advantages that will be apparent soon Satyaki Sikdar© Programming in Python April 22 2016 101 / 137

316. hour 5: oops-a-daisy class classes A user-defined type is also called a class. A class defintion looks like this class Point: """Represents a point in 2-D space.""" This header indicates that the new class is a Point The body is a docstring that explains what the class is for We can define variables and functions inside a class definition The class is like a factory for creating objects To create a Point, you call Point as if it were a function. It is called instantiation and the object is an instance of the class >>> blank = Point() >>> print blank <__main__.Point instance at 0xb7e9d3ac> Satyaki Sikdar© Programming in Python April 22 2016 102 / 137

322. hour 5: oops-a-daisy class attributes You can assign values to an instance using dot notation >>> blank.x = 3.0 >>> blank.y = 4.0 You can read the value of an attribute using the same syntax >>> print blank.y 4.0 >>> x = blank.x >>> print x 3.0 Figure: Object diagram The expression blank.x means, “Go to the object blank refers to and get the value of x” In this case, we assign that value to a variable named x. There is no conﬂict between the variable x and the attribute x Satyaki Sikdar© Programming in Python April 22 2016 103 / 137

326. hour 5: oops-a-daisy class If you try to access an attribute that doesn’t exist, you get an AttributeError If you are not sure what type an object is, you can use the type function If you are not sure whether an object has a particular attribute, you can use the built-in function hasattr >>> p = Point() >>> p.x = 5.5 >>> p.y = 2.2 >>> print p.x 5.5 >>> print p.y 2.2 >>> print p.z AttributeError: Point instance has no attribute 'z' >>> type(p) <type '__main__'.Point'> >>> hasattr(p, 'x') True >>> hasattr(p, 'z') False Satyaki Sikdar© Programming in Python April 22 2016 104 / 137

329. hour 5: oops-a-daisy manipulating objects Dot notations are powerful. They can be used in any context >>> print '(%g, %g)' % (blank.x, blank.y) (3.0, 4.0) >>> import math >>> print math.sqrt(blank.x ** 2 + blank.y ** 2) 5.0 You can pass an instance as an argument in the usual way def print_point(p): print '(%g, %g)' % (p.x, p.y) print_point takes a Point as an arg and prints it in mathematical notation. To invoke it, you pass blank as an arg >>> print_point(blank) (3.0, 4.0) Inside the function, p is an alias for blank, so if the function modiﬁes p, blank changes Satyaki Sikdar© Programming in Python April 22 2016 105 / 137

333. hour 5: oops-a-daisy manipulating objects def dist_bw_points(p1, p2): return math.sqrt((p1.x - p2.x) ** 2 + (p1.y - p2.y) ** 2)) Sometimes it is obvious what the attributes of an object should be, but other times you have to make decisions For example, imagine you are designing a class to represent rectangles. What attributes would you use to specify the location and size of a rectangle? There are at least two possibilities: specify one corner of the rectangle, the width, and the height specify two opposing corners Following the ﬁrst one, here’s an example of the class deﬁnition. Corner represents the lower-left corner class Rectangle: '''Represents a rectangle. attributes: width, height, corner. ''' Satyaki Sikdar© Programming in Python April 22 2016 106 / 137

339. hour 5: oops-a-daisy manipulating objects To represent a rectangle, you have to instantiate a Rectangle object and assign values to the attributes >>> box = Rectangle() >>> box.width = 100.0 >>> box.height = 200.0 >>> box.corner = Point() >>> box.corner.x = 0.0 >>> box.corner.y = 0.0 Figure: Object diagram for box The expression box.corner.x means, “Go to the object box refers to and select the attribute named corner; then go to that object and select the attribute named x” Satyaki Sikdar© Programming in Python April 22 2016 107 / 137

340. hour 5: oops-a-daisy manipulating objects To represent a rectangle, you have to instantiate a Rectangle object and assign values to the attributes >>> box = Rectangle() >>> box.width = 100.0 >>> box.height = 200.0 >>> box.corner = Point() >>> box.corner.x = 0.0 >>> box.corner.y = 0.0 Figure: Object diagram for box The expression box.corner.x means, “Go to the object box refers to and select the attribute named corner; then go to that object and select the attribute named x” Satyaki Sikdar© Programming in Python April 22 2016 107 / 137

