Question 7 (Intermediate):
What does the @property decorator do in Python?

A) Converts a method into a read-only attribute
B) Marks a function as a class method
C) Enforces type checking on variables
D) Makes a method private

#Python #OOP #Decorators #Intermediate

✅ By: https://t.iss.one/DataScienceQ

#numpy #python #programming #question #array #intermediate

Write a Python program using NumPy to perform the following tasks:

1. Create a 1D array of integers from 1 to 10.
2. Reshape it into a 2D array of shape (2, 5).
3. Compute the sum of each row and store it in a new array.
4. Find the indices of elements greater than 7 in the original 1D array.
5. Print the resulting 2D array, the row sums, and the indices.

import numpy as np

# 1. Create a 1D array from 1 to 10
arr_1d = np.arange(1, 11)

# 2. Reshape into a 2D array of shape (2, 5)
arr_2d = arr_1d.reshape(2, 5)

# 3. Compute the sum of each row
row_sums = np.sum(arr_2d, axis=1)

# 4. Find indices of elements greater than 7 in the original 1D array
indices_greater_than_7 = np.where(arr_1d > 7)[0]

# 5. Print results
print("2D Array:\n", arr_2d)
print("Row sums:", row_sums)
print("Indices of elements > 7:", indices_greater_than_7)

Output:

2D Array:
 [[ 1  2  3  4  5]
 [ 6  7  8  9 10]]
Row sums: [15 40]
Indices of elements > 7: [7 8 9]

By: @DataScienceQ 🚀

#pandas #python #programming #question #dataframe #intermediate

Write a Python program using pandas to perform the following tasks:

1. Create a DataFrame from a dictionary with columns: 'Product', 'Category', 'Price', and 'Quantity' containing:
- Product: ['Laptop', 'Mouse', 'Keyboard', 'Monitor', 'Headphones']
- Category: ['Electronics', 'Accessories', 'Accessories', 'Electronics', 'Accessories']
- Price: [1200, 25, 80, 300, 100]
- Quantity: [10, 50, 30, 20, 40]

2. Add a new column 'Total_Value' that is the product of 'Price' and 'Quantity'.

3. Calculate the total value for each category and print it.

4. Find the product with the highest total value and print its details.

5. Filter the DataFrame to show only products in the 'Electronics' category with a price greater than 200.

import pandas as pd

# 1. Create the DataFrame
data = {
    'Product': ['Laptop', 'Mouse', 'Keyboard', 'Monitor', 'Headphones'],
    'Category': ['Electronics', 'Accessories', 'Accessories', 'Electronics', 'Accessories'],
    'Price': [1200, 25, 80, 300, 100],
    'Quantity': [10, 50, 30, 20, 40]
}
df = pd.DataFrame(data)

# 2. Add Total_Value column
df['Total_Value'] = df['Price'] * df['Quantity']

# 3. Calculate total value by category
total_by_category = df.groupby('Category')['Total_Value'].sum()

# 4. Find product with highest total value
highest_value_product = df.loc[df['Total_Value'].idxmax()]

# 5. Filter electronics with price > 200
electronics_high_price = df[(df['Category'] == 'Electronics') & (df['Price'] > 200)]

# Print results
print("Original DataFrame:")
print(df)
print("\nTotal Value by Category:")
print(total_by_category)
print("\nProduct with Highest Total Value:")
print(highest_value_product)
print("\nElectronics Products with Price > 200:")
print(electronics_high_price)

Output:

Original DataFrame:
      Product     Category  Price  Quantity  Total_Value
0     Laptop   Electronics   1200        10       12000
1      Mouse  Accessories     25        50       1250
2  Keyboard  Accessories     80        30       2400
3   Monitor   Electronics    300        20        6000
4  Headphones  Accessories    100        40       4000

Total Value by Category:
Category
Accessories    7650
Electronics    18000
dtype: int64

Product with Highest Total Value:
Product             Laptop
Category      Electronics
Price               1200
Quantity              10
Total_Value       12000
Name: 0, dtype: object

Electronics Products with Price > 200:
   Product     Category  Price  Quantity  Total_Value
0  Laptop  Electronics   1200        10       12000

By: @DataScienceQ 🚀

#opencv #python #programming #question #imageprocessing #intermediate

Write a Python program using OpenCV to perform the following tasks:

1. Load an image from a file named 'image.jpg' in grayscale mode.
2. Apply Gaussian blur with a kernel size of (5, 5).
3. Detect edges using Canny edge detection with thresholds of 100 and 200.
4. Find contours in the edge-detected image.
5. Draw all detected contours on the original blurred image in red color with thickness 2.
6. Save the resulting image as 'output_image.jpg'.

