PPOL 5203 - Data Science I: Foundations

Week 3: Intro to Python - OOP, Data Types, Control Statements and Functions

Professor: Tiago Ventura

Plans for Today

  • Problem Set 1

  • Quick review of Git and remotes with Github

  • In-class exercise about Git

  • Introduction to Python

    • Python as an Object-Oriented Programming Language

    • Data Types in Python

  • Next week (likely):

    • Control statements

    • Functions

First Problem Set Assignment and Datacamp

The three stages of Git

Github (Remote Git Repositories)

Git Remotes: Git + Github.

  • Most times, you will use git integrated with Github.

  • Github allows multiple researchers to write and share code at the same time.

  • This is my workflow for github when starting a New Project. Before you write any code:

    • Go to your github, and create a new repository

    • Open your terminal, and clone the project

    • cd intro your new local folder

# clone
git clone <url>

#Move your working directory to this new folder
cd <project-directory>

#Write code!

Track your changes:

git add .

Commit:

git commit -m 'describe your commit'

Push the changes in your local repository to GitHub:

git push 
# or with branch
git push -u origin [branch-name]

When you do this for this first time, you need to set yo your identity

git config --global user.name "Your Name" 
git config --global user.email "your-email@example.com"

Git will ask:

  • username
  • password: THIS IS YOUR TOKEN. SEE LECTURE NOTES

Pull from Remotes

To keep up with your colleagues work, you need to first pull their updates from the git repo.

# go to your repo
cd <gitrepo>

# pull the changes
git pull

See this tutorial

Some additional tasks:

  • check the discussion about .gitignore in the lecture notes.

  • you might need to set up an personal token to push things on github, see here

  • play around with gitub: readme, directories, and issues.

Questions?

Practice!

Click here to setup your github classroom and do the in-class from last week for you to practice.

Git in Class Exercise

15:00

Solution

TLDR – Don’t Panic!

  • Git should be new for you. Don’t feel desperate if you struggling with git. Most people do.

  • Google every time you have a question. That’s a big part of being a data scientist.

  • Check the Cheat sheet and your lecture notes

Introduction to Python

Python: Object-Oriented Programming Language

In most Python introductions out there, you would start with:

  • Python as a calculator

  • Data Types in Python

  • Objects

We will take a different route starting with a deeper understanding of object-oriented programming

Object-oriented programming (OOP) vs Functional Programming Language

  • Python is an object-oriented programming language (OOP).

    • Key concept: it uses “objects” that contains data and methods (functions).

  • R is a functional programming language

    • Key concept: treats functions as the first-class citizens, meaning they can be assigned to variables, passed as arguments, and returned by other functions

Python OOP

# call library
import pandas as pd

# Using a Pandas Series method (OOP)
s = pd.Series([1, 2, 3, 4, 5])

# create a series object. You use a methods from this object to take the mean
s.mean()


R Functional Programming in Practice

vec <- c(1, 2, 3, 4, 5)
mean(vec)
  • Objects are the core of Python.
  • From the objects, you will access functions that will make Python work.
  • You can also access data from object.

Basics: Creating an object in Python.

= is the assignment operator in Python. (Different from R)

x = 4

What happens upon assignment?

Action 1: A reference is assigned to an object, with an id number in memory

id(x)
4842857480

Action 2 An object type (class) is defined at run time

type(x)
<class 'int'>

Action 3: Object’s class is instantiated upon assignment. An object is an instance of a particular class.

# what is the class?
type(x)
<class 'int'>
# Access methods (behaviors) using .
x.bit_length()
3
# see all methods
dir(x)
['__abs__', '__add__', '__and__', '__bool__', '__ceil__', '__class__', '__delattr__', '__dir__', '__divmod__', '__doc__', '__eq__', '__float__', '__floor__', '__floordiv__', '__format__', '__ge__', '__getattribute__', '__getnewargs__', '__getstate__', '__gt__', '__hash__', '__index__', '__init__', '__init_subclass__', '__int__', '__invert__', '__le__', '__lshift__', '__lt__', '__mod__', '__mul__', '__ne__', '__neg__', '__new__', '__or__', '__pos__', '__pow__', '__radd__', '__rand__', '__rdivmod__', '__reduce__', '__reduce_ex__', '__repr__', '__rfloordiv__', '__rlshift__', '__rmod__', '__rmul__', '__ror__', '__round__', '__rpow__', '__rrshift__', '__rshift__', '__rsub__', '__rtruediv__', '__rxor__', '__setattr__', '__sizeof__', '__str__', '__sub__', '__subclasshook__', '__truediv__', '__trunc__', '__xor__', 'as_integer_ratio', 'bit_count', 'bit_length', 'conjugate', 'denominator', 'from_bytes', 'imag', 'numerator', 'real', 'to_bytes']

Object-Oriented Programming: What are these classes?

  • Instantiation: When we create a object, we are creating an instance of this class.

  • Inheritance: Every time we create an object, the objects inherits a class.

    • A class is a blueprint holding of the properties of a particular data structure.

    • An instance is a realization of a particular class. This instance inherits the characteristics of its class.

  • Components: Classes have two major components:

    • Attributes: these are constant features, data, a characteristic of the broader class

    • Methods: these are actions, behaviors of this class. functions

  • Polymorphism: Both attributes and methods are accessed through . function, conditional on the class

Create a Class

class Example():
  def __init__ (self, name):
    self.name = name
  def hello(self):
    print('Hi, I am ' + self.name)

Instantiate

# Instatiate
me = Example(name="Tiago")
type(me)
<class '__main__.Example'>

Attributes

me.name
'Tiago'

method

me.hello() 
Hi, I am Tiago

Coding

Data types

Quizz: What are data types?

