Python Tutorial

The tutorial below was modified from the Unix/Python Tutorial from Berkeley's CS188 developed by John DeNero and Dan Klein.

Table of Contents

Python Basics

The programming assignments in this course will be written in Python, an interpreted, object-oriented language that shares some features with both Java and Scheme. This tutorial will walk through the primary syntactic constructions in Python, using short examples.

Operators

The Python interpeter can be used to evaluate expressions, for example simple arithmetic expressions. If you enter such expressions at the prompt (>>>) they will be evaluated and the result wil be returned on the next line.

>>> 1 + 1
2
>>> 2 * 3
6

Boolean operators also exist in Python to manipulate the primitive True and False values.

>>> 1==0
False
>>> not (1==0)
True
>>> (2==2) and (2==3)
False
>>> (2==2) or (2==3)
True

Strings

Like Java, Python has a built in string type. The + operator is overloaded to do string concatenation on string values.

>>> 'artificial' + "intelligence"
'artificialintelligence'

There are many built-in methods which allow you to manipulate strings.

>>> 'artificial'.upper()
'ARTIFICIAL'
>>> 'HELP'.lower()
'help'
>>> len('Help')
4

Notice that we can use either single quotes ' ' or double quotes " " to surround string. This allows for easy nesting of strings.

We can also store expressions into variables.

>>> s = 'hello world'
>>> print s
hello world
>>> s.upper()
'HELLO WORLD'
>>> len(s.upper())
11
>>> num = 8.0
>>> num += 2.5
>>> print num
10.5

In Python, you do not have declare variables before you assign to them.

Exercise: Learn about the methods Python provides for strings.

To see what methods Python provides for a datatype, use the dir and help commands:

>>> s = 'abc'

>>> dir(s)
['__add__', '__class__', '__contains__', '__delattr__', '__doc__', '__eq__', '__ge__', '__getattribute__', '__getitem__', '__getnewargs__', '__getslice__', '__gt__', '__hash__', '__init__','__le__', '__len__', '__lt__', '__mod__', '__mul__', '__ne__', '__new__', '__reduce__', '__reduce_ex__','__repr__', '__rmod__', '__rmul__', '__setattr__', '__str__', 'capitalize', 'center', 'count', 'decode', 'encode', 'endswith', 'expandtabs', 'find', 'index', 'isalnum', 'isalpha', 'isdigit', 'islower', 'isspace', 'istitle', 'isupper', 'join', 'ljust', 'lower', 'lstrip', 'replace', 'rfind','rindex', 'rjust', 'rsplit', 'rstrip', 'split', 'splitlines', 'startswith', 'strip', 'swapcase', 'title', 'translate', 'upper', 'zfill']

>>> help(s.find)

Help on built-in function find:



find(...)
    S.find(sub [,start [,end]]) -> int
    
    Return the lowest index in S where substring sub is found,
    such that sub is contained within s[start,end].  Optional
    arguments start and end are interpreted as in slice notation.
    
    Return -1 on failure.
>> s.find('b')
1

 Try out some of the string functions listed in dir (ignore those with underscores '_' around the method name).

Built-in Data Structures

Python comes equipped with some useful built-in data structures, broadly similar to Java's collections package.

Lists

Lists store a sequence of mutable items:

>>> fruits = ['apple','orange','pear','banana']
>>> fruits[0]
'apple'

 We can use the + operator to do list concatenation:

>>> otherFruits = ['kiwi','strawberry']
>>> fruits + otherFruits
>>> ['apple', 'orange', 'pear', 'banana', 'kiwi', 'strawberry']

 Python also allows negative-indexing from the back of the list. For instance, fruits[-1] will access the last element 'banana':

>>> fruits[-2]
'pear'
>>> fruits.pop()
'banana'
>>> fruits
['apple', 'orange', 'pear']
>>> fruits.append('grapefruit')
>>> fruits
['apple', 'orange', 'pear', 'grapefruit']
>>> fruits[-1] = 'pineapple'
>>> fruits
['apple', 'orange', 'pear', 'pineapple']

We can also index multiple adjacent elements using the slice operator. For instance fruits[1:3] which returns a list containing the elements at position 1 and 2. In general fruits[start:stop] will get the elements in start, start+1, ..., stop-1. We can also do fruits[start:] which returns all elements starting from the start index. Also fruits[:end] will return all elements before the element at position end:

