intro-python


Introduction to Python

Python Program Files

  • Python code is usually stored in text files with the file ending in ".py":
    myprogram.py
  • Every line in a Python program file is assumed to be a Python statement, or part thereof.
    • The only exception is comment lines, which start with the character # (optionally preceded by an arbitrary number of white-space characters, i.e., tabs or spaces). Comment lines are usually ignored by the Python interpreter.
      # this is a comment
  • To run our Python program from the command line we use:
    $ python myprogram.py
  • On UNIX systems it is common to define the path to the interpreter on the first line of the program (note that this is a comment line as far as the Python interpreter is concerned):

    #!/usr/bin/env python

    If we do, and if we additionally set the file script to be executable, we can run the program like this:

    $ myprogram.py

Example:


In [1]:
!ls ../scripts/hello-world*.py


../scripts/hello-world.py

In [2]:
!cat ../scripts/hello-world.py


#!/usr/bin/env python

print("Hello world!")

In [3]:
!python scripts/hello-world.py


python: can't open file 'scripts/hello-world.py': [Errno 2] No such file or directory

Jupyter Notebooks

This file - a Jupyter (IPython) notebook - does not follow the standard pattern with Python code in a text file. Instead, an IPython notebook is stored as a file in the JSON format. The advantage is that we can mix formatted text, Python code and code output. It requires the IPython notebook server to run it though, and therefore isn't a stand-alone Python program as described above. Other than that, there is no difference between the Python code that goes into a program file or an IPython notebook.

Modules

Most of the functionality in Python is provided by modules. The Python Standard Library is a large collection of modules that provides cross-platform implementations of common facilities such as access to the operating system, file I/O, string management, network communication, and much more.

References

To use a module in a Python program it first has to be imported. A module can be imported using the import statement. For example, to import the module math, which contains many standard mathematical functions, we can do:


In [4]:
import math

This includes the whole module and makes it available for use later in the program. For example, we can do:


In [5]:
import math
x = math.cos(2 * math.pi)
print(x)


1.0

Alternatively, we can chose to import all symbols (functions and variables) in a module to the current namespace (so that we don't need to use the prefix "math." every time we use something from the math module:


In [6]:
from math import *
x = cos(2 * pi)
print(x)


1.0

This pattern can be very convenient, but in large programs that include many modules it is often a good idea to keep the symbols from each module in their own namespaces, by using the import math pattern. This would elminate potentially confusing problems with name space collisions.

As a third alternative, we can chose to import only a few selected symbols from a module by explicitly listing which ones we want to import instead of using the wildcard character *:


In [7]:
from math import cos, pi
x = cos(2 * pi)
print(x)


1.0

Looking at what a module contains, and its documentation

Once a module is imported, we can list the symbols it provides using the dir function:


In [8]:
import math
print(dir(math))


['__doc__', '__file__', '__loader__', '__name__', '__package__', '__spec__', 'acos', 'acosh', 'asin', 'asinh', 'atan', 'atan2', 'atanh', 'ceil', 'copysign', 'cos', 'cosh', 'degrees', 'e', 'erf', 'erfc', 'exp', 'expm1', 'fabs', 'factorial', 'floor', 'fmod', 'frexp', 'fsum', 'gamma', 'gcd', 'hypot', 'inf', 'isclose', 'isfinite', 'isinf', 'isnan', 'ldexp', 'lgamma', 'log', 'log10', 'log1p', 'log2', 'modf', 'nan', 'pi', 'pow', 'radians', 'sin', 'sinh', 'sqrt', 'tan', 'tanh', 'trunc']

And using the function help we can get a description of each function (almost .. not all functions have docstrings, as they are technically called, but the vast majority of functions are documented this way).


In [9]:
help(math.log)


Help on built-in function log in module math:

log(...)
    log(x[, base])
    
    Return the logarithm of x to the given base.
    If the base not specified, returns the natural logarithm (base e) of x.


In [10]:
math.log(10)


Out[10]:
2.302585092994046

In [11]:
math.log(10, 2)


Out[11]:
3.3219280948873626

We can also use the help function directly on modules: Try

help(math) 

Some very useful modules form the Python standard library are os, sys, math, shutil, re, subprocess, multiprocessing, threading.