341. hour 5: oops-a-daisy manipulating objects Functions can return instances find_center takes a Rectangle as an argument and returns a Point that contains the coordinates of the center of the Rectangle def find_center(rect): p = Point() p.x = rect.corner.x + rect.width / 2.0 p.y = rect.corner.y + rect.height / 2.0 return p >>> center = find_center(box) >>> print_point(center) (50.0, 100.0) Satyaki Sikdar© Programming in Python April 22 2016 108 / 137

342. hour 5: oops-a-daisy manipulating objects Functions can return instances find_center takes a Rectangle as an argument and returns a Point that contains the coordinates of the center of the Rectangle def find_center(rect): p = Point() p.x = rect.corner.x + rect.width / 2.0 p.y = rect.corner.y + rect.height / 2.0 return p >>> center = find_center(box) >>> print_point(center) (50.0, 100.0) Satyaki Sikdar© Programming in Python April 22 2016 108 / 137

343. hour 5: oops-a-daisy manipulating objects You can change the state of an object by making an assignment to one of its attributes You can also write functions that modify objects def grow_rectangle(rect, dwidth, dheight): rect.width += dwidth rect.height += dheight >>> print box.width 100.0 >>> print box.height 200.0 >>> grow_rectangle(box, 50, 100) >>> print box.width 150.0 >>> print box.height 300.0 Satyaki Sikdar© Programming in Python April 22 2016 109 / 137

344. hour 5: oops-a-daisy manipulating objects You can change the state of an object by making an assignment to one of its attributes You can also write functions that modify objects def grow_rectangle(rect, dwidth, dheight): rect.width += dwidth rect.height += dheight >>> print box.width 100.0 >>> print box.height 200.0 >>> grow_rectangle(box, 50, 100) >>> print box.width 150.0 >>> print box.height 300.0 Satyaki Sikdar© Programming in Python April 22 2016 109 / 137

345. hour 5: oops-a-daisy manipulating objects So far, the classes have been a little boring with just the definition and a docstring. Can we do more? Let’s find ou class Point: '''Represents a point in 2-D space.''' def print_point(p): print '(%g, %g)' % (p.x, p.y) >>> pt = Point() >>> pt.x = 3.5 >>> pt.y = 0 >>> Point.print_point(pt) #less common (3.5, 0) >>> pt.print_point() #widely used (3.5, 0) class Point: '''Represents a point in 2-D space.' def print_point( self ): print '(%g, %g)'%(self.x, self.y >>> pt = Point() >>> pt.x = 3.5 >>> pt.y = 0 >>> Point.print_point(pt) #less common (3.5, 0) >>> pt.print_point() #widely used (3.5, 0) By convention, the first arg of every function in a class is set to self Satyaki Sikdar© Programming in Python April 22 2016 110 / 137

346. hour 5: oops-a-daisy manipulating objects So far, the classes have been a little boring with just the definition and a docstring. Can we do more? Let’s find ou class Point: '''Represents a point in 2-D space.''' def print_point(p): print '(%g, %g)' % (p.x, p.y) >>> pt = Point() >>> pt.x = 3.5 >>> pt.y = 0 >>> Point.print_point(pt) #less common (3.5, 0) >>> pt.print_point() #widely used (3.5, 0) class Point: '''Represents a point in 2-D space.' def print_point( self ): print '(%g, %g)'%(self.x, self.y >>> pt = Point() >>> pt.x = 3.5 >>> pt.y = 0 >>> Point.print_point(pt) #less common (3.5, 0) >>> pt.print_point() #widely used (3.5, 0) By convention, the first arg of every function in a class is set to self Satyaki Sikdar© Programming in Python April 22 2016 110 / 137

