python 01 September 16, 2016

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1 python 01 September 16, Introduction to Python adapted from Steve Phelps lectures - ( 2 Python is interpreted Python is an interpreted language (Java and C are not). In [1]: Out[1]: 9 good for scripting we can have a shell (interactive mode) In [2]: x = 2 y = Ciao z = 1.2 In [3]: x + 5 Out[3]: 7 3 Assignments versus equations Python is imperative when we write x = 5 this means something different from an equation x = 5. variables in Python can refer to different things as more statements are interpreted. In [4]: x = 1 print( The value of x is, x) x = 2.5 print( Now the value of x is, x) x = hello there print( Now it is, x) The value of x is 1 Now the value of x is 2.5 Now it is hello there 1

2 4 Calling Functions We can call functions in a conventional way using round brackets In [5]: print(round(3.14)) 3 5 Types Values in Python have an associated type. combining types incorrectly we get an error. In [6]: print(y) Ciao In [7]: y TypeError Traceback (most recent call last) <ipython-input-7-c3d4037f4656> in <module>() ----> 1 y + 5 TypeError: Can t convert int object to str implicitly 6 The type function We can query the type of a value using the type function. In [8]: type(1) Out[8]: int In [9]: type( ciao ) Out[9]: str In [10]: type(2.5) Out[10]: float In [11]: type(true) Out[11]: bool 2

3 7 Null values Sometimes we represent no data or not applicable. Python has the special value None. This corresponds to Null in Java or SQL. In [12]: result = None When we fetch the value None in the interactive interpreter, no result is printed out. In [13]: result We can check whether there is a result or not using the is operator: In [14]: result is None Out[14]: True In [15]: x = 5 x is None Out[15]: False 8 Converting values between types We can convert values between different types. To convert an integer to a floating-point number use the float() function. To convert a floating-point to an integer use the int() function. In [16]: x = 1 print(type(x)) print(x) <class int > 1 In [17]: y = float(x) print(type(y)) print(y) <class float > 1.0 In [18]: print(int(y)) 1 9 Variables are not typed Variables themselves do not have a fixed type. Only the values that they refer to have a type. the type referred to by a variable can change as more statements are interpreted. In [19]: y = ciao print( type referred by y is, type(y)) y = 5.0 print( type referred by y is, type(y)) type referred by y is type referred by y is <class str > <class float > 3

4 10 Polymorphism The meaning of an operator depends on the types we are applying it to. In [20]: 1 / 5 Out[20]: 0.2 In [21]: 1.0 / 5.0 Out[21]: 0.2 In [22]: Out[22]: 2 In [23]: a + b Out[23]: ab In [24]: Out[24]: Conditional Statements and Indentation The syntax for control structures in Python use colons and indentation. Beware that white-space affects the semantics of Python code. In [25]: x = 5 if x > 0: print( x is strictly positive ) print(x) print( finished ) x is strictly positive 5 finished In [26]: x = 0 if x > 0: print( x is strictly positive ) print(x) 0 finished 12 Lists print( finished ) We can use lists to hold an ordered sequence of values. In [27]: l = [ first, second, third ] l 4

5 Out[27]: [ first, second, third ] Lists can contain different types of variable, even in the same list. In [28]: another_list = [ first, second, third, 1, 2, 3] another_list Out[28]: [ first, second, third, 1, 2, 3] 13 Mutable Datastructures Lists are mutable - their contents can change as more statements are interpreted In [29]: l.append( fourth ) l Out[29]: [ first, second, third, fourth ] 14 References Whenever we bind a variable to a value in Python we create a reference. A reference is distinct from the value that it refers to. Variables are names for references. In [30]: X = [1, 2, 3] Y = X X and Y are two references to the same value [1, 2, 3] Because lists are mutable, changing them can have side-effects on other variables. If we append something to X what will happen to Y? In [31]: X.append(4) X Out[31]: [1, 2, 3, 4] In [32]: Y Out[32]: [1, 2, 3, 4] 15 State and identity The state referred to by a variable is different from its identity. To compare state use the == operator. To compare identity use the is operator. When we compare identity we check equality of references. When we compare state we check equality of values. In [33]: X = [1, 2] Y = [1] Y.append(2) 5