A complete lists of standard modules for Python 3 are available at http://docs.python.org/3/library/.

For example, this is the os module in the standard library.


In [12]:
import os
os.listdir()


Out[12]:
['.ipynb_checkpoints',
 '__pycache__',
 'course-overview.ipynb',
 'install-party.ipynb',
 'intro-data-science.ipynb',
 'intro-numpy.ipynb',
 'intro-python.ipynb',
 'mymodule.py',
 'Untitled.ipynb']

Variables and types

Symbol names

Variable names in Python can contain alphanumerical characters a-z, A-Z, 0-9 and some special characters such as _. Normal variable names must start with a letter.

By convention, variable names start with a lower-case letter, and Class names start with a capital letter.

In addition, there are a number of Python keywords that cannot be used as variable names. These keywords are:

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

Assignment

The assignment operator in Python is =. Python is a dynamically typed language, so we do not need to specify the type of a variable when we create one.

Assigning a value to a new variable creates the variable:


In [13]:
# variable assignments
x = 1.0
my_variable = 12.2

Although not explicitly specified, a variable does have a type associated with it. The type is derived from the value that was assigned to it.


In [14]:
type(x)


Out[14]:
float

If we assign a new value to a variable, its type can change.


In [15]:
x = 1

In [16]:
type(x)


Out[16]:
int

If we try to use a variable that has not yet been defined we get an NameError:


In [17]:
import traceback 

try:
    print(y)
except NameError as e:
    print(traceback.format_exc())


Traceback (most recent call last):
  File "<ipython-input-17-0488135cf974>", line 4, in <module>
    print(y)
NameError: name 'y' is not defined

Fundamental types


In [18]:
# integers
x = 1
type(x)


Out[18]:
int

In [19]:
# float
x = 1.0
type(x)


Out[19]:
float

In [20]:
# boolean
b1 = True
b2 = False

type(b1)


Out[20]:
bool

In [21]:
# complex numbers: note the use of `j` to specify the imaginary part
x = 1.0 - 1.0j
type(x)


Out[21]:
complex

In [22]:
print(x)


(1-1j)

In [23]:
print(x.real, x.imag)


1.0 -1.0

Type utility functions


In [24]:
x = 1.0

# check if the variable x is a float
type(x) is float


Out[24]:
True

In [25]:
# check if the variable x is an int
type(x) is int


Out[25]:
False

We can also use the isinstance method for testing types of variables:


In [26]:
isinstance(x, float)


Out[26]:
True

Type casting


In [27]:
x = 1.5

print(x, type(x))


1.5 <class 'float'>

In [28]:
x = int(x)

print(x, type(x))


1 <class 'int'>

In [29]:
z = complex(x)

print(z, type(z))


(1+0j) <class 'complex'>

In [30]:
import traceback 

try:
    x = float(z)
except TypeError as e:
    print(traceback.format_exc())


Traceback (most recent call last):
  File "<ipython-input-30-68b971d0047b>", line 4, in <module>
    x = float(z)
TypeError: can't convert complex to float

Operators and comparisons

Most operators and comparisons in Python work as one would expect:

  • Arithmetic operators +, -, *, /, // (integer division), '**' power

In [31]:
1 + 2, 1 - 2, 1 * 2, 1 / 2


Out[31]:
(3, -1, 2, 0.5)

In [32]:
1.0 + 2.0, 1.0 - 2.0, 1.0 * 2.0, 1.0 / 2.0


Out[32]:
(3.0, -1.0, 2.0, 0.5)

In [33]:
# Integer division of float numbers
3.0 // 2.0


Out[33]:
1.0

In [34]:
# Note! The power operators in python isn't ^, but **
2 ** 2


Out[34]:
4

Note: The / operator always performs a floating point division in Python 3.x. This is not true in Python 2.x, where the result of / is always an integer if the operands are integers. to be more specific, 1/2 = 0.5 (float) in Python 3.x, and 1/2 = 0 (int) in Python 2.x (but 1.0/2 = 0.5 in Python 2.x).

The boolean operators are spelled out as the words and, not, or.