Data Type

Python comes with a number of built-in data types. Two major data type groups in Python:

  • Scalar types: hold one piece of information, like a digit.

  • Collection types: hold multiple pieces of information

There are two ways of instantiating a data class in Python:

  • Literals: syntactic representation in Python, e.g. []

  • Constructors: class constructors, e.g. list()

Scalar

Type Description Example Literal Constructor
int integer types 4 x = 4 int(4)
float 64-bit floating point numbers 4.567 x = 4.567 float(4)
bool boolean logical values True x = True bool(0)
None null object (serves as a valuable place holder) None x = None

Collectors

Type Description Example Mutable Literal Constructor
list heterogeneous sequences of objects [1,"2",True] ✓ x = ["c","a","t"] x = list("cat")
str sequences of characters "A word" ✘ x = "12345" x = str(12345)
tuples heterogeneous sequence of objects (1,2) ✘ x = (1,2) x = tuple([1,2])
sets unordered collection of distinct objects {1,2} ✓ x = {1,2} x = set([1,2])
dicts associative array of key/value mappings {"a": 1} keys ✘
values ✓		 x = {'a':1} x = dict(a = 1)

Coding

Key concepts

  • Why, different from R, I should use . to run specific functions, instead of just running the functions on a object function(obj)?

  • Tuples and Lists are pretty much the same. But tuples are more computationally efficient (storage and speed in accessing it) than lists. Why do you think this is the case?

  • Assume I have this dictionary my_dict = {'a': 4, 'b': 7, 'c': 9.2}. If I tried to access the first element of the dictionary with my_dict[0], Python will throw me an error. Why?

In-class exercises

Let’s practice with lists first. One way to explore data structures is to learn their methods. Check all the methods of a list by running ‘dir()’ on a list object. Let’s explore these functions using the following list object, by answering the below questions. See here for list methods:


list_exercise = ["Ramy", "Victorie", "Letty", "Robin", "Antoine", "Griffin"]


  1. Add “Cathy O’Neil” to the list. Insert “Professor Crast” as the first element of the list
  1. Remove “Letty” from the list. Also remove the last element of the list.
  1. Find the index of the occurrence of the name “Robin”. Count the number of times None appears in the list.
  1. Create a new list with the names in alphabetical order, copy this list as a new list without changing the values of the original list
  1. Add the string “Lovell” to copied_list and ensure that list_exercise remains unchanged.

Let’s do a similar exercise with Dictionaries. Consider the dictionary below. See here for dictionary methods:


dict_exercise = {"Ramy": "India",
                  "Victorie":"Haiti", 
                  "Letty":"England", 
                  "Robin":"Canton", 
                  "Antoine":"Nigeria", 
                  "Griffin":"China"}
dict_exercise


  1. Look up the keys in the dictionary, and store them in a list object called keys
  1. Add yourself, and two other colleagues in this dictionary. The values are the countries the person in the key was born.
  1. Remove “Ramy” from the dictionary, and save as another dictionary

Let’s now play around with some string methods. See the string below from the book “Babel:An Arcane History”. See here for string methods:

babel = "That's just what translation is, I think. That's all speaking is. Listening to the other and trying to see past your own biases to glimpse what they're trying to say. Showing yourself to the world, and hoping someone else understands."
  1. Determine if the word “Babel” is present in the string.
  1. Count how many times the word “translation” appears
  1. Convert the entire string to upper case
  1. Convert the pronoum “I” to “We” in the entire text.
  1. Strip any punctuation (like commas, exclamation marks, etc.) from the string.

Let’s take a break (Until next week!)

10:00

Class Part II: Control Statements, Loops and Functions

We will go over some concepts that are very general for any programming language.

  • Using logical operators for comparisons.

  • Control the behavior of code when iterating using control statements.

  • Explore iterating through containers using loops

  • Defining functions to make code more flexible, debuggable, and readable. (Probably for next week)

Comparison Operators

Operator Property
== (value) equivalence
> greater than
< strictly less than
<= less than or equal
> strictly greater than
>= greater than or equal
!= Not Equals
is object identity
is not negated object identity
in membership in
not in negated membership in

Conditional Statements

Any programming language needs statements that controls the sequence of execution of a particular piece of code.

  • if-else statements
  • for loops
  • while loops

Ifelse Statements

Definition: Conditional execution.


if <logical statement>:
 
 
  ~~~~ CODE ~~~~


elif <logical statement>:


  ~~~~ CODE ~~~~


else:

  ~~~~ CODE ~~~~

For loops

Definition: Taking one item at a time from a collection. We start at the beginning of the collection and mover through it until we reach the end.

In python, we can iterate over:

  • lists

  • strings

  • dictionaries

  • file connections

  • grouped pandas df

Example:

# create a list
my_list = [1, 2, 3, 4, 5]

# iterate with a for loop:
for m in my_list:
  print(m)
1
2
3
4
5

While Loops

  • While loops are all about repetitions. Instead of a sequence, the operation will repeat up according to the conditional statement in the loop.
# while loops
x = 0
while x < 5:
    print("This")
    x += 1
This
This
This
This
This

Coding

Data science I: Foundations