347. hour 5: oops-a-daisy manipulating objects There’s no upper bound of the number of functions inside a class The ﬁrst argument self is implicit, for example class Point: '''Represents a point in 2-D space.''' def print_point( self ): print '(%g, %g)'%(self.x, self.y) def transl(self, dx, dy): self.x += dx self.y += dy >>> pt = Point() >>> pt.transl(10, 20) #fine >>> pt.x = 6.0 >>> pt.transl(10, 20, 30) #not fine >>> pt.y = 8.0 TypeError: transl() takes exactly 3 arguments (4 given) >>> pt.print_point() (6.0, 8.0) Satyaki Sikdar© Programming in Python April 22 2016 111 / 137

348. hour 5: oops-a-daisy manipulating objects There’s no upper bound of the number of functions inside a class The ﬁrst argument self is implicit, for example class Point: '''Represents a point in 2-D space.''' def print_point( self ): print '(%g, %g)'%(self.x, self.y) def transl(self, dx, dy): self.x += dx self.y += dy >>> pt = Point() >>> pt.transl(10, 20) #fine >>> pt.x = 6.0 >>> pt.transl(10, 20, 30) #not fine >>> pt.y = 8.0 TypeError: transl() takes exactly 3 arguments (4 given) >>> pt.print_point() (6.0, 8.0) Satyaki Sikdar© Programming in Python April 22 2016 111 / 137

349. hour 5: oops-a-daisy special methods the init method The init method is a special method that gets invoked when an object is created The full name is __init__, two underbars followed by init followed by two more underbars The parameters for init usually have the same name as the attributes The parameters here, are optional #inside class Point def __init__(self, x=0.0, y=0.0): self.x = x self.y = y #outside the class >>> p1 = Point() >>> p2 = Point(2, 3.4) #also allowed >>> p1.print_point() >>> p2.print_point() (0.0, 0.0) (2, 3.4) Satyaki Sikdar© Programming in Python April 22 2016 112 / 137

353. hour 5: oops-a-daisy special methods the str method __str__ is a special method like __init__ and it’s supposed to return a string representation of the object Takes just the object as the argument For the Point class, the str method can be like #inside the class Point def __str__(self): #allows you to use the print statement return '(%g, %g)' % (self.x, self.y) #outside the class >>> p = Point(2.0, 3.0) >>> print p (2.0, 3.0) Satyaki Sikdar© Programming in Python April 22 2016 113 / 137

356. hour 5: oops-a-daisy special methods the add method __add__ is a special method like __str__ and it overloads the ’+’ operator Takes two arguments, the caller object and another object. Returns a new object that’s the sum of the two objects For the Point class, the add method can be like #inside the class Point def __add__(self, other_pt): new_pt = Point() new_pt.x = self.x + other_pt.x new_pt.y = self.y + other_pt.y return new_pt #outside the class >>> p1 = Point(2.0, 3.0) >>> print p1 + p2 >>> p2 = Point(3.4, 8.2) (5.4, 11.2) Other special methods incl __sub__, __lt__, __le__, __eq__, etc.Satyaki Sikdar© Programming in Python April 22 2016 114 / 137

360. hour 5: oops-a-daisy an elaborate example another example There are 52 cards in a deck. There are 4 suits - Spades, Hearts, Diamonds and Clubs Each suit has 13 cards - Ace, 2, ..., 10, Jack, Queen and King We’ll create a card class. Attributes can be strings, like ’Spade’ for suits and ’Queen’ for ranks. We’ll have trouble comparing the cards We use integers to encode the ranks and suits Spades → 3, Hearts → 2, Diamonds → 1 and Clubs → 0 Ace → 1, Jack → 11, Queen → 12 and King → 13 Satyaki Sikdar© Programming in Python April 22 2016 115 / 137