In [35]:
True and False


Out[35]:
False

In [36]:
not False


Out[36]:
True

In [37]:
True or False


Out[37]:
True

Comparison operators >, <, >= (greater or equal), <= (less or equal), == equality, is identical.


In [38]:
2 > 1, 2 < 1


Out[38]:
(True, False)

In [39]:
2 > 2, 2 < 2


Out[39]:
(False, False)

In [40]:
2 >= 2, 2 <= 2


Out[40]:
(True, True)

In [41]:
# equality
[1,2] == [1,2]


Out[41]:
True

In [42]:
# objects identical?
list1 = list2 = [1,2]

list1 is list2


Out[42]:
True

Exercise:

Mindy has $5.25 in her pocket. Apples cost 29 cents each. Calculate how many apples mindy can buy and how much change she will have left. Money should be represented in variables of type float and apples should be represented in variables of type integer.

Answer:

  • mindy_money = 5.25
  • apple_cost = .29
  • num_apples = ?
  • change = ?

Compound types: Strings, List and dictionaries

Strings

Strings are the variable type that is used for storing text messages.


In [43]:
s = "Hello world"
type(s)


Out[43]:
str

In [44]:
# length of the string: the number of characters
len(s)


Out[44]:
11

In [45]:
# replace a substring in a string with somethign else
s2 = s.replace("world", "test")
print(s2)


Hello test

We can index a character in a string using []:


In [46]:
s[0]


Out[46]:
'H'

Heads up MATLAB and R users: Indexing start at 0!

We can extract a part of a string using the syntax [start:stop], which extracts characters between index start and stop -1 (the character at index stop is not included):


In [47]:
s[0:5]


Out[47]:
'Hello'

In [48]:
s[4:5]


Out[48]:
'o'

If we omit either (or both) of start or stop from [start:stop], the default is the beginning and the end of the string, respectively:


In [49]:
s[:5]


Out[49]:
'Hello'

In [50]:
s[6:]


Out[50]:
'world'

In [51]:
s[:]


Out[51]:
'Hello world'

We can also define the step size using the syntax [start:end:step] (the default value for step is 1, as we saw above):


In [52]:
s[::1]


Out[52]:
'Hello world'

In [53]:
s[::2]


Out[53]:
'Hlowrd'

This technique is called slicing. Read more about the syntax here: https://docs.python.org/3.5/library/functions.html#slice

Python has a very rich set of functions for text processing. See for example https://docs.python.org/3.5/library/string.html for more information.

String formatting examples


In [54]:
print("str1", "str2", "str3")  # The print statement concatenates strings with a space


str1 str2 str3

In [55]:
print("str1", 1.0, False, -1j)  # The print statements converts all arguments to strings


str1 1.0 False (-0-1j)

In [56]:
print("str1" + "str2" + "str3") # strings added with + are concatenated without space


str1str2str3

In [57]:
print("value = %f" % 1.0)       # we can use C-style string formatting


value = 1.000000

In [58]:
# this formatting creates a string
s2 = "value1 = %.2f. value2 = %d" % (3.1415, 1.5)

print(s2)


value1 = 3.14. value2 = 1

In [59]:
# alternative, more intuitive way of formatting a string 
s3 = 'value1 = {0}, value2 = {1}'.format(3.1415, 1.5)

print(s3)


value1 = 3.1415, value2 = 1.5

Exercise:

Paste in the code from your previous exercise and output the result as a story (round monetary values to 2 decimal places). The ouptut should look like this:

"Mindy had \$5.25 in her pocket. Apples at her nearby store cost 29 cents. With her \$5.25, mindy can buy 18 apples and will have 10 cents left over."

List

Lists are very similar to strings, except that each element can be of any type.

The syntax for creating lists in Python is [...]:


In [60]:
l = [1,2,3,4]

print(type(l))
print(l)


<class 'list'>
[1, 2, 3, 4]

We can use the same slicing techniques to manipulate lists as we could use on strings:


In [61]:
print(l)

print(l[1:3])

print(l[::2])


[1, 2, 3, 4]
[2, 3]
[1, 3]

Heads up MATLAB and R users: Indexing starts at 0!


