Python Coding Challange - Question With Answer(01180225)

 

Step-by-step Execution:

1. Define a Set:

   
   set1 = {10, 20, 30}

   • set1 is a set containing {10, 20, 30}.

2. Remove an Element:

   set2 = set1.remove(20)

   • The .remove(20) method removes the element 20 from set1.
   • However, remove() does not return anything; it modifies set1 in place.
   • This means set2 gets assigned the return value of set1.remove(20), which is None.

3. Print the Result:

   
   print(set2)

   • Since set2 = None, the output will be:

     
     None

---

Final Output:

None

Key Learning:

• .remove() modifies the original set but returns None.
• If you assign set2 = set1.remove(x), set2 will always be None.

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25 Insanely Useful Python Code Snippets For Everyday Problems

 

📝 1. Swap Two Variables Without a Temp Variable

a, b = 5, 10
a, b = b, a
print(a, b)

Output:

10 5

---

📏 2. Check if a String is a Palindrome

def is_palindrome(s):
    return s == s[::-1]
print(is_palindrome("madam"))

Output:

True

---

🔢 3. Find the Factorial of a Number

from math import factorial
print(factorial(5))

Output:

120

---

🎲 4. Generate a Random Password

import secrets, string

def random_password(length=10):
    chars = string.ascii_letters + string.digits + string.punctuation
    return ''.join(secrets.choice(chars) for _ in range(length))
print(random_password())

Output:

e.g., "A9$uT1#xQ%"

---

🔄 5. Flatten a Nested List

def flatten(lst):
    return [i for sublist in lst for i in sublist]
print(flatten([[1, 2], [3, 4]]))

Output:

[1, 2, 3, 4]

---

🎭 6. Check if Two Strings are Anagrams

from collections import Counter

def is_anagram(s1, s2): 
    return Counter(s1) == Counter(s2)

print(is_anagram("listen", "silent"))

Output:

True

---

🛠️ 7. Merge Two Dictionaries

d1, d2 = {'a': 1}, {'b': 2}
merged = {**d1, **d2}
print(merged)

Output:

{'a': 1, 'b': 2}

---

📅 8. Get the Current Date and Time

from datetime import datetime
print(datetime.now())

Output:

2025-02-18 14:30:45.123456

---

🏃 9. Find Execution Time of a Function

import time

start = time.time()
time.sleep(1)
print(time.time() - start)

Output:

1.001234 (approx.)

---

📂 10. Get the Size of a File

import os
print(os.path.getsize("example.txt"))

Output:

e.g., 1024 (size in bytes)

---

🔎 11. Find the Most Frequent Element in a List

from collections import Counter

def most_frequent(lst):
    return Counter(lst).most_common(1)[0][0]

print(most_frequent([1, 2, 3, 1, 2, 1]))

Output:

1

---

🔢 12. Generate Fibonacci Sequence

def fibonacci(n):
    a, b = 0, 1
    for _ in range(n):
        yield a
        a, b = b, a + b 

print(list(fibonacci(10)))

Output:

[0, 1, 1, 2, 3, 5, 8, 13, 21, 34]

---

🔄 13. Reverse a List in One Line

lst = [1, 2, 3, 4]
print(lst[::-1])

Output:

[4, 3, 2, 1]

---

🔍 14. Find Unique Elements in a List

print(list(set([1, 2, 2, 3, 4, 4])))

Output:

[1, 2, 3, 4]

---

🎯 15. Check if a Number is Prime

def is_prime(n):
    return n > 1 and all(n % i for i in range(2, int(n**0.5) + 1))

print(is_prime(7))

Output:

True

---

📜 16. Read a File in One Line

print(open("example.txt").read())

Output:

Contents of the file

---

📝 17. Count Words in a String

def word_count(s):
    return len(s.split())

print(word_count("Hello world!"))