366. hour 5: oops-a-daisy an elaborate example the class definition class Card: '''Represents a standard playing card''' suit_names = ['Clubs', 'Diamonds', 'Hearts', 'Spades'] rank_names = [None, 'Ace', '2', '3', '4', '5', '6', '7', '8', '9', '10', 'Jack', 'Queen', 'King'] def __init__(self, suit=0, rank=2): self.suit = suit self.rank = rank def __str__(self): return '%s of %s' % (Card.rank_names[self.rank], Card.suit_names[self.suit]) >>> two_of_clubs = Card() >>> queen_of_diamonds = Card(1, 12) Satyaki Sikdar© Programming in Python April 22 2016 116 / 137

367. hour 5: oops-a-daisy an elaborate example class and instance attributes class attribute instance attribute Deﬁned outside any method Deﬁned inside methods Referred by class.class_attr Referred by inst.inst_attr One copy per class One copy per instance Eg: suit_names and rank_names Eg: suit and rank Figure: Class and instance attributes Satyaki Sikdar© Programming in Python April 22 2016 117 / 137

368. hour 5: oops-a-daisy an elaborate example comparing cards For built-in types, there are relational operators (<, >, ==, etc.) that compare two things to produce a boolean For user-deﬁned types, we need to override the __cmp__ method. It takes in two parameters, self and other, returns a positive number if the ﬁrst object is greater a negative number if the second object is greater zero if they are equal The ordering for cards is not obvious. Which is better, the 3 of Clubs or the 2 of Diamonds? One has a higher rank, but the other has a higher suit We arbitrarily choose that suit is more important, so all the Spades outrank all the Diamonds and so on. Satyaki Sikdar© Programming in Python April 22 2016 118 / 137

375. hour 5: oops-a-daisy an elaborate example writing the __cmp__ method #inside Card class def __cmp__(self, other): if self.suit > other.suit: #check the suits return 1 elif self.suit < other.suit: return -1 elif self.rank > other.rank: #check the ranks return 1 elif self.rank < other.rank: return -1 else: #both the suits and the ranks are the same return 0 Satyaki Sikdar© Programming in Python April 22 2016 119 / 137

376. hour 5: oops-a-daisy an elaborate example decks Now that we have Cards, we deﬁne Decks. It will contain a list of Cards The init method creates the entire deck of 52 cards class Deck: '''Represents a deck of cards''' def __init__(self): self.cards = [] for suit in range(4): for rank in range(1, 14): card = Card(suit, rank) self.cards.append(card) Satyaki Sikdar© Programming in Python April 22 2016 120 / 137

377. hour 5: oops-a-daisy an elaborate example decks Now that we have Cards, we deﬁne Decks. It will contain a list of Cards The init method creates the entire deck of 52 cards class Deck: '''Represents a deck of cards''' def __init__(self): self.cards = [] for suit in range(4): for rank in range(1, 14): card = Card(suit, rank) self.cards.append(card) Satyaki Sikdar© Programming in Python April 22 2016 120 / 137

378. hour 5: oops-a-daisy an elaborate example decks #inside class Deck def __str__(self): res = [] for card in self.cards: res.append(str(card)) return 'n'.join(res) def shuffle(self): random.shuffle(self.cards) #inside class Deck def pop_card(self): return self.cards.pop() def add_card(self, card): self.cards.append(card) def sort(self): self.cards.sort() >>> deck = Deck() >>> print deck.pop_card() King of Spades Satyaki Sikdar© Programming in Python April 22 2016 121 / 137

379. hour 5: oops-a-daisy inheritance inheritance The language feature most often associated with object-oriented programming is inheritance It’s the ability to define a new class that’s a modified version of an existing class The existing class is called the parent and the new class is called the child We want a class to represent a hand that is, the set of cards held by a player A hand is similar to a deck: both are made up of a set of cards, and both require operations like adding and removing cards A hand is also different from a deck; there are operations we want for hands that don’t make sense for a deck Satyaki Sikdar© Programming in Python April 22 2016 122 / 137

385. hour 5: oops-a-daisy inheritance The definition of a child class is like other class definitions, but the name of the parent class appears in parentheses class Hand(Deck): '''Represents a hand of playing cards''' This definition indicates that Hand inherits from Deck; that means we can use methods like pop_card and add_card for Hands as well as Decks Hand also inherits __init__ from Deck, but it doesn’t really do what we want: the init method for Hands should initialize cards with an empty list We can provide an init method, overriding the one in Deck #inside class Hand def __init__(self, label=''): self.cards = [] self.label = label Satyaki Sikdar© Programming in Python April 22 2016 123 / 137