In [62]:
l[0]


Out[62]:
1

Elements in a list do not all have to be of the same type:


In [63]:
l = [1, 'a', 1.0, 1-1j]

print(l)


[1, 'a', 1.0, (1-1j)]

Python lists can be heterogeneous and arbitrarily nested:


In [64]:
nested_list = [1, [2, [3, [4, [5]]]]]

nested_list


Out[64]:
[1, [2, [3, [4, [5]]]]]

Lists play a very important role in Python. For example they are used in loops and other flow control structures (discussed below). There are a number of convenient functions for generating lists of various types, for example the range function:


In [65]:
start = 10
stop = 30
step = 2

range(start, stop, step)


Out[65]:
range(10, 30, 2)

In [66]:
# in python 3 range generates an iterator, which can be converted to a list using 'list(...)'.
# It has no effect in python 2
list(range(start, stop, step))


Out[66]:
[10, 12, 14, 16, 18, 20, 22, 24, 26, 28]

In [67]:
list(range(-10, 10))


Out[67]:
[-10, -9, -8, -7, -6, -5, -4, -3, -2, -1, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9]

In [68]:
s


Out[68]:
'Hello world'

In [69]:
# convert a string to a list by type casting:
s2 = list(s)

s2


Out[69]:
['H', 'e', 'l', 'l', 'o', ' ', 'w', 'o', 'r', 'l', 'd']

In [70]:
# sorting lists (by creating a new variable)

s3 = sorted(s2)

print(s2)
print(s3)


['H', 'e', 'l', 'l', 'o', ' ', 'w', 'o', 'r', 'l', 'd']
[' ', 'H', 'd', 'e', 'l', 'l', 'l', 'o', 'o', 'r', 'w']

In [71]:
# sorting lists in place
s2.sort()

print(s2)


[' ', 'H', 'd', 'e', 'l', 'l', 'l', 'o', 'o', 'r', 'w']

Adding, inserting, modifying, and removing elements from lists


In [72]:
# create a new empty list
l = []

# add an elements using `append`
l.append("A")
l.append("d")
l.append("d")

print(l)


['A', 'd', 'd']

We can modify lists by assigning new values to elements in the list. In technical jargon, lists are mutable.


In [73]:
l[1] = "p"
l[2] = "p"

print(l)


['A', 'p', 'p']

In [74]:
l[1:3] = ["d", "d"]

print(l)


['A', 'd', 'd']

Insert an element at an specific index using insert


In [75]:
l.insert(0, "i")
l.insert(1, "n")
l.insert(2, "s")
l.insert(3, "e")
l.insert(4, "r")
l.insert(5, "t")

print(l)


['i', 'n', 's', 'e', 'r', 't', 'A', 'd', 'd']

Remove first element with specific value using 'remove'


In [76]:
l.remove("A")

print(l)


['i', 'n', 's', 'e', 'r', 't', 'd', 'd']

Remove an element at a specific location using del:


In [77]:
del l[7]
del l[6]

print(l)


['i', 'n', 's', 'e', 'r', 't']

See help(list) for more details, or read the online documentation

Tuples

Tuples are like lists, except that they cannot be modified once created, that is they are immutable.

In Python, tuples are created using the syntax (..., ..., ...), or even ..., ...:


In [78]:
point = (10, 20)

print(point, type(point))


(10, 20) <class 'tuple'>

In [79]:
point = 10, 20

print(point, type(point))


(10, 20) <class 'tuple'>

We can unpack a tuple by assigning it to a comma-separated list of variables:


In [80]:
x, y = point

print("x =", x)
print("y =", y)


x = 10
y = 20

If we try to assign a new value to an element in a tuple we get an error:


In [81]:
try:
    point[0] = 20
except TypeError as e:
    print(traceback.format_exc())


Traceback (most recent call last):
  File "<ipython-input-81-c017eac57cd7>", line 2, in <module>
    point[0] = 20
TypeError: 'tuple' object does not support item assignment

Dictionaries

Dictionaries are also like lists, except that each element is a key-value pair. The syntax for dictionaries is {key1 : value1, ...}:


In [82]:
params = {"parameter1" : 1.0,
          "parameter2" : 2.0,
          "parameter3" : 3.0,}

print(type(params))
print(params)


