Data Science Standards: Python OOP

Part 3 of 43

This is the third in a series of questions and answers about core data science topics. See more standards on my Data Science Standards page.

3. OOP

  1. Given the code for a python class, instantiate a python object and call the methods and list the attributes.
  2. Write the python code for a simple class.
class ClassName:
  def __init__(self, parameter1, p2):
    self.var = parameter1
    self.var2 = p2

  def __repr__(self):
  """Return a text description."""
    return "{} of {}".format(self.var, self.var2)

  @property # Field can't be changed w/o making setter (@color.setter)
  def color(self):
    return self._c_map[self.p2]

  def __call__(self, elem):
  '''Perform map operation on an element'''
    return self._impl(elem)

  def _impl(self, elem):
    pass

  def method_name(self, p3):
    self.var2 += self.var * p3

  1. Match key “magic” methods to their functionality. Full list/tutorial here: http://www.diveintopython3.net/special-method-names.html

  2. Design a program or algorithm in object oriented fashion.

  3. Compare and contrast functional and object oriented programming.
    • In OOP the user creates and defines a new data type, which it's own attributes (data), and methods (operations). This new class can then be used to create objects.
    • In functional programming, your program just uses functions and does not create an entirely new class to organize data around.
    • Functional programming typically is faster, takes up less memory, but can less organized and difficult for humans to interpret if there's a lot going on.

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