6 In [34]: X == Y Out[34]: True In [35]: X is Y Out[35]: False In [36]: X = Y In [37]: X is Y Out[37]: True 16 Iteration for loop iterates over each element of a list In [38]: for i in l: print(i) first second third fourth iterate with index In [39]: for i,element in enumerate([0, a, 5, last_element ]): print("index " + str(i) + " - " + str(element)) index 0-0 index 1 - a index 2-5 index 3 - last element 17 For loops with the range function The function range() counts for us (starting at 0). In [40]: range(4) Out[40]: range(0, 4) In [41]: for i in range(4): print("string: " + str(i)) string: 0 string: 1 string: 2 string: 3 18 List Indexing Lists can be indexed using square brackets to retrieve the element stored in a particular position. In [42]: print(l[0]) first In [43]: print(l[1]) second 6

7 19 List Slicing We can also a specify a range of positions. This is called slicing. The example below indexes from position 0 (inclusive) to 2 (exclusive). In [44]: print(l[0:2]) [ first, second ] If we leave out the starting index it implies the beginning of the list: In [45]: print(l[:2]) [ first, second ] If we leave out the final index it implies the end of the list: In [46]: l[2:] Out[46]: [ third, fourth ] 20 Negative Indexing Negative indices count from the end of the list: In [47]: l[-1] Out[47]: fourth In [48]: l[:-1] Out[48]: [ first, second, third ] By step e.g. 2 with ::2 In [49]: l[0::2] Out[49]: [ first, third ] In [50]: l[-1::-2] Out[50]: [ fourth, second ] 21 Collections Lists are an example of a collection. A collection is a type of value that can contain other values. There are other collection types in Python: tuple set dict 7

8 22 Tuples Tuples are another way to combine different values. The combined values can be of different types. Like lists, they have a well-defined ordering and can be indexed. To create a tuple in Python, use round brackets instead of square brackets In [51]: tuple1 = (50, hello ) tuple1 Out[51]: (50, hello ) In [52]: tuple1[0] Out[52]: 50 In [53]: type(tuple1) Out[53]: tuple 23 Tuples are immutable Unlike lists, tuples are immutable. Once we have created a tuple we cannot add values to it. In [54]: tuple1.append(2) AttributeError Traceback (most recent call last) <ipython-input-54-a29bee310764> in <module>() ----> 1 tuple1.append(2) AttributeError: tuple object has no attribute append 24 Sets Lists can contain duplicate values. A set, in contrast, contains no duplicates. Sets can be created from lists using the set() function. In [56]: X = set([1, 2, 3, 3, 4]) X Out[56]: {1, 2, 3, 4} In [57]: type(x) Out[57]: set Alternatively we can write a set literal using the { and } brackets. In [58]: X = {1, 2, 3, 4} type(x) Out[58]: set 8

9 25 Sets are mutable Sets are mutable like lists: In [59]: X.add(5) X Out[59]: {1, 2, 3, 4, 5} Duplicates are automatically removed In [60]: X.add(5) X Out[60]: {1, 2, 3, 4, 5} 26 Sets are unordered Sets do not have an ordering. Therefore we cannot index or slice them: In [61]: X[0] TypeError Traceback (most recent call last) <ipython-input-61-1d61f6e5db90> in <module>() ----> 1 X[0] TypeError: set object does not support indexing 27 Operations on sets Union: X Y In [ ]: X = {1, 2, 3} Y = {4, 5, 6} X.union(Y) Intersection: X Y : In [ ]: X = {1, 2, 3, 4} Y = {3, 4, 5} X.intersection(Y) Difference X Y : In [ ]: X - Y 9

10 28 Arrays Python also has fixed-length arrays which contain a single type of value i.e. we cannot have different types of value within the same array. Arrays are provided by a separate module called numpy Modules correspond to packages in e.g. Java. We can import the module and then give it a shorter alias. In [ ]: import numpy as np We can now use the functions defined in this package by prefixing them with np. The function array() creates an array given a list. In [ ]: x = np.array([0, 1, 2, 3, 4]) print(x) print(type(x)) 29 Functions over arrays When we use arithmetic operators on arrays, we create a new array with the result of applying the operator to each element. In [ ]: y = x * 2 y The same goes for functions: In [ ]: x = np.array([-1, 2, 3, -4]) y = abs(x) y 30 Populating Arrays To populate an array with a range of values we use the np.arange() function: In [ ]: x = np.arange(0, 10) x We can also use floating point increments. In [ ]: x = np.arange(0, 1, 0.1) x In [ ]: x = np.arange(1, 0, -0.2) x 31 Basic Plotting We will use a module called matplotlib to plot some simple graphs. This module provides functions which are very similar to MATLAB plotting commands. In [ ]: import matplotlib.pyplot as plt %matplotlib inline y = x*2 + 5 plt.plot(x, y) 10