<class 'dict'>
{'parameter1': 1.0, 'parameter3': 3.0, 'parameter2': 2.0}

In [83]:
print("parameter1 = " + str(params["parameter1"]))
print("parameter2 = " + str(params["parameter2"]))
print("parameter3 = " + str(params["parameter3"]))


parameter1 = 1.0
parameter2 = 2.0
parameter3 = 3.0

In [84]:
params["parameter1"] = "A"
params["parameter2"] = "B"

# add a new entry
params["parameter4"] = "D"

print("parameter1 = " + str(params["parameter1"]))
print("parameter2 = " + str(params["parameter2"]))
print("parameter3 = " + str(params["parameter3"]))
print("parameter4 = " + str(params["parameter4"]))


parameter1 = A
parameter2 = B
parameter3 = 3.0
parameter4 = D

Exercise:

Mindy doesn't want 18 apples, that's too many for someone who lives by themselves. We're now going to represent mindy's world using our new data types.

Make a list containing the fruits that mindy desires. She likes apples, strawberries, pinapples, and papayas.

Make a tuple containing the fruits that the store has. This is immutable because the store doesn't change their inventory. The local store has apples, strawberries, pinapples, pears, bananas, and oranges.

Make a dictonary showing the price of each fruit at the store. Apples are 29 cents, bananas are 5 cents, oranges are 20 cents, strawberries are 30 cents and pinapples are $1.50.

Control Flow

Conditional statements: if, elif, else

The Python syntax for conditional execution of code uses the keywords if, elif (else if), else:


In [85]:
statement1 = False
statement2 = False

if statement1:
    print("statement1 is True")
    
elif statement2:
    print("statement2 is True")
    
else:
    print("statement1 and statement2 are False")


statement1 and statement2 are False

For the first time, here we encounted a peculiar and unusual aspect of the Python programming language: Program blocks are defined by their indentation level.

Compare to the equivalent C code:

if (statement1)
{
    printf("statement1 is True\n");
}
else if (statement2)
{
    printf("statement2 is True\n");
}
else
{
    printf("statement1 and statement2 are False\n");
}

In C blocks are defined by the enclosing curly brakets { and }. And the level of indentation (white space before the code statements) does not matter (completely optional).

But in Python, the extent of a code block is defined by the indentation level (usually a tab or say four white spaces). This means that we have to be careful to indent our code correctly, or else we will get syntax errors.

Examples:


In [86]:
statement1 = statement2 = True

if statement1:
    if statement2:
        print("both statement1 and statement2 are True")


both statement1 and statement2 are True

In [87]:
# # Bad indentation!
# if statement1:
#     if statement2:
#     print("both statement1 and statement2 are True")  # this line is not properly indented

In [88]:
statement1 = False 

if statement1:
    print("printed if statement1 is True")
    
    print("still inside the if block")

In [89]:
if statement1:
    print("printed if statement1 is True")
    
print("now outside the if block")


now outside the if block

Loops

In Python, loops can be programmed in a number of different ways. The most common is the for loop, which is used together with iterable objects, such as lists. The basic syntax is:

for loops:


In [90]:
for x in [1,2,3]:
    print(x)


1
2
3

The for loop iterates over the elements of the supplied list, and executes the containing block once for each element. Any kind of list can be used in the for loop. For example:


In [91]:
for x in range(4): # by default range start at 0
    print(x)


0
1
2
3

Note: range(4) does not include 4 !


In [92]:
for x in range(-3,3):
    print(x)


-3
-2
-1
0
1
2

In [93]:
for word in ["scientific", "computing", "with", "python"]:
    print(word)


scientific
computing
with
python

To iterate over key-value pairs of a dictionary:


In [94]:
for key, value in params.items():
    print(key + " = " + str(value))


parameter1 = A
parameter4 = D
parameter3 = 3.0
parameter2 = B

Sometimes it is useful to have access to the indices of the values when iterating over a list. We can use the enumerate function for this:


In [95]:
for idx, x in enumerate(range(-3,3)):
    print(idx, x)


0 -3
1 -2
2 -1
3 0
4 1
5 2

List comprehensions: Creating lists using for loops:

A convenient and compact way to initialize lists:


In [96]:
l1 = [x**2 for x in range(0,5)]

print(l1)


[0, 1, 4, 9, 16]

while loops:


In [97]:
i = 0

while i < 5:
    print(i)
    
    i = i + 1
    
print("done")


0
1
2
3
4
done

Note that the print("done") statement is not part of the while loop body because of the difference in indentation.

