🚀 Advanced Python

import math

class Circle:
    """A class to represent a circle"""
    
    def __init__(self, radius=1):
        """Initialize circle with radius (default 1)"""
        self.radius = radius
    
    def area(self):
        """Calculate and return area of circle"""
        return math.pi * self.radius ** 2
    
    def circumference(self):
        """Calculate and return circumference"""
        return 2 * math.pi * self.radius
    
    def __repr__(self):
        """String representation of Circle"""
        return f"Circle(radius={self.radius})"

# Test the Circle class
c1 = Circle()           # Default radius
c2 = Circle(5)          # Radius of 5
c3 = Circle(radius=10)  # Named argument

print("Circle 1:", c1)
print(f"  Area: {c1.area():.2f}")
print(f"  Circumference: {c1.circumference():.2f}")

print("\nCircle 2:", c2)
print(f"  Area: {c2.area():.2f}")
print(f"  Circumference: {c2.circumference():.2f}")

print("\nCircle 3:", c3)
print(f"  Area: {c3.area():.2f}")
print(f"  Circumference: {c3.circumference():.2f}")

import numpy as np

# Set seed for reproducibility
np.random.seed(42)

# a) Generate 10 random numbers between 1 and 100
random_nums = np.random.randint(1, 101, size=10)
print("a) Random numbers (1-100):")
print(random_nums)

# b) Create and operate on 2x2 matrices
print("\nb) Matrix Operations:")
matrix1 = np.array([[1, 2], [3, 4]])
matrix2 = np.array([[5, 6], [7, 8]])

print("Matrix 1:")
print(matrix1)
print("\nMatrix 2:")
print(matrix2)

# Addition
print("\nMatrix Addition (A + B):")
print(matrix1 + matrix2)

# Element-wise multiplication
print("\nElement-wise Multiplication:")
print(matrix1 * matrix2)

# Matrix multiplication (dot product)
print("\nMatrix Multiplication (A @ B):")
print(matrix1 @ matrix2)

# c) Statistical operations
print("\nc) Statistics of random numbers:")
print(f"Mean: {np.mean(random_nums):.2f}")
print(f"Median: {np.median(random_nums):.2f}")
print(f"Std Dev: {np.std(random_nums):.2f}")
print(f"Min: {np.min(random_nums)}")
print(f"Max: {np.max(random_nums)}")
print(f"Sum: {np.sum(random_nums)}")

import pandas as pd

# Create sample data (simulating CSV read)
data = {
    'Product': ['Laptop', 'Phone', 'Tablet', 'Watch', 'Headphones'],
    'Price': [999.99, 699.99, 449.99, 299.99, 149.99],
    'Quantity': [10, 25, 15, 30, 50]
}
df = pd.DataFrame(data)

# a) Display first 5 rows
print("a) First 5 rows (head):")
print(df.head())

# b) Price statistics
print("\nb) Price Statistics:")
print(f"Maximum Price: ${df['Price'].max():.2f}")
print(f"Minimum Price: ${df['Price'].min():.2f}")
print(f"Average Price: ${df['Price'].mean():.2f}")

# c) Create new column with lambda
print("\nc) Add 'Total Value' column:")
df['Total_Value'] = df.apply(lambda row: row['Price'] * row['Quantity'], axis=1)
print(df)

# Alternative: Vectorized approach (faster)
df['Discounted'] = df['Price'].apply(lambda x: x * 0.9)
print("\nWith 10% discount column:")
print(df)

# d) Sort by price descending
print("\nd) Sorted by Price (descending):")
sorted_df = df.sort_values('Price', ascending=False)
print(sorted_df)

import pandas as pd
import numpy as np

# Create sample sales data
sales = pd.DataFrame({
    'Product': ['Laptop', 'Phone', 'Tablet', 'Laptop', 'Phone', 'Watch'],
    'Category': ['Electronics', 'Electronics', 'Electronics', 'Electronics', 'Electronics', 'Accessories'],
    'Price': [999, 699, 449, 1099, 599, np.nan],
    'Units': [5, 10, 8, 3, 15, 20]
})

# a) Filter: Products with Price > 500
print("a) Products with Price > 500:")
filtered = sales[sales['Price'] > 500]
print(filtered)

# Multiple conditions
print("\nProducts: Price > 500 AND Units > 5:")
multi_filter = sales[(sales['Price'] > 500) & (sales['Units'] > 5)]
print(multi_filter)

# b) Group by and aggregate
print("\nb) Group by Product - Statistics:")
grouped = sales.groupby('Product').agg({
    'Price': ['mean', 'min', 'max'],
    'Units': 'sum'
})
print(grouped)

# Simpler groupby
print("\nTotal units per category:")
print(sales.groupby('Category')['Units'].sum())

# c) Handle missing values
print("\nc) Handling Missing Values:")
print("Missing values per column:")
print(sales.isnull().sum())

# Fill missing values
sales_filled = sales.fillna(sales['Price'].mean())
print("\nAfter filling with mean:")
print(sales_filled)

# Alternative: Drop rows with NaN
sales_dropped = sales.dropna()
print(f"\nRows after dropping NaN: {len(sales_dropped)}")

# d) Merge DataFrames
products = pd.DataFrame({
    'Product': ['Laptop', 'Phone', 'Tablet', 'Watch'],
    'Manufacturer': ['Dell', 'Apple', 'Samsung', 'Fitbit']
})

print("\nd) Merge with manufacturer info:")
merged = sales.merge(products, on='Product', how='left')
print(merged)