Python 3 Core Concepts: A Practical Guide

This guide covers essential Python programming concepts through hands-on code eaxmples. Each section includes working code snippets with expected output.

1. Working with Jupyter Notebook

---

Jupyter Notebook is an interactive development environment widely used in Python development. Here are essential keyboard shortcuts:

• Select a cell and press DD to delete it
• Press Shift+Enter to run code and move to the next cell
• Press Ctrl+Enter to run code without moving
• Press M to convert a cell to Markdown format

2. The print() Function

---

The print() function outputs values to the console. It handles strings, numbers, and concatenation.

print(1)
print('hello')
print("it's working")
print('it\'s working')
print('a' + 'b')

Output:

1
hello
it's working
it's working
ab

3. Mathematical Operations

---

Python supports standard mathematical operators. The exponentiation operator (**) raises a number to a power.

result = 2 ** 3
print(result)

Output:

8

4. Loops and Indentation

---

Python uses indentation to define code blocks. Four spaces is the standard indentation level.

While Loop

i = 0
while i < 3:
    print(i)
    i = i + 1

Output:

0
1
2

For Loop

for i in range(1, 5):
    print(i)

Output:

1
2
3
4

The range() function generates a sequence from the start value (inclusive) to the end value (exclusive).

5. Conditional Statements

---

Python supports if, elif (else if), and else statements for conditional logic.

a = 10
b = 5

# Simple if statement
if a > b:
    print('a is greater than b')

# If-else statement
if a > b:
    print('a is greater')
else:
    print('b is greater or equal')

# If-elif-else chain
a = 5
b = 5
if a > b:
    print('a is greater')
elif a == b:
    print('a equals b')
else:
    print('b is greater')

6. Functions

---

Functions are defined using the def keyword. They can accept parameters and return values.

def add_numbers(x, y):
    result = x + y
    print(result)

add_numbers(3, 4)

Output:

7

Default Parameters

Parameters can have default values. When a default parameter is provided, the argument becomes optional.

def default_function(a, b=3):
    result = a + b
    print(result)

default_function(2)

Output:

5

Important Rule: Non-default arguments cannot follow default argumants. The following code will raise a SyntaxError:

# This will cause an error
def invalid_function(a=3, b):
    print(a + b)

Error: SyntaxError: non-default argument follows default argument

7. File Operations

---

Creating and Writing to a File

content = "1,2,3"
file = open('data.txt', 'w')
file.write(content)
file.close()

Appending to an Existing File

new_content = "\nhello world"
file = open('data.txt', 'a')
file.write(new_content)
file.close()

Reading from a File

file = open('data.txt', 'r')
content = file.read()
print(content)
file.close()

Output:

1,2,3
hello world

8. Classes and Object-Oriented Programming

---

Classes define blueprints for creating objects. They encapsulate data and functionality.

class Calculator:
    price = 18
    brand = "casco"
    
    def add(self, x, y):
        result = x + y
        print(result)
    
    def show_price(self):
        print(self.price)

calc = Calculator()
calc.add(3, 5)
calc.show_price()

Output:

8
18

The self parameter refers to the instance of the class and must be included in all method definitions.

The __init__ Constructor

The __init__ method initializes object attributes when an instance is created.

class Calculator:
    def __init__(self, name, price, brand, size):
        self.n = name
        self.p = price
        self.b = brand
        self.s = size
    
    def show_details(self):
        print(self.n, self.p, self.b, self.s)

calc2 = Calculator('q', 3, 'y', 2)
calc2.show_details()
print(calc2.n)

Default Values in Class Constructors

class Calculator:
    def __init__(self, name='e', price=8, brand='u', size=7):
        self.n = name
        self.p = price
        self.b = brand
        self.s = size
    
    def show_details(self):
        print(self.n, self.p, self.b, self.s)

calc3 = Calculator()
calc3.show_details()

Output:

e 8 u 7

9. The input() Function

---

The input() function reads user input from the console. All input is treated as a string by default.

user_input = input()
if user_input == '1':
    print('yes')
else:
    print('no')

Input:

1

Output:

yes

To convert input to other types, use type casting functions like int() or float().