>>> fruits[0:2]
['apple', 'orange']
>>> fruits[:3]
['apple', 'orange', 'pear']
>>> fruits[2:]
['pear', 'pineapple']
>>> len(fruits)
4

The items stored in lists can be any Python data type. So for instance we can have lists of lists:

>>> lstOfLsts = [['a','b','c'],[1,2,3],['one','two','three']]
>>> lstOfLsts[1][2]
3
>>> lstOfLsts[0].pop()
'c'
>>> lstOfLsts
[['a', 'b'],[1, 2, 3],['one', 'two', 'three']]
Exercise: Play with some of the list functions. You can find the methods you can call on an object via the dir and get information about them via the help command:

>>> dir(list)
['__add__', '__class__', '__contains__', '__delattr__', '__delitem__',
'__delslice__', '__doc__', '__eq__', '__ge__', '__getattribute__',
'__getitem__', '__getslice__', '__gt__', '__hash__', '__iadd__', '__imul__',
'__init__', '__iter__', '__le__', '__len__', '__lt__', '__mul__', '__ne__',
'__new__', '__reduce__', '__reduce_ex__', '__repr__', '__reversed__',
'__rmul__', '__setattr__', '__setitem__', '__setslice__', '__str__',
'append', 'count', 'extend', 'index', 'insert', 'pop', 'remove', 'reverse',
'sort']
 
>>> help(list.reverse)
Help on built-in function reverse:

reverse(...)
    L.reverse() -- reverse *IN PLACE*
>>> lst = ['a','b','c']
>>> lst.reverse()
>>> ['c','b','a']

 Note: Ignore functions with underscores "_" around the names; these are private helper methods.

Tuples

A data structure similar to the list is the tuple, which is like a list except that it is immutable once it is created (i.e. you cannot change its content once created). Note that tuples are surrounded with parentheses while lists have square brackets.

>>> pair = (3,5)
>>> pair[0]
3
>>> x,y = pair
>>> x
3
>>> y
5
>>> pair[1] = 6
TypeError: object does not support item assignment

 The attempt to modify an immutable structure raised an exception. Exceptions indicate errors: index out of bounds errors, type errors, and so on will all report exceptions in this way.

Sets

A set is another data structure that serves as an unordered list with no duplicate items. Below, we show how to create a set, add things to the set, test if an item is in the set, and perform common set operations (difference, intersection, union):

>>> shapes = ['circle','square','triangle','circle']
>>> setOfShapes = set(shapes)
>>> setOfShapes
set(['circle','square','triangle'])
>>> setOfShapes.add('polygon')
>>> setOfShapes
set(['circle','square','triangle','polygon'])
>>> 'circle' in setOfShapes
True
>>> 'rhombus' in setOfShapes
False
>>> favoriteShapes = ['circle','triangle','hexagon']
>>> setOfFavoriteShapes = set(favoriteShapes)
>>> setOfShapes - setOfFavoriteShapes
set(['square','polyon'])
>>> setOfShapes & setOfFavoriteShapes
set(['circle','triangle'])
>>> setOfShapes | setOfFavoriteShapes
set(['circle','square','triangle','polygon','hexagon'])

Note that the objects in the set are unordered; you cannot assume that their traversal or print order will be the same across machines!

Dictionaries

The last built-in data structure is the dictionary which stores a map from one type of object (the key) to another (the value). The key must be an immutable type (string, number, or tuple). The value can be any Python data type.

Note: In the example below, the printed order of the keys returned by Python could be different than shown below. The reason is that unlike lists which have a fixed ordering, a dictionary is simply a hash table for which there is no fixed ordering of the keys (see the FAQ about dictionary key ordering).

>>> studentIds = {'knuth': 42.0, 'turing': 56.0, 'nash': 92.0 }
>>> studentIds['turing']
56.0
>>> studentIds['nash'] = 'ninety-two'
>>> studentIds
{'knuth': 42.0, 'turing': 56.0, 'nash': 'ninety-two'}
>>> del studentIds['knuth']
>>> studentIds
{'turing': 56.0, 'nash': 'ninety-two'}
>>> studentIds['knuth'] = [42.0,'forty-two']
>>> studentIds
{'knuth': [42.0, 'forty-two'], 'turing': 56.0, 'nash': 'ninety-two'}
>>> studentIds.keys()
['knuth', 'turing', 'nash']
>>> studentIds.values()
[[42.0, 'forty-two'], 56.0, 'ninety-two']
>>> studentIds.items()
[('knuth',[42.0, 'forty-two']), ('turing',56.0), ('nash','ninety-two')]
>>> len(studentIds)
3

As with nested lists, you can also create dictionaries of dictionaries.

