Brief Overview of Python and NumPy

(Adopted from Notes by Hal Duame, U. of Maryland)

Table of Contents

Python Basics

Invoking the Interpreter

Python can be run in one of two modes. It can either be used interactively, via an interpreter, or it can be called from the command line to execute a script. We will first use the Python interpreter interactively. Typically, you invoke the interpreter by entering python at the command prompt.


The Python interpreter 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 will be returned on the next line.

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

Boolean operators also exist in Python.

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


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

>>> 'machine' + "learning"

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

>>> 'machine'.upper()
>>> 'HELP'.lower()
>>> len('Help')

Notice that we can use either single quotes ' ' or double quotes " " to surround string.

We can also store expressions into variables.

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

In Python, unlike Java or C, you do not have declare variables before you assign to them.

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')

Built-in Data Structures


Lists store a sequence of mutable items:

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

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]
>>> fruits.pop()
>>> 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)

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]
>>> lstOfLsts[0].pop()
>>> lstOfLsts
[['a', 'b'],[1, 2, 3],['one', 'two', 'three']]

>>> lst = ['a','b','c']
>>> lst.reverse()
>>> ['c','b','a']


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]
>>> x,y = pair
>>> x
>>> y
>>> pair[1] = 6
TypeError: object does not support item assignment

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


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

Dictionaries (Dicts)

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.

>>> studentIds = {'knuth': 42.0, 'turing': 56.0, 'nash': 92.0 }
>>> studentIds['turing']
>>> 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)

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

Example Scripts

# 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)
        print fruit + ' are too expensive!'

Example of 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
Put this code into a file called and run the script:

$ python

Beware of Indentation!

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 a single tab for indentation.

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)
        cost = fruitPrices[fruit] * numPounds
        print "That'll be %f please" % (cost)

# Main Function
if __name__ == '__main__':        

Save this script as and run it:

$ python
That'll be 4.800000 please
Sorry we don't have coconuts

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 = 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):

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? There are two reasons: 1) Encapsulating the data prevents it from being altered or used inappropriately and 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 from here and save it to a file called We then import the file using import shop, since is the name of the file, and make instances of the FruitShop by calling shop.FruitShop('MyFruitShop', myDictionary) (i.e., filename.className([args])). We can use the FruitShop as follows:
import shop

name = 'Best Fruits'
fruitPrices = {'apples':2.00, 'oranges': 1.50, 'pears': 1.75}
myFruitShop = shop.FruitShop(name, fruitPrices)
print myFruitShop.getCostPerPound('apples')

otherName = 'Fruits R Us'
otherFruitPrices = {'kiwis':1.00, 'bananas': 1.50, 'peaches': 2.75}
otherFruitShop = shop.FruitShop(otherName, otherFruitPrices)
print otherFruitShop.getCostPerPound('bananas')
Copy the code above into a file called (in the same directory as and run it:

$ python
Welcome to the Best Fruits fruit shop
Welcome to the Fruits R Us fruit shop

Static vs Instance Variables

The following example with illustrate how to use static and instance variables in python.
Create the 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:
$ python

Now use the class as follows:
>>> import person_class
>>> p1 = person_class.Person(12)
>>> p1.get_population()
>>> p2 = person_class.Person(63)
>>> p1.get_population()
>>> p2.get_population()
>>> p1.get_age()
>>> p2.get_age()
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.

Exercise: Write a function, shopSmart(orders,shops) which takes an orderList (like the kind passed in to FruitShop.getPriceOfOrder) and a list of FruitShop and returns the FruitShop where your order costs the least amount in total. This function should be defined in a file called A stub implementation is provided here. Use the implementation as a "support" file.

Test Case:

orders1 = [('apples',1.0), ('oranges',3.0)]
orders2 = [('apples',3.0)]			 
dir1 = {'apples': 2.0, 'oranges':1.0}
shop1 =  shop.FruitShop('shop1',dir1)
dir2 = {'apples': 1.0, 'oranges': 5.0}
shop2 = shop.FruitShop('shop2',dir2)
shops = [shop1, shop2]

The following are true:

shopSmart.shopSmart(orders1, shops).getName() == 'shop1'


shopSmart.shopSmart(orders2, shops).getName() == 'shop2'


NumPy Basics

Let's first test NumPy by doing some simple vector operations:

>>> from numpy import *
>>> array([1,2,3,4,5])
array([1, 2, 3, 4, 5])
>>> array([1,2,3,4,5]) / 5
array([0, 0, 0, 0, 1])
>>> array([1.0,2,3,4,5])
array([ 1., 2., 3., 4., 5.])
>>> array([1.0,2,3,4,5]) / 5.0
array([ 0.2, 0.4, 0.6, 0.8, 1. ])

NumPy differentiates between integer vectors (called arrays) and real vectors. You can also specify the type directly:

>>> array([1,2,3,4,5], dtype='f') / 5
array([0.2, 0.4, 0.6, 0.8, 1.], dtype=float32)

We can do dot products:

>>> dot(array([1,2,3,4,5]), array([2,3,4,5,6]))

And matrix operations:

>>> array([[1,2,3],[4,5,6],[7,8,9]])
array([[1, 2, 3],
[4, 5, 6],
[7, 8, 9]])
>>> array([[1,2,3],[4,5,6],[7,8,9]]).T
array([[1, 4, 7],
[2, 5, 8],
[3, 6, 9]])
>>> array([[1,2,3],[4,5,6],[7,8,9]]) * array([1,10,20])
array([[ 1, 20, 60],
[ 4, 50, 120],
[ 7, 80, 180]])
>>> dot(array([[1,2,3],[4,5,6],[7,8,9]]), array([1,10,20]))
array([ 81, 174, 267])

Here, .T means "transpose." Note that * is interpreted as point-wise multiplication and that dot is required to get a matrix/vector product.

Indexing is straightforward:

>>> x = array([[1,2,3,4],[5,6,7,8]])
>>> x
array([[1, 2, 3, 4],
[5, 6, 7, 8]])
>>> x[1,1]
>>> x[0,3]

NumPy supports slicing operations that are incredibly useful for ML applications. We can extract rows and columns in their entirety:

>>> x[0,:]
array([1, 2, 3, 4])
>>> x[:,0]
array([1, 5])
>>> x[:,0:2]
array([[1, 2],
[5, 6]])

You can use arrays to index into other arrays. For instance, perhaps we want to extract all values of x that are greater than 5 and maybe sum them up:

>>> x>5
array([[False, False, False, False],
[False, True, True, True]], dtype=bool)
>>> x[x>5]
array([6, 7, 8])
>>> sum(x[x>5])
>>> (x>2) & (x<7)
array([[False, False, True, True],
[ True, True, False, False]], dtype=bool)
>>> x[(x>2) & (x<7)]
array([3, 4, 5, 6])

You can even do assignment within slices:

>>> x
array([[1, 2, 3, 4],
[5, 6, 7, 8]])
>>> x[x>5]
array([6, 7, 8])
>>> x[x>5] = 5
>>> x
array([[1, 2, 3, 4],
[5, 5, 5, 5]])


matplotlib Basics

In order to test matplotlib, let's try their default example:

>>> from pylab import randn, hist
>>> x = randn(10000)
>>> hist(x, 100)
>>> show()

This should pop up a histogram showing something that looks approximately Gaussian. The randn function is generating 10k random values from a standard normal and hist is generating the histogram.

>>> plot(x,sin(x/50*math.pi),'b-', x,cos(x/50*math.pi),'r--');
>>> legend( ('sin','cos') )
>>> show()


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