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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

Given the code for a python class, instantiate a python object and call the methods and list the attributes.
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

Match key “magic” methods to their functionality. Full list/tutorial here: http://www.diveintopython3.net/special-method-names.html
Design a program or algorithm in object oriented fashion.
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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tags: data-science-standards python

Part 2 of 43

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

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Data Science Standards: Python
Published: Tue 16 January 2018
By Thomas Dunlap

In main.

tags: data-science-standards python

Part 1 of 43

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

1. Python
1. Explain the difference between mutable and immutable types and their relationship to dictionaries.
  - Mutable - Refers to a data structure …
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Published: Wed 27 December 2017
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There's a lot of expensive data science bootcamps out there: Galvanize, Metis, and NYC Data Academy all cost $16,000. Springboard has an online one that will cost you $5,000 to $8,000. Udacity is on the cheaper side at $200 per/month for their Machine Learning Nanodegree, but …
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AT&T's $1000 Bonuses Aren't Actually Impressive

Published: Wed 20 December 2017
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Statistics Vocabulary

Published: Sun 10 December 2017
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Anecdotal Evidence: Substantiating a claim with potentially biased stories, memories, etc., instead of scientific and statistically rigorous examination.

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Published: Thu 07 December 2017
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Did you know you're supposed to format your commits? And not just write vaguely entertaining reminders to yourself like "Filled in some_function() but there's 3 nested for loops WHY WOULD GOD DO THIS TO ME?!" Just kidding, I would never write that. You can't write a commit message with exclamation …
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Figuring It All Out

Published: Tue 05 December 2017
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I have little idea how this blog works. I know we're supposed to lay out things and explain things like we're experts, and I will eventually, but for future readers admiring my tremendous insights (sarcasm?), know that when I started this I had no clue.

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Second Post!

Published: Tue 05 December 2017
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tags: second post

Second Post!

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Other articles

Part 2 of 43

2. Version Control / Git

Part 1 of 43

1. Python

Second Post!

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