Exercise:

Loop through all of the fruits that mindy wants and check if the store has them. For each fruit that she wants print

"Mindy, the store has apples and they cost $.29"

or

"Mindy, the store does not have papayas"

Functions

A function in Python is defined using the keyword def, followed by a function name, a signature within parentheses (), and a colon :. The following code, with one additional level of indentation, is the function body.


In [98]:
def func0():   
    print("test")

In [99]:
func0()


test

Optionally, but highly recommended, we can define a so called "docstring", which is a description of the functions purpose and behaivor. The docstring should follow directly after the function definition, before the code in the function body.


In [100]:
def func1(s):
    """
    Print a string 's' and tell how many characters it has    
    """
    
    print(s + " has " + str(len(s)) + " characters")

In [101]:
help(func1)


Help on function func1 in module __main__:

func1(s)
    Print a string 's' and tell how many characters it has


In [102]:
func1("test")


test has 4 characters

Functions that returns a value use the return keyword:


In [103]:
def square(x):
    """
    Return the square of x.
    """
    return x ** 2

In [104]:
square(4)


Out[104]:
16

We can return multiple values from a function using tuples (see above):


In [105]:
def powers(x):
    """
    Return a few powers of x.
    """
    return x ** 2, x ** 3, x ** 4

In [106]:
powers(3)


Out[106]:
(9, 27, 81)

In [107]:
x2, x3, x4 = powers(3)

print(x3)


27

Default argument and keyword arguments

In a definition of a function, we can give default values to the arguments the function takes:


In [108]:
def myfunc(x, p=2, debug=False):
    if debug:
        print("evaluating myfunc for x = " + str(x) + " using exponent p = " + str(p))
    return x**p

If we don't provide a value of the debug argument when calling the the function myfunc it defaults to the value provided in the function definition:


In [109]:
myfunc(5)


Out[109]:
25

In [110]:
myfunc(5, debug=True)


evaluating myfunc for x = 5 using exponent p = 2
Out[110]:
25

If we explicitly list the name of the arguments in the function calls, they do not need to come in the same order as in the function definition. This is called keyword arguments, and is often very useful in functions that takes a lot of optional arguments.


In [111]:
myfunc(p=3, debug=True, x=7)


evaluating myfunc for x = 7 using exponent p = 3
Out[111]:
343

Unnamed functions (lambda function)

In Python we can also create unnamed functions, using the lambda keyword:


In [112]:
f1 = lambda x: x**2
    
# is equivalent to 

def f2(x):
    return x**2

In [113]:
f1(2), f2(2)


Out[113]:
(4, 4)

This technique is useful for example when we want to pass a simple function as an argument to another function, like this:


In [114]:
# map is a built-in python function
map(lambda x: x**2, range(-3,4))


Out[114]:
<map at 0x10484e978>

In [115]:
# in python 3 we can use `list(...)` to convert the iterator to an explicit list
list(map(lambda x: x**2, range(-3,4)))


Out[115]:
[9, 4, 1, 0, 1, 4, 9]

Exercise:

Mindy is great, but we want code that can tell anyone what fruits the store has. To do this we will generalize our code for Mindy using a function.

Write a function that takes the following parameters

  • full_name (string)
  • fruits_you_want (list)
  • fruits_the_store_has (tuple)
  • prices (dict)

and prints to the terminal a sentence per fruit that you want just like the last exercise. For example, if

name = 'Al'
list_of_fruits_you_want = ['apple', 'banana']
tuple_of_fruits_the_store_has = ('apple', 'banana', 'orange', 'strawberries', 'pineapple')
prices = {
    'apple' : .29
    'banana':  .05
    'orange': .20
    'strawberries': .30
    'pinapple': 1.50
}

The function should print.

"Al, the store has apples and they cost \$.29" "Al, the store has bananas and they cost \$.05"

Classes

Classes are the key features of object-oriented programming. A class is a structure for representing an object and the operations that can be performed on the object.