Output:

2

---

🔄 18. Convert a List to a Comma-Separated String

lst = ["apple", "banana", "cherry"]
print(", ".join(lst))

Output:

apple, banana, cherry

---

🔀 19. Shuffle a List Randomly

import random
lst = [1, 2, 3, 4]
random.shuffle(lst)
print(lst)

Output:

e.g., [3, 1, 4, 2]

---

🔢 20. Convert a List of Strings to Integers

lst = ["1", "2", "3"]
print(list(map(int, lst)))

Output:

[1, 2, 3]

---

🔗 21. Get the Extension of a File

print("example.txt".split(".")[-1])

Output:

txt

---

📧 22. Validate an Email Address

import re

def is_valid_email(email):
    return bool(re.match(r"[^@]+@[^@]+\.[^@]+", email))

print(is_valid_email("test@example.com"))

Output:

True

---

📏 23. Find the Length of the Longest Word in a Sentence

def longest_word_length(s):
    return max(map(len, s.split()))

print(longest_word_length("Python is awesome"))

Output:

7

---

🔠 24. Capitalize the First Letter of Each Word

print("hello world".title())

Output:

Hello World

---

🖥️ 25. Get the CPU Usage Percentage

import psutil
print(psutil.cpu_percent(interval=1))

Output:

e.g., 23.4

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Python Coding Challange - Question With Answer(01170225)

 

Step-by-Step Execution:

First Iteration (a = 0)

• b = 0 → d[0] = 0
• b = 1 → d[0] = 1 (overwrites previous value)
• b = 2 → d[0] = 2 (overwrites previous value)
• b = 3 → d[0] = 3 (overwrites previous value)
• b = 4 → d[0] = 4 (overwrites previous value)

Second Iteration (a = 1)

• b = 0 → d[1] = 0
  
• b = 1 → d[1] = 1 (overwrites previous value)
• b = 2 → d[1] = 2 (overwrites previous value)
• b = 3 → d[1] = 3 (overwrites previous value)
• b = 4 → d[1] = 4 (overwrites previous value)

---

Final Dictionary Output

After the loops complete, we have:

{0: 4, 1: 4}

Why?

• Each value of b replaces the previous one for the current a.
• The final assigned value for both 0 and 1 is 4, since it's the last value of b in the inner loop.

Summary

• The dictionary keys are 0 and 1 (values of a).
• Each key gets assigned the final value of b, which is 4.
• The dictionary ends up as {0: 4, 1: 4}.

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Python Coding Challange - Question With Answer(01160225)

 

Explanation of the Code

s = 'robot'  # Assign the string 'robot' to variable s

a, b, c, d, e = s # Unpacking the string into individual characters
# a = 'r', b = 'o', c = 'b', d = 'o', e = 't'

b = d = '*' # Assign '*' to both b and d

s = (a, b, c, d, e) # Create a tuple with the modified values 
print(s) # Output the tuple

Step-by-Step Execution

Variable	Value Before Modification	Value After Modification
a	'r'	'r'
b	'o'	'*'
c	'b'	'b'
d	'o'	'*'
e	't'	't'

Final Tuple Created

('r', '*', 'b', '*', 't')

Final Output

('r', '*', 'b', '*', 't')

Key Concepts Used:

1. String Unpacking – The string 'robot' is unpacked into five variables (a, b, c, d, e).
2. Variable Assignment – b and d are assigned '*'.
3. Tuple Construction – A tuple (a, b, c, d, e) is created and printed.

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5 Cool Jupyter Notebook Tips to Boost Your Productivity

 

Jupyter Notebook is a powerful tool for data scientists, developers, and researchers. It allows for an interactive coding experience, making it easier to write, debug, and visualize code. Whether you’re a beginner or an experienced user, these five cool tips will help you get the most out of Jupyter Notebook.

1. Run Shell Commands Inside Jupyter

You can run shell commands directly in Jupyter by prefixing them with !. This is useful for tasks like checking files, installing packages, or running system commands.

!ls  # Lists files in the current directory (Linux/macOS)
!dir # Lists files in Windows

2. Use Magics for Enhanced Functionality

Jupyter supports magic commands that make development easier. There are two types:

• Line magics (%) - Work on a single line.
• Cell magics (%%) - Apply to the entire cell.

Examples:

%timeit sum(range(1000))  # Measures execution time

%%writefile script.py
print("This code is saved in script.py") # Writes code to a file

3. Display Plots Inline with %matplotlib inline

If you're working with Matplotlib, use this magic command to display plots inside the notebook:

%matplotlib inline
import matplotlib.pyplot as plt
plt.plot([1, 2, 3], [4, 5, 6])
plt.show()

4. Autocomplete and Docstrings with Shift + Tab

Want to see function details without searching online? Press Shift + Tab inside parentheses to view its documentation.

Example:

len(  # Press Shift + Tab inside the parentheses

5. Convert Notebook to Other Formats

You can export Jupyter notebooks as HTML, PDF, or Python scripts using:

!jupyter nbconvert --to html notebook.ipynb  # Convert to HTML
!jupyter nbconvert --to script notebook.ipynb # Convert to Python script

Conclusion

Jupyter Notebook is more than just a coding environment; it’s a productivity powerhouse. With these tips, you can work more efficiently and make the most of your Jupyter experience!