389. hour 5: oops-a-daisy inheritance So when you create a Hand, Python invokes it’s own init >>> hand = Hand('new hand') >>> print hand.cards [] >>> print hand.label new hand But the other methods are inherited from Deck >>> deck = Deck() >>> card = deck.pop_card() >>> hand.add_card(card) #add_card from Hand >>> print hand #using the str of Hand King of Spades A natural next step is to encapsulate this code in a method called move_cards #inside class Deck def move_cards(self, hand, card): for i in xrange(num): hand.add_card(self.pop_card()) move_cards takes two arguments, a Hand object and the number of cards to deal. Modiﬁes both self and hand Satyaki Sikdar© Programming in Python April 22 2016 124 / 137

393. hour 5: oops-a-daisy inheritance #inside class Deck def deal_hands(self, num_hands, cards_per_hand): hands = [] self.shuffle() #shuffling the deck for i in range(num_hands): hand = Hand('player %d' % (i)) for j in range(cards_per_hand): hand.add_card(self.pop_card()) hands.append(hand) return hands Now you have a proper framework for a card game, be it poker, blackjack or bridge! Satyaki Sikdar© Programming in Python April 22 2016 125 / 137

394. hour 5: oops-a-daisy file handling 101 the need for file handling Most of the programs we have seen so far are transient in the sense that they run for a short time and produce some output, but when they end, their data disappears. If you run the program again, it starts with a clean slate Other programs are persistent: they run for a long time (or all the time); they keep at least some of their data in permanent storage (a hard drive, for example); if they shut down and restart, they pick up where they left oﬀ Big input and output sizes - too big for the main memory Satyaki Sikdar© Programming in Python April 22 2016 126 / 137

397. hour 5: oops-a-daisy file handling 101 Examples of persistent programs are operating systems, which run pretty much whenever a computer is on, and web servers, which run all the time, waiting for requests to come in on the network. One of the simplest ways for programs to maintain their data is by reading and writing text ﬁles. fp_read = open('input.txt', 'r') fp_write = open('output.txt', 'w') Satyaki Sikdar© Programming in Python April 22 2016 127 / 137

398. hour 5: oops-a-daisy file handling 101 Examples of persistent programs are operating systems, which run pretty much whenever a computer is on, and web servers, which run all the time, waiting for requests to come in on the network. One of the simplest ways for programs to maintain their data is by reading and writing text ﬁles. fp_read = open('input.txt', 'r') fp_write = open('output.txt', 'w') Satyaki Sikdar© Programming in Python April 22 2016 127 / 137

399. hour 5: oops-a-daisy file handling 101 reading from files The built-in function open takes the name of the file as a parameter and returns a file object you can use to read the file >>> fin = open('input.txt', 'r') >>> print fin >>> <open file 'input.txt', mode 'r' at 0xb7eb2410> A few things to note: The file opened must exist. An IOError is thrown otherwise. The exact path to the file must be provided which includes the correct filename with extension (if any) Satyaki Sikdar© Programming in Python April 22 2016 128 / 137

403. hour 5: oops-a-daisy file handling 101 reading files The file object provides several methods for reading, including readline, which reads characters from the file until it gets to a newline and returns the result as a string: >>> fin.readline() 'the first line n' If you keep on doing fin.readlines(), you’d end up reading the whole file, one line at a time. Let’s see a few examples of reading files. Satyaki Sikdar© Programming in Python April 22 2016 129 / 137

404. hour 5: oops-a-daisy file handling 101 writing to files >>> fout = open('output.txt', 'w') >>> print fout <open file 'output.txt', mode 'w' at 0xb7eb2410> If the file already exists, opening it in write mode clears out the old data and starts fresh, so be careful! If the file doesn’t exist, a new one is created >>> line1 = 'He left yesterday behind him, you might say he was born again, >>> fout.write(line1) Again, the file object keeps track of where it is, so if you call write again, it adds the new data to the end >>> line2 = 'you might say he found a key for every door.n' >>> fout.write(line2) Satyaki Sikdar© Programming in Python April 22 2016 130 / 137

