Unit 2: Introduction to Python

1. Basics of Python Programming

Python is a high-level, interpreted programming language known for its simplicity and readability. It emphasizes code clarity and uses meaningful indentation.

Key Characteristics:

• Simple Syntax: Easy to learn and read, similar to English
• Interpreted: Code is executed line by line
• Dynamically Typed: Variables don't need type declarations
• Object-Oriented: Supports classes and objects
• Extensive Libraries: Rich collection of built-in modules

First Program:

print("Hello, Python!")
x = 5
y = 10
print(f"Sum: {x + y}")

2. Execution Modes

Interactive Mode

Immediate feedback on commands. Type python in terminal to enter.

>>> x = 5
>>> print(x)
5
>>> print(x * 2)
10

Script Mode

Write code in a .py file and execute it with python filename.py

# hello.py
name = input("Enter your name: ")
print(f"Hello, {name}!")

3. Program Structure & Indentation

Python uses indentation to define code blocks. Indentation is mandatory and consistent.

# Comments start with #

# Single-line comment
"""
Multi-line comment
Also used for docstrings
"""

# Statements
x = 10
if x > 5:
    print("x is greater than 5")  # 4-space indentation
    
# Functions
def greet(name):
    message = f"Hello, {name}"
    return message

4. Identifiers, Keywords & Constants

Identifiers

Names given to variables, functions, classes, etc. Must follow rules:

• Start with letter or underscore, not a number
• Can contain letters, numbers, and underscores
• Case-sensitive: myVar and myvar are different
• No spaces allowed

Keywords

Reserved words with special meaning in Python:

if, else, elif, while, for, break, continue, def, return, class
import, from, try, except, finally, raise, with, as, pass

Constants

Fixed values that don't change. By convention, use uppercase:

PI = 3.14159
MAX_SIZE = 100
GRAVITY = 9.8

5. Variables & Data Types

Variable Declaration

# No explicit type declaration needed
x = 10              # int
name = "Python"     # str
score = 9.5         # float
is_valid = True     # bool

# Multiple assignment
a, b, c = 1, 2, 3
x = y = z = 0       # All equal to 0

Data Types

Type Conversion

# Explicit conversion
str_num = "123"
num = int(str_num)              # Convert to int: 123
decimal = float("3.14")         # Convert to float: 3.14
text = str(42)                  # Convert to string: "42"
is_true = bool(1)               # Convert to bool: True

# Using type() to check data type
print(type(x))                  # <class 'int'>
print(type("hello"))            # <class 'str'>

6. Operators & Precedence

Operator Precedence (Highest to Lowest)

1. ** (Exponentiation)
2. +x, -x, ~x (Unary)
3. *, /, //, % (Multiplication/Division)
4. +, - (Addition/Subtraction)
5. ==, !=, <, >, <=, >= (Comparison)
6. not (Logical NOT)
7. and (Logical AND)
8. or (Logical OR)

# Example
result = 2 + 3 * 4 ** 2
# = 2 + 3 * 16 = 2 + 48 = 50

7. Control Statements

if-else Statement

# Simple if-else
age = 18
if age >= 18:
    print("Adult")
else:
    print("Minor")

if-elif-else Statement

# Grade classification
marks = 75
if marks >= 90:
    print("Grade A")
elif marks >= 80:
    print("Grade B")
elif marks >= 70:
    print("Grade C")
else:
    print("Grade D")

while Loop

# Print 1 to 5
i = 1
while i <= 5:
    print(i)
    i += 1

# Sum of first n numbers
n = 5
sum = 0
i = 1
while i <= n:
    sum += i
    i += 1
print(f"Sum: {sum}")

for Loop

# Using range
for i in range(1, 6):  # 1 to 5
    print(i)

# Iterating through list
fruits = ["apple", "banana", "orange"]
for fruit in fruits:
    print(fruit)

# Using enumerate for index
for index, fruit in enumerate(fruits):
    print(f"{index}: {fruit}")

break and continue

# break - exits loop
for i in range(1, 11):
    if i == 5:
        break
    print(i)  # Prints 1 to 4

# continue - skips current iteration
for i in range(1, 6):
    if i == 3:
        continue
    print(i)  # Prints 1, 2, 4, 5