import cv2
import numpy as np

# 1. Load image in grayscale
img = cv2.imread('image.jpg', cv2.IMREAD_GRAYSCALE)
if img is None:
    raise FileNotFoundError("Image file not found")

# 2. Apply Gaussian blur
blurred = cv2.GaussianBlur(img, (5, 5), 0)

# 3. Apply Canny edge detection
edges = cv2.Canny(blurred, 100, 200)

# 4. Find contours
contours, _ = cv2.findContours(edges, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)

# 5. Create a copy of the blurred image to draw contours
result_img = cv2.cvtColor(blurred, cv2.COLOR_GRAY2BGR)  # Convert to BGR for color drawing
cv2.drawContours(result_img, contours, -1, (0, 0, 255), 2)  # Draw contours in red

# 6. Save the output image
cv2.imwrite('output_image.jpg', result_img)

print("Processing complete. Output saved as 'output_image.jpg'")

Note: This code assumes that 'image.jpg' exists in the working directory. The output will be a colored image with red contours drawn over the blurred grayscale image.

By: @DataScienceQ 🚀

#imageprocessing #python #programming #question #dataset #intermediate

Write a Python program to process a dataset of images stored in a folder named 'images'. Perform the following tasks:

1. Load all images from the 'images' folder and convert them to grayscale.
2. Resize each image to 100x100 pixels.
3. Calculate the average pixel value for each image.
4. Store the average values in a list.
5. Find the image with the highest average pixel value and print its filename.
6. Save the processed grayscale images to a new folder named 'processed_images'.

import os
import cv2
import numpy as np

# 1. Define paths
input_folder = 'images'
output_folder = 'processed_images'

# Create output folder if it doesn't exist
os.makedirs(output_folder, exist_ok=True)

# List to store average pixel values
avg_values = []

# 2. Process each image in the input folder
for filename in os.listdir(input_folder):
    if filename.lower().endswith(('.png', '.jpg', '.jpeg')):
        img_path = os.path.join(input_folder, filename)
        
        # Load image in grayscale
        img = cv2.imread(img_path, cv2.IMREAD_GRAYSCALE)
        
        # Resize to 100x100 pixels
        resized_img = cv2.resize(img, (100, 100))
        
        # Calculate average pixel value
        avg_value = np.mean(resized_img)
        avg_values.append((filename, avg_value))
        
        # Save processed image
        output_path = os.path.join(output_folder, filename)
        cv2.imwrite(output_path, resized_img)

# 3. Find image with highest average pixel value
max_avg_image = max(avg_values, key=lambda x: x[1])
print(f"Image with highest average pixel value: {max_avg_image[0]}")
print(f"Average value: {max_avg_image[1]:.2f}")

print("All images processed and saved to 'processed_images' folder.")

Note: This code assumes that the 'images' folder exists and contains valid image files. It processes all PNG, JPG, and JPEG files in the folder, resizes them, calculates their average pixel intensity, and saves the processed images to a new folder.

By: @DataScienceQ 🚀

#matplotlib #python #programming #question #visualization #intermediate

Write a Python program using matplotlib to perform the following tasks:

1. Generate two arrays: x from 0 to 10 with 100 points, and y = sin(x) + 0.5 * cos(2x).
2. Create a figure with two subplots arranged vertically.
3. In the first subplot, plot y vs x as a line graph with red color and marker 'o'.
4. In the second subplot, create a histogram of the y values with 20 bins.
5. Add titles, labels, and grid to both subplots.
6. Adjust the layout and save the figure as 'output_plot.png'.

import numpy as np
import matplotlib.pyplot as plt

# 1. Generate data
x = np.linspace(0, 10, 100)
y = np.sin(x) + 0.5 * np.cos(2 * x)

# 2. Create figure with two subplots
fig, (ax1, ax2) = plt.subplots(2, 1, figsize=(8, 10))

# 3. First subplot - line plot
ax1.plot(x, y, color='red', marker='o', linestyle='-', linewidth=2)
ax1.set_title('sin(x) + 0.5*cos(2x)')
ax1.set_xlabel('x')
ax1.set_ylabel('y')
ax1.grid(True)

# 4. Second subplot - histogram
ax2.hist(y, bins=20, color='blue', alpha=0.7)
ax2.set_title('Histogram of y values')
ax2.set_xlabel('y')
ax2.set_ylabel('Frequency')
ax2.grid(True)

# 5. Adjust layout
plt.tight_layout()

# 6. Save the figure
plt.savefig('output_plot.png')

print("Plot saved as 'output_plot.png'")

Note: This code generates a sine wave with an added cosine component, creates a line plot and histogram of the data in separate subplots, adds appropriate labels and grids, and saves the resulting visualization.