10. Tuples and Lists

---

Tuples are immutable sequences, while lists are mutable. Both can store multiple items.

# Tuple - immutable
a_tuple = (1, 2, 3, 4, 5, 6)

# List - mutable
a_list = [7, 6, 5, 4, 3, 2]

# Iterating over a tuple
for item in a_tuple:
    print(item, end=' ')
print()

# Accessing list elements by index
for i in range(len(a_list)):
    print(a_list[i], end=' ')
print()

Output:

1 2 3 4 5 6 
7 6 5 4 3 2 

List Methods

a_list = [7, 6, 5, 4, 3, 2]
a_list.append('a')
print(a_list)

a_list.insert(3, 'h')
print(a_list)

a_list.remove('h')
print(a_list)

print(a_list[-1])  # Last element
print(a_list[2:4])  # Elements from index 2 to 3
print(a_list.index('a'))  # Find index of element

b_list = [1, 8, 5, 8, 9, 2]
b_list.sort()
print(b_list)  # Small to large

b_list.sort(reverse=True)
print(b_list)  # Large to small

Output:

[7, 6, 5, 4, 3, 2, 'a']
[7, 6, 5, 'h', 4, 3, 2, 'a']
[7, 6, 5, 4, 3, 2, 'a']
a
[5, 4]
6
[1, 2, 5, 8, 8, 9]
[9, 8, 8, 5, 2, 1]

Multi-dimensional Lists

multi_list = [
    [1, 2, 3],
    [4, 5, 6],
    [7, 8, 9]
]
print(multi_list[0][0])  # First row, first column
print(multi_list[1][2])  # Second row, third column

Output:

1
6

11. Dictionaries

---

Dictionaries store key-value pairs. Keys must be unique and immutable.

student = {'name': 'Alice', 'age': 25, 'major': 'Computer Science'}
print(student['age'])

# Delete a key-value pair
del student['major']
print(student)

# Values can be tuples, lists, functions, or even other dictionaries
complex_dict = {
    'data': (1, 2, 3),
    'nested': {'inner': 'value'},
    'function': len
}

Output:

25
{'name': 'Alice', 'age': 25}

12. Importing Modules

---

Modules extend Python's functionality. Use the import statement to include external libraries.

import time as t
print(t.localtime())

Output:

time.struct_time(tm_year=2025, tm_mon=1, tm_mday=15, tm_hour=10, tm_min=30, tm_sec=45, tm_wday=2, tm_yday=15, tm_isdst=0)

13. Break and Continue Statements

---

The break statement exits a loop completely. The continue statement skips to the next iteration.

# Using break to exit loop
while True:
    user_input = input()
    if user_input == 'quit':
        print('ending program')
        break
    else:
        print('continuing...')

# Using continue to skip iteration
while True:
    user_input = input()
    if user_input == 'skip':
        print('skipping this iteration')
        continue
    elif user_input == 'quit':
        break
    else:
        print('processing:', user_input)

14. Functional Programming: map, lambda, and zip

---

The zip() Function

zip() combines multiple iterables element-wise into tuples.

list1 = [1, 2, 3]
list2 = [4, 5, 6]
combined = list(zip(list1, list2))
print(combined)

# zip with three lists
list3 = [7, 8, 9]
triple = list(zip(list1, list2, list3))
print(triple)

Output:

[(1, 4), (2, 5), (3, 6)]
[(1, 4, 7), (2, 5, 8), (3, 6, 9)]

Lambda Functions

Lambda functions are anonymous functions defined in a single line. They are useful for short operations.

# Regular function
def add(x, y):
    return x + y

result = add(2, 6)
print(result)

# Lambda equivalent
add_lambda = lambda x, y: x + y
result = add_lambda(4, 3)
print(result)

Output:

8
7

The map() Function

map() applies a function to each element of an iterable.

def multiply(x, y):
    return x * y

# map with two lists
result = map(multiply, [2, 3, 4], [5, 6, 7])
print(list(result))

Output:

[10, 18, 28]

Combining lambda with map provides a concise way to transform data:

numbers = [1, 2, 3, 4, 5]
squared = list(map(lambda x: x ** 2, numbers))
print(squared)

Output:

[1, 4, 9, 16, 25]