8. Lists

Creating and Initializing Lists

# Different ways to create lists
numbers = [1, 2, 3, 4, 5]
mixed = [1, "Python", 3.14, True]
empty = []
from_range = list(range(10))    # [0, 1, 2, ..., 9]

# Accessing elements
print(numbers[0])               # 1 (first)
print(numbers[-1])              # 5 (last)
print(numbers[1:4])             # [2, 3, 4] (slicing)

List Methods and Operations

Practical Example

scores = [85, 90, 78, 92, 88]

# Traversing
for score in scores:
    print(score)

# Manipulation
scores.append(95)
scores.insert(0, 100)
scores.remove(78)
scores.sort()

# Statistics
print(f"Avg: {sum(scores)/len(scores):.2f}")
print(f"Highest: {max(scores)}, Lowest: {min(scores)}")

9. Dictionaries

Key-Value Pairs

# Creating dictionary
student = {
    "name": "Alice",
    "age": 20,
    "course": "CS",
    "gpa": 3.8
}

# Accessing
print(student["name"])          # Alice
print(student.get("age"))       # 20

# Adding/Updating
student["email"] = "alice@mail.com"  # Add
student["age"] = 21                  # Update

# Deleting
del student["gpa"]

Dictionary Methods

Practical Example

students = {
    "S001": {"name": "Alice", "marks": 85},
    "S002": {"name": "Bob", "marks": 92},
    "S003": {"name": "Carol", "marks": 78}
}

# Traversing
for roll, details in students.items():
    print(f"{roll}: {details['name']} - {details['marks']}")

# Finding highest
highest = max(students.items(), key=lambda x: x[1]["marks"])
print(f"Topper: {highest[1]['name']}")

10. Introduction to NumPy

What is NumPy?

Creating NumPy Arrays

import numpy as np

# From Python list
arr1 = np.array([1, 2, 3, 4, 5])
print(arr1)                     # [1 2 3 4 5]

# Multi-dimensional array
arr2 = np.array([[1, 2, 3], [4, 5, 6]])

# Using built-in functions
arr3 = np.zeros(5)              # [0. 0. 0. 0. 0.]
arr4 = np.ones(3)               # [1. 1. 1.]
arr5 = np.arange(0, 10, 2)      # [0 2 4 6 8]
arr6 = np.linspace(0, 1, 5)     # [0. 0.25 0.5 0.75 1.]

Array Properties

import numpy as np

arr = np.array([[1, 2, 3], [4, 5, 6]])

print(arr.shape)                # (2, 3) - 2 rows, 3 cols
print(arr.dtype)                # int64 - data type
print(arr.size)                 # 6 - total elements
print(arr.ndim)                 # 2 - dimensions

Array Operations

import numpy as np

arr = np.array([1, 2, 3, 4, 5])

# Arithmetic
result = arr * 2                # [2 4 6 8 10]
result = arr + 10               # [11 12 13 14 15]

# Statistical functions
print(np.sum(arr))              # 15
print(np.mean(arr))             # 3.0
print(np.max(arr))              # 5
print(np.min(arr))              # 1
print(np.std(arr))              # Standard deviation

Applications of NumPy

Practical Example

import numpy as np

# Grade analysis
grades = np.array([85, 90, 78, 92, 88, 76, 95])

print(f"Average: {np.mean(grades):.2f}")
print(f"Highest: {np.max(grades)}")
print(f"Lowest: {np.min(grades)}")
print(f"Std Dev: {np.std(grades):.2f}")

# Count above average
above_avg = np.sum(grades > np.mean(grades))
print(f"Above average: {above_avg}")

Tips & Takeaways