405. hour 5: oops-a-daisy file handling 101 using files for something meaningful Let’s combine the knowledge of ﬁle handling with dictionaries to do some basic lexical analysis import string def char_freq(filename): counter = dict() with open(filename, 'r') as f: raw_text = f.read() for c in raw_text: c = c.lower() if c in string.ascii_lowercase: if c in counter: counter[c] += 1 else: counter[c] = 1 return counter def normalize(counter): sum_values = float(sum(counter.values())) for key in counter: counter[key] /= sum_values return counter Satyaki Sikdar© Programming in Python April 22 2016 131 / 137

406. hour 5: oops-a-daisy algorithms in Python algorithm design in Python One of the strong points of Python is the ease of expression Turning pseudocode into actual code is not diﬃcult Let’s try to implement the Merge Sort algorithm in Python A high level idea of the algorithm Divide: Divide the n-element sequence into two subsequences of n 2 elements Conquer: Sort the subsequences recursively Combine: Merge the two sorted subsequences to produce the sorted answer Satyaki Sikdar© Programming in Python April 22 2016 132 / 137

412. hour 5: oops-a-daisy the pseudocode Algorithm 1: MERGE(left, right) begin Append ∞ to left and right i ← 0, j ← 0 merged ← new list while len(merged) < len(left) + len(right) do if left[i] < right[j] then merged.append(left[i]) i ← i + 1 else merged.append(right[j]) j ← j + 1 return merged Algorithm 2: MERGE-SORT(A) begin if len(A) < 2 then return A else left ← ﬁrst n 2 elements of A right ← last n 2 elements of A left ← MERGE − SORT(left) right ← MERGE − SORT(right) return MERGE(left, right) Satyaki Sikdar© Programming in Python April 22 2016 133 / 137

413. hour 5: oops-a-daisy the pseudocode the core idea The algorithm is naturally recursive The MERGE method takes two sorted lists and merges into a single sorted list MERGE − SORT sorts the list recursively by breaking it into equal sized halves and sorting them A list having less than 2 elements is trivially sorted - base case Smaller sorted lists are agglomerated to form the overall sorted list Satyaki Sikdar© Programming in Python April 22 2016 134 / 137

418. hour 5: oops-a-daisy implementation Algorithm 3: MERGE(left, right) begin Append ∞ to left and right i ← 0, j ← 0 merged ← new list while len(merged) < len(left) + len(right) do if left[i] < right[j] then merged.append(left[i]) i ← i + 1 else merged.append(right[j]) j ← j + 1 return merged def merge(left, right): left.append(float('inf')) right.append(float('inf')) i = 0 j = 0 merged = [] while len(merged) < len(left) + len(right): if left[i] < right[j]: merged.append(left[i]) i += 1 else: merged.append(right[j]) j += 1 return merged Satyaki Sikdar© Programming in Python April 22 2016 135 / 137

419. hour 5: oops-a-daisy implementation Algorithm 4: MERGE-SORT(A) begin if len(A) < 2 then return A else left ← ﬁrst n 2 elements of A right ← last n 2 elements of A left ← MERGE − SORT(left) right ← MERGE − SORT(right) return MERGE(left, right) def merge_sort(A): if len(A) < 2: return A else: mid = len(A) / 2 left = A[: mid] right = A[mid: ] left = merge_sort(left) right = merge_sort(right) return merge(left, right) Satyaki Sikdar© Programming in Python April 22 2016 136 / 137

420. hour 5: oops-a-daisy implementation That’s all for today! Hope you had fun! Make sure you have a working networkx setup for tomorrow... Satyaki Sikdar© Programming in Python April 22 2016 137 / 137

A 12 hour workshop on Introduction to Python

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A 12 hour workshop on Introduction to Python