11 32 Plotting a sine curve In [ ]: from numpy import pi, sin x = np.arange(0, 2*pi, 0.01) y = sin(x) plt.plot(x, y) 33 Plotting a histogram We can use the hist() function in matplotlib to plot a histogram In [ ]: # Generate some random data data = np.random.randn(1000) #data ax = plt.hist(data) #ax = plt.hist(data, bins=[-3,-1.5,-0.5,0.5,1.5,3]) # If bins is an int, it defines the number of equal-width bins in the given range (10, by defaul #If bins is a sequence, it defines the bin edges, including the rightmost edge, allowing for non 34 Computing histograms as matrices The function histogram() in the numpy module will count frequencies into bins and return the result as a 2-dimensional array. In [ ]: np.histogram(data) In [ ]: np.histogram(data, bins=5) 35 Defining new functions In [ ]: def squared(x): return x ** 2 squared(5) 36 Local Variables Variables created inside functions are local to that function. They are not accessable to code outside of that function. In [ ]: def squared(x): result = x ** 2 return result squared(5) In [ ]: print(result) 11

12 37 Functional Programming Functions are first-class citizens in Python. They can be passed around just like any other value. In [ ]: print(squared) In [ ]: y = squared print(y) In [ ]: y(5) 38 Mapping the elements of a collection We can apply a function to each element of a collection using the built-in function map(). This will work with any collection: list, set, tuple or string. This will take as an argument another function, and the list we want to apply it to. It will return the results of applying the function, as a list. In [ ]: map(squared, [1, 2, 3, 4]) # from Python 3 returns an iterator In [ ]: [i for i in map(squared, [1, 2, 3, 4])] 39 List Comprehensions Because this is such a common operation, Python has a special syntax to do the same thing, called a list comprehension. In [ ]: [squared(i) for i in [1, 2, 3, 4]] If we want a set instead of a list we can use a set comprehension In [ ]: {squared(i) for i in [1, 2, 3, 4]} 40 Cartesian product using list comprehensions The Cartesian product of two collections X = A B can be expressed by using multiple for statements in a comprehension. In [ ]: A = { x, y, z } B = {1, 2, 3} {(a,b) for a in A for b in B} 41 Cartesian products with other collections The syntax for Cartesian products can be used with any collection type. In [ ]: first_names = ( Steve, John, Peter ) surnames = ( Smith, Doe ) [(first_name, surname) for first_name in first_names for surname in surnames] 12

13 42 Anonymous Function Literals We can also write anonymous functions. These are function literals, and do not necessarily have a name. They are called lambda expressions (after the λ calculus). In [ ]: list(map(lambda x: x ** 2, [1, 2, 3, 4])) 43 Filtering data We can filter a list by applying a predicate to each element of the list. A predicate is a function which takes a single argument, and returns a boolean value. filter(p, X) is equivalent to {x : p(x) x X} in set-builder notation. In [ ]: list(filter(lambda x: x > 0, [-5, 2, 3, -10, 0, 1])) We can use both filter() and map() on other collections such as strings or sets. In [ ]: list(filter(lambda x: x!=, hello world )) In [ ]: list(map(ord, hello world )) In [ ]: list(filter(lambda x: x > 0, {-5, 2, 3, -10, 0, 1})) 44 Filtering using a list comprehension Again, because this is such a common operation, we can use simpler syntax to say the same thing. We can express a filter using a list-comprehension by using the keyword if: In [ ]: data = [-5, 2, 3, -10, 0, 1] [x for x in data if x > 0] We can also filter and then map in the same expression: In [ ]: from numpy import sqrt [sqrt(x) for x in data if x > 0] 45 The reduce function The reduce() function recursively applies another function to pairs of values over the entire list, resulting in a single return value. In [ ]: from functools import * In [ ]: reduce(lambda x, y: x + y, [0, 1, 2, 3, 4, 5]) 46 Big Data The map() and reduce() functions form the basis of the map-reduce programming model. Map-reduce is the basis of modern highly-distributed large-scale computing frameworks. In [ ]: 13