By: @DataScienceQ 🚀

#scipy #python #programming #question #scientificcomputing #intermediate

Write a Python program using SciPy to perform the following tasks:

1. Generate a random dataset of 1000 samples from a normal distribution with mean=5 and standard deviation=2.
2. Use SciPy's stats module to calculate the mean, median, standard deviation, and skewness of the dataset.
3. Perform a one-sample t-test to test if the sample mean is significantly different from 5 (null hypothesis).
4. Use SciPy's optimize module to find the minimum of the function f(x) = x^2 + 3x + 2.
5. Print all results including the test statistic, p-value, and the minimum point.

import numpy as np
from scipy import stats
from scipy.optimize import minimize_scalar

# 1. Generate random dataset
np.random.seed(42)
data = np.random.normal(loc=5, scale=2, size=1000)

# 2. Calculate descriptive statistics
mean = np.mean(data)
median = np.median(data)
std_dev = np.std(data)
skewness = stats.skew(data)

# 3. Perform one-sample t-test
t_stat, p_value = stats.ttest_1samp(data, popmean=5)

# 4. Find minimum of function f(x) = x^2 + 3x + 2
def objective_function(x):
    return x**2 + 3*x + 2

result = minimize_scalar(objective_function)

# 5. Print all results
print("Descriptive Statistics:")
print(f"Mean: {mean:.4f}")
print(f"Median: {median:.4f}")
print(f"Standard Deviation: {std_dev:.4f}")
print(f"Skewness: {skewness:.4f}")
print("\nOne-Sample T-Test:")
print(f"T-statistic: {t_stat:.4f}")
print(f"P-value: {p_value:.4f}")
print("\nOptimization Result:")
print(f"Minimum occurs at x = {result.x:.4f}")
print(f"Minimum value = {result.fun:.4f}")

Note: This code generates a normally distributed dataset, computes various statistical measures, performs a hypothesis test, and finds the minimum of a quadratic function using SciPy's optimization tools.

By: @DataScienceQ 🚀

#python #programming #question #fibonacci #intermediate #algorithm

Write a Python program that implements three different methods to generate the Fibonacci sequence up to the nth term:

1. Use an iterative approach with a loop.
2. Use recursion with memoization.
3. Use dynamic programming with a list.

For each method, calculate the 20th Fibonacci number and measure the execution time. Print the results for each method along with their respective times.

import time

def fibonacci_iterative(n):
    if n <= 1:
        return n
    a, b = 0, 1
    for i in range(2, n + 1):
        a, b = b, a + b
    return b

def fibonacci_recursive_memo(n, memo={}):
    if n in memo:
        return memo[n]
    if n <= 1:
        return n
    memo[n] = fibonacci_recursive_memo(n - 1, memo) + fibonacci_recursive_memo(n - 2, memo)
    return memo[n]

def fibonacci_dp(n):
    if n <= 1:
        return n
    dp = [0] * (n + 1)
    dp[1] = 1
    for i in range(2, n + 1):
        dp[i] = dp[i - 1] + dp[i - 2]
    return dp[n]

# Test all three methods for the 20th Fibonacci number
n = 20

# Method 1: Iterative
start_time = time.time()
result_iter = fibonacci_iterative(n)
iter_time = time.time() - start_time

# Method 2: Recursive with memoization
start_time = time.time()
result_rec = fibonacci_recursive_memo(n)
rec_time = time.time() - start_time

# Method 3: Dynamic Programming
start_time = time.time()
result_dp = fibonacci_dp(n)
dp_time = time.time() - start_time

print(f"20th Fibonacci number using iterative method: {result_iter} (Time: {iter_time:.6f} seconds)")
print(f"20th Fibonacci number using recursive method: {result_rec} (Time: {rec_time:.6f} seconds)")
print(f"20th Fibonacci number using DP method: {result_dp} (Time: {dp_time:.6f} seconds)")

By: @DataScienceQ 🚀

#python #programming #question #simulation #intermediate #matryoshka

Write a Python program to simulate a Matryoshka doll game with the following requirements:

1. Create a class Matryoshka that represents a nested doll with attributes: size (int), color (string), and contents (list of smaller Matryoshka objects).
2. Implement methods to:
- Add a smaller Matryoshka inside the current one
- Remove the smallest Matryoshka from the set
- Display all dolls in the nesting hierarchy
3. Create a main function that:
- Builds a nesting of 4 Matryoshka dolls (largest to smallest)
- Displays the complete nesting
- Removes the smallest doll
- Displays the updated nesting

class Matryoshka:
    def __init__(self, size, color):
        self.size = size
        self.color = color
        self.contents = []
    
    def add_doll(self, doll):
        if doll.size < self.size:
            self.contents.append(doll)
        else:
            print(f"Cannot add doll of size {doll.size} into size {self.size} doll")
    
    def remove_smallest(self):
        if not self.contents:
            print("No dolls to remove")
            return None
        
