1.3 - Basics of Programming

Ha Khanh Nguyen - hknguyen

In this lecture, we will discuss:

• Python semantics
• Object-oriented programming
• Variables and argument passing

• 1. Indentations, not braces
• 2. Everything is an object
• 3. Comments in Python
• 4. Variables and arguments passing
• 5. Dynamic references, strong types
• 6. Attributes and methods
• 7. Imports
• 8. Binary operators and comparisons
• 9. Mutable and immutable objects

---

1. Indentations, not braces

• Other languagues (R, C++, Java, etc.) use braces to structure code.

# R code (not Python)
a = 10
if (a < 5) {
  result = TRUE
  b = 0
} else {
  result = FALSE
  b = 100
}

• Python uses whitespaces (tabs or spaces) to structure code instead.

a = 10
if a < 5:
    result = True
    b = 0
else:
    result = False
    b = 100

• A colon : denotes the start of an indented code block.
• Python statements (like R statements) do not need to be separated by semicolons ;.
• Recommended: Use TAB for indentation.
  • One of the most common errors in Python is incorrect indentation.
  • Be consistent with how you indent your code goes a long way!

---

2. Everything is an object

• Python is an object-oriented programming language (so is R). That is, every number, string, data structure, function, etc. is a Python object.
• Think of them as a BOX. There are "doors" (methods) associated with each box to help us access the content of the box.
• Each object has an associated type (like string, list, DataFrame, etc.).

---

3. Comments in Python

• Use hashtag # to denote a comment in Python (just like R!).
• Recommended: Adding comments to code increase code readability, especially for other people to read your code.

# initialize count
count = 0

for i in [1, 2, 3]:
    # increase count
    count = count + 1

---

4. Variables and arguments passing

• When assigning an object to a variable in Python, you are creating a reference to the object on the righthand side of the equals sign.

a = [1, 2, 3]
b = a

• In Python, a and b actually now refer to the same object, the original list [1, 2, 3].

• In some languages, this assignment would cause the data [1, 2, 3] to be copied.

---

5. Dynamic references, strong types

• Object references (variables) in Python have no type associated with them.

a = 5
type(a)

int

a = 'foo'
type(a)

str

---

6. Attributes and methods

• Objects in Python typically have both:
  • Attributes - other Python objects stored “inside” the object.
  • Methods - functions associated with an object that can have access to the object’s internal data.
• They can be accessed via the syntax obj.attribute_name or obj.method_name.

---

7. Imports

• In Python a module (or library) is simply a file with the .py extension containing Python code.

# some_module.py
PI = 3.14159

def f(x):
      return x + 2

def g(a, b):
      return a + b

• If we want to access the variables and functions defined in some_module.py:

import some_module

result = some_module.f(5)
pi = some_module.PI

Or

import some_module as sm
from some_module import PI as pi, g as gf

r1 = sm.f(pi)
r2 = gf(r1, pi)

---

8. Binary operators and comparisons

Operation	Description
a + b	Add a and b
a - b	Substract b from a
a * b	Multiply a by b
a / b	Divide a by b
a // b	Floor-divide a by b, dropping any fractional remainder
a ** b	Raise a to the b power
a & b	True if both a and b are True; for integers, take the bitwise AND
`a	b`	True if either a or b is True; for integers, take the bitwise OR
a ^ b	True if a is True or b is True, BUT NOT BOTH; for integers, take the bitwise EXCLUSIVE-OR
a == b	True if a equals b
a != b	True if a is not equal to b
a <= b, a < b	True if a is less than (less than or equal) to b
a >= b, a > b	True if a is greater than (greater than or equal) to b
a is b	True if a and b reference the same Python object
a is not b	True if a and be references different Python objects

• Comparing with is is NOT the same as the == operator:

a = [1, 2, 3]
b = a
c = list(a)
a is b

True

a is c

False

a == c

True

---

9. Mutable and immutable objects

• Most objects in Python (list, dict, DataFrame) are mutable, meaning the object or values that they contain can be modified.

a_list = ['foo', 2, [4, 5]]
a_list[2] = (3, 4)
a_list

['foo', 2, (3, 4)]

• Other objects, like strings or tuples, are immutable.

a_tuple = (3, 5, (2, 4))
a_tuple[1] = 2

---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
<ipython-input-8-3fb7f1898f95> in <module>
      1 a_tuple = (3, 5, (2, 4))
----> 2 a_tuple[1] = 2

TypeError: 'tuple' object does not support item assignment

---

This lecture note is modified from Chapter 2 of Wes McKinney's Python for Data Analysis 2nd Ed.