What is Python?

Python is a programming language that lets you talk to a computer and tell it what to do. It’s like giving instructions to your robot friend. Python is easy to read and fun to learn!

How to Start?

• Go to https://replit.com
• Or install Python from https://python.org
• Open an editor and start coding!

Your First Program

print("Hello, world!")

1. Python as a Calculator

print(3 + 2)
print(5 - 1)
print(4 * 2)
print(8 / 2)

2. Variables

name = "Alex"
age = 10
print(name)
print(age)

3. Strings (Words)

greeting = "Hi there!"
print(greeting)
print("My name is " + name)

4. Numbers

apples = 5
oranges = 3
total = apples + oranges
print("Total fruits:", total)

5. If Statements (Making Decisions)

age = 12

if age >= 10:
    print("You're allowed to play!")
else:
    print("Sorry, you're too young.")

6. Loops (Repeat Things)

For Loop

for i in range(5):
    print("This is number", i)

While Loop

count = 0
while count < 3:
    print("Counting...", count)
    count += 1

7. Functions (Little Machines)

def say_hello():
    print("Hello there!")

say_hello()

Function with a Parameter

def greet(name):
    print("Hello", name)

greet("Sam")
greet("Ella")

8. Lists (Groups of Items)

fruits = ["apple", "banana", "cherry"]
print(fruits[0])
print(len(fruits))

for fruit in fruits:
    print("I like", fruit)

9. Random Fun!

import random

number = random.randint(1, 10)
print("Your lucky number is:", number)

10. Build a Simple Game: Guess the Number

import random

secret = random.randint(1, 5)
guess = int(input("Guess a number between 1 and 5: "))

if guess == secret:
    print("🎉 You got it right!")
else:
    print("Oops! The number was", secret)

11. Bonus Fun: Turtle Graphics

import turtle

t = turtle.Turtle()

for i in range(4):
    t.forward(100)
    t.right(90)

turtle.done()

12. Doing More Math with Python

# Math with operators
print(10 % 3)   # Modulus (Remainder)
print(2 ** 4)   # Exponent (2^4 = 16)
print(15 // 4)  # Floor Division

import math

print(math.sqrt(64))    # Square root
print(math.factorial(5))# Factorial
print(math.sin(0))      # Trigonometric functions

13. Meet NumPy: Fast Math with Arrays

import numpy as np

a = np.array([1, 2, 3])
b = np.array([4, 5, 6])
print(a + b)
print(np.sqrt(a))

14. Meet pandas: Play with Data Tables

import pandas as pd

data = {
  "Name": ["Ava", "Ben", "Cara"],
  "Marks": [88, 92, 85]
}

df = pd.DataFrame(data)
print(df)
print(df["Marks"].mean())

Great Python Books for Kids

• Python for Kids by Jason R. Briggs
• Coding Projects in Python by DK
• Teach Your Kids to Code by Dr. Bryson Payne
• Adventures in Python by Craig Richardson
• Mission Python by Sean McManus

Basic Python Math

print(10 + 5)     # Addition
print(10 - 2)     # Subtraction
print(4 * 3)      # Multiplication
print(9 / 3)      # Division
print(10 % 3)     # Remainder (modulus)
print(2 ** 3)     # Exponents (2^3 = 8)
print(15 // 4)    # Floor division
 

import math

print(math.sqrt(25))     # Square root
print(math.pow(2, 5))     # 2 to the power of 5
print(math.pi)            # Value of Pi
print(math.sin(0))        # Sine function
print(math.factorial(5))  # Factorial of 5 

NumPy: Vectors and Matrices

NumPy is a library used for fast math on big lists of numbers called arrays.

Arrays and Vectors

import numpy as np

v = np.array([1, 2, 3])
print("Vector:", v)
print("Add 5:", v + 5)
print("Double:", v * 2)
print("Squared:", v ** 2) 

Matrices (2D Arrays)

matrix = np.array([[1, 2], [3, 4]])
print(matrix)
print("Transpose:", matrix.T)
print("Multiplied:", matrix * 2) 

Matrix Multiplication

A = np.array([[1, 2], [3, 4]])
B = np.array([[5, 6], [7, 8]])
print("Dot Product:", np.dot(A, B)) 

Other NumPy Functions

arr = np.array([4, 7, 2, 9, 5])
print("Max:", np.max(arr))
print("Min:", np.min(arr))
print("Mean:", np.mean(arr))
print("Sum:", np.sum(arr))
print("Sorted:", np.sort(arr)) 

pandas: Work with CSV Data

pandas is like Excel in Python! It helps organize and analyze data tables.

Read a CSV

import pandas as pd
df = pd.read_csv("students.csv")
print(df.head()) 

Explore and Describe

print(df.columns)
print(df.shape)
print(df.describe())
print("Average Score:", df["Score"].mean()) 

Filter and Select

high_scores = df[df["Score"] > 80]
print(high_scores)

print(df[["Name", "Score"]]) 

Add and Edit Columns

df["Passed"] = df["Score"] > 50
df["Score"] = df["Score"] + 5 

Save Updated CSV

df.to_csv("updated_students.csv", index=False) 

Getting Started

import numpy as np

Creating Vectors and Matrices

# 1D Vector
v = np.array([1, 2, 3])
print("Vector v:", v)

# 2D Matrix
m = np.array([[1, 2], [3, 4]])
print("Matrix m:")
print(m)

Vector Operations

v = np.array([10, 20, 30])

print(v + 5)        # Add 5 to each element
print(v - 2)        # Subtract 2
print(v * 2)        # Multiply
print(v / 10)       # Divide
print(v ** 2)       # Power
print(np.sqrt(v))   # Square Root

Matrix Operations

A = np.array([[1, 2], [3, 4]])
B = np.array([[5, 6], [7, 8]])

# Element-wise operations
print(A + B)
print(A - B)
print(A * B)
print(A / B)

# Transpose
print("Transpose of A:")
print(A.T)

Matrix Multiplication (Dot Product)

A = np.array([[1, 2], [3, 4]])
B = np.array([[2, 0], [1, 2]])

# Dot Product
dot_result = np.dot(A, B)
print("Dot product of A and B:")
print(dot_result)

Norms and Magnitudes

v = np.array([3, 4])

# Magnitude (Euclidean norm)
print("||v|| =", np.linalg.norm(v))

Identity, Inverse, and Determinant

# Identity matrix
I = np.eye(3)
print("Identity Matrix:")
print(I)

# Determinant
D = np.array([[4, 7], [2, 6]])
print("Determinant:", np.linalg.det(D))

# Inverse
inv_D = np.linalg.inv(D)
print("Inverse of D:")
print(inv_D)

Solving Systems of Equations

# Solve Ax = b
A = np.array([[2, 1], [1, 3]])
b = np.array([8, 13])

x = np.linalg.solve(A, b)
print("Solution x:", x)

Extra Functions and Tips

arr = np.array([10, 20, 30, 40, 50])

print(np.mean(arr))     # Average
print(np.median(arr))   # Median
print(np.std(arr))      # Standard Deviation
print(np.max(arr))      # Max
print(np.min(arr))      # Min
print(np.sort(arr))     # Sort