Summary: Computer Science and Programming in Python - Part 02

Intro

On my path of Learning In Public, I try to learn as efficient as possible. The stuff has to fit my needs, but I don't want to waste time by binge-learning useless stuff.

Therefore I decided to have a look at Computer Science and Programming in Python.

I don't know if it is too easy for me, because I know most of the Computer Science stuff, but therefore I decided to use a course with Python, because I only new the basics of it. If it is too easy, I go faster.

Links

I created a list with all the links to the summary, the videos and the slides.

There is also a link to the github repo, I invite you to make a Pull Request if you see some typos.

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Summary

05. Tuples, Lists, Aliasing, Mutability, Cloning

Compound Data Types

• made up of other data types

Tuples

• ordered sequence of (mixed) elements
• immutable
• creation: ()
• swap: (a, b) = (b, a)
• iterable

Lists

• ordered sequence of (mixed) elements
• mutable
• creation: []
• iterable
• add element: a.append(b)
• concat lists: a.extend(b)
• delete element: del(a[index])
• sort list: a.sort()
• reverse list: a.reverse()
• string to lists and vice-versa: split(), join()

Aliasing, Mutability, Cloning

• variable name points to object, e.g. a person
• many different variables can point to the same object, e.g. person has multiple nicknames
• if you change the object (=> the person object), any variable (=> any nickname) pointing to that object is affected

=> side effects!

• copy list: copy = original[:]

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06. Recursion, Dictionaries

Recursion

• the process of repeating items in a self-similar way
• algorithmically: a way to design solutions by divide-and-conquer: reduce a problem to simpler versions of the same problem
• semantically: a programming technique where a function calls itself: base case + recursive step
• e.g. multiplication of a and b by adding a to itself b times
• each recursive call to a function creates its own scope
• bindings of variables in a scope are not changed by recursive call
• flow of control passes back to previous scope once function call returns value

Iteration vs. Recursion

# iteration
def factorial_iter(n):
  prod = 1
  for i in range(1, n+1):
    prod *= i
  return prod

# recursion
def factorial(n):
  if n == 1:
    return 1
  else:
    return n * factorial(n-1)

• recursion may be simpler, more intuitive
• recursion may be efficient from programmer point of view
• recursion may not be efficient from computer point of view

Induction

• To prove a statement indexed on integers is true for all values of n:
  • Prove it is true when n is smallest value (e.g. n = 0 or n = 1)
  • Then prove that if it is true for an arbitrary value of n, one can show that it must be true for n+1

Dictionaries

• store data as key-value pairs
• creation: {}
• unordered
• add element: dict[newKey] = newValue
• delete element: del(dict[key])
• get all keys: dict.keys()
• get all values: dict.values()

List vs. Dictionary

• List: ordered <===> Dict: unordered
• List: matches index to value <===> Dict: matches key to value
• List: look up element by index (starts from 0, increasing) <===> Dict: look up element by key (whatever you like)

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07. Testing, Debugging, Exceptions, Assertions

• You are making soup but bugs keep falling in from the ceiling. What do you do?
  • keep lid closed: defensive programming
  • check soup for bugs: testing
  • clean kitchen: debugging

Defensive Programming

• Decomposition
• Abstraction

Testing

• Compare input/output pairs to specification

Types

• Unit Test: validate each piece of program separately
• Regression Test: catch reintroduced errors that were previously fixed
• Integration Test: test if overall programm works

Approaches

• black box testing:
  • explore paths through specification
  • can be reused if implementation changes
  • can be done by someone other than the implementer (biased)
• glass box testing:
  • explore paths through code
  • - called path-complete if every potential path through code is tested at least once

Debugging

• Study events leading up to an error
• steep learning curve
• goal is to have a bug-free program
• tools:
  • print with bisecting
  • read error messages
  • logic errors: think before coding, explain code to someone else

DON'T

1. write entire program
2. test entire program
3. debug entire program

DO

1. write one function
2. unit test this function
3. debug this function
4. integration test
5. repeat from 1.

Exception

• handle exception: use try & except
• never fail silently
• instead raise exception

Assertions

• assertions don’t allow a programmer to control response to unexpected conditions
• ensure that execution halts whenever an expected condition is not met
• typically used to check inputs to functions, but can be used anywhere
• can be used to check outputs of a function to avoid propagating bad values
• can make it easier to locate a source of a bug
• goal is to spot bugs as soon as introduced and make clear where they happened
• use as a supplement to testing
• raise exceptions if users supplies bad data input
• use assertions to:
  • check types of arguments or values
  • check that invariants on data structures are met
  • check constraints on return values
  • check for violations of constraints on procedure (e.g. no duplicates in a list)

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08. Object Oriented Programming

Basics

• Python supports many different kinds of data
• each is an object, and every object has:
  • a type
  • an internal data representation (primitive or composite)
  • a set of procedures for interaction with the object
• an object is an instance of a type: 123 is an instance of an int, hello is an instance of a string
• EVERYTHING IN PYTHON IS AN OBJECT (and has a type)
• can create new objects of some type
• can update objects
• can delete objects
  • explicitly using del or just “forget” about them
  • python system will reclaim destroyed or inaccessible objects – called “garbage collection”

What Are Objects?

• objects are a data abstraction that captures:
  • an internal representation: through data attributes
  • an interface for interacting with object:
  • through methods (aka procedures/functions)
  • defines behaviors but hides implementation
• internal representation should be private
• correct behavior may be compromised if you manipulate internal representation directly

Advantages Of OOP

• bundle data into packages together with procedures that work on them through interfaces
• divide- and- conquer development:
  • implement and test behavior of each class separately
  • increased modularity reduces complexity
• classes make it easy to reuse code
  • many Python modules define new classes
  • each class has a separate environment (no collision on function names)
  • inheritance allows subclasses to redefine or extend a selected subset of a superclass’ behavior

Creating And Using Your Own Types With Classes

• make a distinction between creating a class and using an instance of the class
• creating the class involves:
  • defining the class name
  • defining class attributes
  • for example, someone wrote code to implement a list class
• using the class involves:
  • creating new instances of objects
  • doing operations on the instances

What Are Attributes?

• data and procedures that “belong” to the class
• data attributes:
  • think of data as other objects that make up the class
  • for example, a coordinate is made up of two numbers
• methods (procedural attributes):
  • think of methods as functions that only work with this class
  • how to interact with the object
  • for example you can define a distance between two coordinate objects but there is no meaning to a distance between two list objects
• defining how to create an instance: special method init
• actually creating an instance: var_name = class_name(parameters)
• . operator is used to access any attribute (data attribute or method) of an object
• use isinstance() to check if an object is an instance of a class

The Power Of OOP

• bundle together objects that share:
  • common attributes and
  • procedures that operate on those attributes
• use abstraction to make a distinction between how to implement an object vs how to use the object
• build layers of object abstractions that inherit behaviors from other classes of objects
• create our own classes of objects on top of Python’s basic classes

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