        # Find the smallest doll recursively
        smallest = self._find_smallest()
        if smallest:
            self._remove_doll(smallest)
            return smallest
        return None
    
    def _find_smallest(self):
        if not self.contents:
            return self
        smallest = self
        for doll in self.contents:
            result = doll._find_smallest()
            if result.size < smallest.size:
                smallest = result
        return smallest
    
    def _remove_doll(self, target):
        if self.contents:
            for i, doll in enumerate(self.contents):
                if doll == target:
                    self.contents.pop(i)
                    return
                elif doll._remove_doll(target):
                    return
    
    def display(self, level=0):
        indent = "  " * level
        print(f"{indent}{self.color} ({self.size})")
        for doll in self.contents:
            doll.display(level + 1)

def main():
    # Create nesting of 4 Matryoshka dolls (largest to smallest)
    large = Matryoshka(4, "Red")
    medium = Matryoshka(3, "Blue")
    small = Matryoshka(2, "Green")
    tiny = Matryoshka(1, "Yellow")
    
    # Build the nesting
    large.add_doll(medium)
    medium.add_doll(small)
    small.add_doll(tiny)
    
    # Display initial nesting
    print("Initial nesting:")
    large.display()
    print()
    
    # Remove the smallest doll
    removed = large.remove_smallest()
    if removed:
        print(f"Removed: {removed.color} ({removed.size})")
    
    # Display updated nesting
    print("\nUpdated nesting:")
    large.display()

if __name__ == "__main__":
    main()

Output:

Initial nesting:
  Red (4)
    Blue (3)
      Green (2)
        Yellow (1)

Removed: Yellow (1)

Updated nesting:
  Red (4)
    Blue (3)
      Green (2)

By: @DataScienceQ 🚀

#python #programming #question #simulation #intermediate #philosophers_dinner

Write a Python program to simulate the Dining Philosophers problem with the following requirements:

1. Create a class Philosopher that represents a philosopher with attributes: name (string), left_fork (int), right_fork (int), and eating (boolean).
2. Implement methods for:
- Attempting to pick up forks (with a delay)
- Eating
- Releasing forks
3. Use threading to simulate 5 philosophers sitting around a circular table.
4. Ensure no deadlock occurs by implementing a strategy where philosophers pick up forks in order (e.g., odd-numbered philosophers pick up left fork first, even-numbered pick up right fork first).
5. Display the state of each philosopher (thinking, eating, or waiting) at regular intervals.

import threading
import time
import random

class Philosopher:
    def __init__(self, name, left_fork, right_fork):
        self.name = name
        self.left_fork = left_fork
        self.right_fork = right_fork
        self.eating = False
    
    def eat(self):
        print(f"{self.name} is trying to eat...")
        # Pick up left fork
        self.left_fork.acquire()
        print(f"{self.name} picked up left fork")
        
        # Pick up right fork
        self.right_fork.acquire()
        print(f"{self.name} picked up right fork")
        
        # Eat
        self.eating = True
        print(f"{self.name} is eating")
        time.sleep(random.uniform(1, 3))
        
        # Release forks
        self.right_fork.release()
        self.left_fork.release()
        print(f"{self.name} finished eating")
        self.eating = False

def philosopher_thread(philosopher, num_philosophers):
    while True:
        # Think
        print(f"{philosopher.name} is thinking")
        time.sleep(random.uniform(1, 3))
        
        # Try to eat
        philosopher.eat()

# Main simulation
if __name__ == "__main__":
    # Create 5 forks (semaphores)
    forks = [threading.Semaphore(1) for _ in range(5)]
    
    # Create 5 philosophers
    philosophers = [
        Philosopher("Philosopher 1", forks[0], forks[1]),
        Philosopher("Philosopher 2", forks[1], forks[2]),
        Philosopher("Philosopher 3", forks[2], forks[3]),
        Philosopher("Philosopher 4", forks[3], forks[4]),
        Philosopher("Philosopher 5", forks[4], forks[0])
    ]
    
    # Start threads for each philosopher
    threads = []
    for i, philosopher in enumerate(philosophers):
        thread = threading.Thread(target=philosopher_thread, args=(philosopher, len(philosophers)))
        threads.append(thread)
        thread.start()
    
    # Wait for all threads to complete (infinite loop)
    try:
        while True:
            time.sleep(1)
    except KeyboardInterrupt:
        print("Simulation ended")

By: @DataScienceQ 🚀