Exercise: Use dir and help to learn about the functions you can call on dictionaries.

Writing Scripts

Now that you've got a handle on using Python interactively, let's write a simple Python script that demonstrates Python's for loop. Open the file called foreach.py and update it with the following code: 
# This is what a comment looks like 
fruits = ['apples','oranges','pears','bananas']
for fruit in fruits:
    print fruit + ' for sale'

fruitPrices = {'apples': 2.00, 'oranges': 1.50, 'pears': 1.75}
for fruit, price in fruitPrices.items():
    if price < 2.00:
        print '%s cost %f a pound' % (fruit, price)
    else:
        print fruit + ' are too expensive!'
At the command line, use the following command in the directory containing foreach.py:

[cs188-tf@solar ~/tutorial]$ python foreach.py
apples for sale
oranges for sale
pears for sale
bananas for sale
oranges cost 1.500000 a pound
pears cost 1.750000 a pound
apples are too expensive!
Remember that the print statements listing the costs may be in a different order on your screen than in this tutorial; that's due to the fact that we're looping over dictionary keys, which are unordered. To learn more about control structures (e.g., if and else) in Python, check out the official Python tutorial section on this topic.

If you like functional programming (like Scheme) you might also like map and filter:

>>> map(lambda x: x * x, [1,2,3])
[1, 4, 9]
>>> filter(lambda x: x > 3, [1,2,3,4,5,4,3,2,1])
[4, 5, 4]
You can learn more about lambda if you're interested. The next snippet of code demonstrates python's list comprehension construction: 
nums = [1,2,3,4,5,6]
plusOneNums = [x+1 for x in nums]
oddNums = [x for x in nums if x % 2 == 1]
print oddNums
oddNumsPlusOne = [x+1 for x in nums if x % 2 ==1]
print oddNumsPlusOne
This code is in a file called listcomp.py, which you can run:

[cs188-ta@midway ~]$ python listcomp.py
[1,3,5]
[2,4,6]

 Those of you familiar with Scheme, will recognize that the list comprehension is similar to the map function. In Scheme, the first list comprehension would be written as: 
(define nums '(1,2,3,4,5,6))
(map 
   (lambda (x) (+ x 1))  nums)
Exercise: Write a list comprehension which, from a list, generates a lowercased version of each string that has length greater than five. Solution: listcomp2.py

Beware of Indendation!

Unlike many other languages, Python uses the indentation in the source code for interpretation. So for instance, for the following script: 
if 0 == 1: 
    print 'We are in a world of arithmetic pain' 
print 'Thank you for playing'
will output

Thank you for playing

 But if we had written the script as 
if 0 == 1: 
    print 'We are in a world of arithmetic pain'
    print 'Thank you for playing'
there would be no output. The moral of the story: be careful how you indent! It's best to use four spaces for indentation -- that's what the course code uses.

Writing Functions

As in Scheme or Java, in Python you can define your own functions: 
fruitPrices = {'apples':2.00, 'oranges': 1.50, 'pears': 1.75}

def buyFruit(fruit, numPounds):
    if fruit not in fruitPrices:
        print "Sorry we don't have %s" % (fruit)
    else:
        cost = fruitPrices[fruit] * numPounds
        print "That'll be %f please" % (cost)

# Main Function
if __name__ == '__main__':        
    buyFruit('apples',2.4)
    buyFruit('coconuts',2)
Rather than having a main function as in Java, the __name__ == '__main__' check is used to delimit expressions which are executed when the file is called as a script from the command line. The code after the main check is thus the same sort of code you would put in a main function in Java.

Save this script as fruit.py and run it.

Object Basics

Although this isn't a class in object-oriented programming, you'll have to use some objects in the programming projects, and so it's worth covering the basics of objects in Python. An object encapsulates data and provides functions for interacting with that data.