In Python a class can contain attributes (variables) and methods (functions).

A class is defined almost like a function, but using the class keyword, and the class definition usually contains a number of class method definitions (a function in a class).

  • Each class method should have an argument self as its first argument. This object is a self-reference.

  • Some class method names have special meaning, for example:


In [116]:
class Point:
    """
    Simple class for representing a point in a Cartesian coordinate system.
    """
    
    def __init__(self, x, y):
        """
        Create a new Point at x, y.
        """
        self.x = x
        self.y = y
        
    def translate(self, dx, dy):
        """
        Translate the point by dx and dy in the x and y direction.
        """
        self.x += dx
        self.y += dy
        
    def __str__(self):
        return("Point at [%f, %f]" % (self.x, self.y))

To create a new instance of a class:


In [117]:
p1 = Point(0, 0) # this will invoke the __init__ method in the Point class

print(p1)         # this will invoke the __str__ method


Point at [0.000000, 0.000000]

To invoke a class method in the class instance p:


In [118]:
p2 = Point(1, 1)

p1.translate(0.25, 1.5)

print(p1)
print(p2)


Point at [0.250000, 1.500000]
Point at [1.000000, 1.000000]

Note that calling class methods can modifiy the state of that particular class instance, but does not effect other class instances or any global variables.

That is one of the nice things about object-oriented design: code such as functions and related variables are grouped in separate and independent entities.

Modules

One of the most important concepts in good programming is to reuse code and avoid repetitions.

The idea is to write functions and classes with a well-defined purpose and scope, and reuse these instead of repeating similar code in different part of a program (modular programming). The result is usually that readability and maintainability of a program is greatly improved. What this means in practice is that our programs have fewer bugs, are easier to extend and debug/troubleshoot.

Python supports modular programming at different levels. Functions and classes are examples of tools for low-level modular programming. Python modules are a higher-level modular programming construct, where we can collect related variables, functions and classes in a module. A python module is defined in a python file (with file-ending .py), and it can be made accessible to other Python modules and programs using the import statement.

Consider the following example: the file mymodule.py contains simple example implementations of a variable, function and a class:


In [119]:
%%file mymodule.py
"""
Example of a python module. Contains a variable called my_variable,
a function called my_function, and a class called MyClass.
"""

my_variable = 0

def my_function():
    """
    Example function
    """
    return my_variable
    
class MyClass:
    """
    Example class.
    """

    def __init__(self):
        self.variable = my_variable
        
    def set_variable(self, new_value):
        """
        Set self.variable to a new value
        """
        self.variable = new_value
        
    def get_variable(self):
        return self.variable


Overwriting mymodule.py

We can import the module mymodule into our Python program using import:


In [120]:
import mymodule

Use help(module) to get a summary of what the module provides:


In [121]:
help(mymodule)


Help on module mymodule:

NAME
    mymodule

DESCRIPTION
    Example of a python module. Contains a variable called my_variable,
    a function called my_function, and a class called MyClass.

CLASSES
    builtins.object
        MyClass
    
    class MyClass(builtins.object)
     |  Example class.
     |  
     |  Methods defined here:
     |  
     |  __init__(self)
     |      Initialize self.  See help(type(self)) for accurate signature.
     |  
     |  get_variable(self)
     |  
     |  set_variable(self, new_value)
     |      Set self.variable to a new value
     |  
     |  ----------------------------------------------------------------------
     |  Data descriptors defined here:
     |  
     |  __dict__
     |      dictionary for instance variables (if defined)
     |  
     |  __weakref__
     |      list of weak references to the object (if defined)

FUNCTIONS
    my_function()
        Example function

DATA
    my_variable = 0

FILE
    /Users/johria/Development/DAT-DC-12/notebooks/mymodule.py



In [122]:
mymodule.my_variable


Out[122]:
0

In [123]:
mymodule.my_function()


Out[123]:
0

In [124]:
my_class = mymodule.MyClass() 
my_class.set_variable(10)
my_class.get_variable()


Out[124]:
10

If we make changes to the code in mymodule.py, we need to reload it using reload:


In [125]:
import importlib
importlib.reload(mymodule) # Python 3 only
# For Python 2 use reload(mymodule)


Out[125]:
<module 'mymodule' from '/Users/johria/Development/DAT-DC-12/notebooks/mymodule.py'>

Exceptions

In Python errors are managed with a special language construct called "Exceptions". When errors occur exceptions can be raised, which interrupts the normal program flow and fallback to somewhere else in the code where the closest try-except statement is defined.