Defining Classes

Here's an example of defining a class named FruitShop: 
class FruitShop:

    def __init__(self, name, fruitPrices):
        """
            name: Name of the fruit shop
            
            fruitPrices: Dictionary with keys as fruit 
            strings and prices for values e.g. 
            {'apples':2.00, 'oranges': 1.50, 'pears': 1.75} 
        """
        self.fruitPrices = fruitPrices
        self.name = name
        print 'Welcome to the %s fruit shop' % (name)
        
    def getCostPerPound(self, fruit):
        """
            fruit: Fruit string
        Returns cost of 'fruit', assuming 'fruit'
        is in our inventory or None otherwise
        """
        if fruit not in self.fruitPrices:
            print "Sorry we don't have %s" % (fruit)
            return None
        return self.fruitPrices[fruit]
        
    def getPriceOfOrder(self, orderList):
        """
            orderList: List of (fruit, numPounds) tuples
            
        Returns cost of orderList. If any of the fruit are  
        """ 
        totalCost = 0.0             
        for fruit, numPounds in orderList:
            costPerPound = self.getCostPerPound(fruit)
            if costPerPound != None:
                totalCost += numPounds * costPerPound
        return totalCost
    
    def getName(self):
        return self.name

The FruitShop class has some data, the name of the shop and the prices per pound of some fruit, and it provides functions, or methods, on this data. What advantage is there to wrapping this data in a class?

  1. Encapsulating the data prevents it from being altered or used inappropriately,
  2. The abstraction that objects provide make it easier to write general-purpose code.

Using Objects

So how do we make an object and use it? Download the FruitShop implementation in shop.py. We then import the code from this file (making it accessible to other scripts) using import shop, since shop.py is the name of the file. Then, we can create FruitShop objects as follows: 
import shop

shopName = 'the Berkeley Bowl'
fruitPrices = {'apples': 1.00, 'oranges': 1.50, 'pears': 1.75}
berkeleyShop = shop.FruitShop(shopName, fruitPrices)
applePrice = berkeleyShop.getCostPerPound('apples')
print applePrice
print('Apples cost $%.2f at %s.' % (applePrice, shopName))

otherName = 'the Stanford Mall'
otherFruitPrices = {'kiwis':6.00, 'apples': 4.50, 'peaches': 8.75}
otherFruitShop = shop.FruitShop(otherName, otherFruitPrices)
otherPrice = otherFruitShop.getCostPerPound('apples')
print otherPrice
print('Apples cost $%.2f at %s.' % (otherPrice, otherName))
print("My, that's expensive!")
You can download this code in shopTest.py and run it like this: 
[cs188-ta@midway ~]$ python shopTest.py
Welcome to the Berkeley Bowl fruit shop
1.0
Apples cost $1.00 at the Berkeley Bowl.
Welcome to the Stanford Mall fruit shop
4.5
Apples cost $4.50 at the Stanford Mall.
My, that's expensive!
So what just happended? The import shop statement told Python to load all of the functions and classes in shop.py. The line berkeleyShop = shop.FruitShop(shopName, fruitPrices) constructs an instance of the FruitShop class defined in shop.py, by calling the __init__ function in that class. Note that we only passed two arguments in, while __init__ seems to take three arguments: (self, name, fruitPrices). The reason for this is that all methods in a class have self as the first argument. The self variable's value is automatically set to the object itself; when calling a method, you only supply the remaining arguments. The self variable contains all the data (name and fruitPrices) for the current specific instance (similar to this in Java). The print statements use the substitution operator (described in the Python docs if you're curious).

Static vs Instance Variables

The following example with illustrate how to use static and instance variables in python.
Create the person_class.py containing the following code:

class Person:
    population = 0
    def __init__(self, myAge):
        self.age = myAge
        Person.population += 1
    def get_population(self):
        return Person.population
    def get_age(self):
        return self.age
We first compile the script:
[cs188-ta@midway ~]$ python person_class.py
Now use the class as follows:
>>> import person_class
>>> p1 = person_class.Person(12)
>>> p1.get_population()
1
>>> p2 = person_class.Person(63)
>>> p1.get_population()
2
>>> p2.get_population()
2
>>> p1.get_age()
12
>>> p2.get_age()
63
In the code above, age is an instance variable and population is a static variable. population is shared by all instances of the Person class whereas each instance has its own age variable.

More Python Tips and Tricks

This tutorial has briefly touched on some major aspects of Python that will be relevant to the course. Here's some more useful tidbits:

Troubleshooting

These are some problems (and their solutions) that new python learners commonly encounter.

More References!