To generate an exception we can use the raise statement, which takes an argument that must be an instance of the class BaseException or a class derived from it.


In [126]:
try:
    raise Exception("description of the error")
except Exception as e:
    print(traceback.format_exc())


Traceback (most recent call last):
  File "<ipython-input-126-72b44174a06f>", line 2, in <module>
    raise Exception("description of the error")
Exception: description of the error

A typical use of exceptions is to abort functions when some error condition occurs, for example:

def my_function(arguments):

    if not verify(arguments):
        raise Exception("Invalid arguments")

    # rest of the code goes here

To gracefully catch errors that are generated by functions and class methods, or by the Python interpreter itself, use the try and except statements:

try:
    # normal code goes here
except:
    # code for error handling goes here
    # this code is not executed unless the code
    # above generated an error

For example:


In [127]:
try:
    print("test")
    # generate an error: the variable test is not defined
    print(test)
except Exception:
    print("Caught an exception")


test
Caught an exception

To get information about the error, we can access the Exception class instance that describes the exception by using for example:

except Exception as e:

In [128]:
try:
    print("test")
    # generate an error: the variable test is not defined
    print(test)
except Exception as e:
    print("Caught an exception:", e)


test
Caught an exception: name 'test' is not defined

Excercise:

Make two classes with the following variables and methods

Store

variables

  • inventory (dict)

methods

  • show_inventory()
    • nicely displays the store's inventory
  • message_customer(customer)
    • shows the customer if the store has the fruits they want and how much each fruit costs (this is code from previous exercises, except it will use the customer's "Formal Greeting" instead of their first name.)

Customer

variables

  • first_name (string)
  • last_name (string)
  • is_male (boolean)
  • money (float)
  • fruit (dict)
  • preferred_fruit (list)

methods

  • formal_greeting()
    • (Mr. Al Johri, Ms. Mindy Smith)
  • buy_fruit(store, fruit_name, fruit_amt)
    • inputs are a store, the name of a fruit, and the amount of that fruit
    • checks to see if the store has the fruit - returns an error if it does not
    • checks to see if the customer can afford the amount of fruit they intend to buy - returns an error if not
    • "purchases" the fruit by adding it to the customers fruit dict and removes the correct amount of money from their money variable

Exercise:

Instantiate a list of the following customers.

  • Mindy Smith - \$5.25, likes apples and oranges
  • Al Johri - \$20.19, likes papaya, strawberries, pinapple, and apples
  • Hillary Clinton - \$15, likes strawberries and oranges
  • Oliver Twist - \$.05, likes apples
  • Donald Trump - \$4000, only likes durian

Create a store called Whole Foods with the following inventory

  • 'apple' : \$.29
  • 'banana': \$.05
  • 'orange': \$.20
  • 'strawberries': \$.30
  • 'pinapple': \$1.50,
  • 'grapes': \$.22,
  • 'durian': \$5000

Write code to do the following

  1. Print the store's inventory
  2. For each customer, print the store's message to them
  3. Have each customer purchase 1 of each fruit in their list of preferred fruits.

(Make sure you have error handling so the program doesn't halt if the store doesn't have the fruit the customer wants or if the customer doesn not have enough money to buy the fruit.)

Bonus Exercise

Organize the code! Make a module called fruits (in a file fruits.py) that contains the class definitions. Make a separate cell (in a file main.py) which imports the fruits module, instantiates the store and the list of customers, and runs the code in the previous exercise.

Further reading

Versions


In [129]:
%reload_ext version_information
%version_information


Out[129]:
SoftwareVersion
Python3.5.1 64bit [GCC 4.2.1 (Apple Inc. build 5577)]
IPython4.0.3
OSDarwin 15.0.0 x86_64 i386 64bit
Sun Mar 20 16:53:36 2016 EDT