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Equilateral Triangle Pattern Plot using Python

import matplotlib.pyplot as plt

rows = 5

plt.figure(figsize=(6, 6))

for i in range(rows):

    for j in range(i + 1):

        x = j - i / 2  

        y = -i

        plt.scatter(x, y, s=500, c='blue')

plt.xlim(-rows / 2, rows / 2)

plt.ylim(-rows, 1)

plt.axis('off')

plt.gca().set_aspect('equal', adjustable='datalim')

plt.title("Equilateral Triangle Pattern Plot", fontsize=14)

plt.show()

#source code --> clcoding.com 

Code Explanation:

1. Importing Required Libraries

import matplotlib.pyplot as plt

import numpy as np

matplotlib.pyplot → Used for plotting the pattern.

numpy → Imported but not used in this code. It can be removed.

2. Defining the Number of Rows

rows = 5

Defines the height of the equilateral triangle as 5 rows.

3. Creating the Figure

plt.figure(figsize=(6, 6))

Creates a figure of size 6x6 inches to display the pattern properly.

4. Generating the Equilateral Triangle Pattern Using Nested Loops

for i in range(rows):  # Loops through each row (i)

    for j in range(i + 1):  # Increasing dots in each row

        x = j - i / 2  # Centering the triangle

        y = -i  # Y-coordinates for arranging rows from top to bottom

        plt.scatter(x, y, s=500, c='blue')  # Plots a blue dot

Outer loop (i) → Controls the number of rows.

Inner loop (j) → Controls the number of dots per row:

Row 0 → 1 dot

Row 1 → 2 dots

Row 2 → 3 dots

Row 3 → 4 dots

Row 4 → 5 dots

Plotting the dots:

plt.scatter(x, y, s=500, c='blue')

s=500 → Sets dot size.

c='blue' → Sets dot color to blue.

5. Adjusting Plot Limits

plt.xlim(-rows / 2, rows / 2)  

plt.ylim(-rows, 1)  

plt.xlim(-rows / 2, rows / 2) → Centers the triangle horizontally.

plt.ylim(-rows, 1) → Adjusts the vertical range to fit the triangle.

6. Formatting the Plot

plt.axis('off')  

plt.gca().set_aspect('equal', adjustable='datalim')  

plt.title("Equilateral Triangle Pattern Plot", fontsize=14)  

plt.axis('off') → Hides the axes for a cleaner look.

plt.gca().set_aspect('equal', adjustable='datalim') → Ensures that dot spacing remains uniform.

plt.title("Equilateral Triangle Pattern Plot", fontsize=14) → Adds a title.

7. Displaying the Final Output

plt.show()

Displays the equilateral triangle pattern.

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Inverted Right-Angled Triangle Pattern using Python

 

import matplotlib.pyplot as plt

rows = 5

plt.figure(figsize=(6, 6))

for i in range(rows):

    for j in range(rows - i):

        plt.scatter(j, -i, s=800, c='red')

plt.xlim(-0.5, rows - 0.5)

plt.ylim(-rows + 0.5, 0.5)

plt.axis('off')

plt.gca().set_aspect('equal', adjustable='datalim')

plt.title("Inverted Right-Angled Triangle Pattern Plot",fontsize=14)

plt.show()

#source code --> clcoding.com 

Code Explanation:

1. Importing Matplotlib

import matplotlib.pyplot as plt

This imports pyplot from Matplotlib, which is used for plotting.

2. Defining the Number of Rows

rows = 5

This variable sets the number of rows to 5, meaning the triangle will have 5 levels.

3. Creating the Figure

plt.figure(figsize=(6, 6))

Creates a 6x6 inch figure for proper visualization.

4. Generating the Inverted Triangle Pattern Using Nested Loops

for i in range(rows):  # Loops through each row (i)

    for j in range(rows - i):  # Decreasing number of dots in each row

        plt.scatter(j, -i, s=800, c='red')  # Red dots

The outer loop (i) iterates over the number of rows.

The inner loop (j) ensures that the number of dots decreases in each row:

Row 0 → 5 dots

Row 1 → 4 dots

Row 2 → 3 dots

Row 3 → 2 dots

Row 4 → 1 dot

plt.scatter(j, -i, s=800, c='red') places a red dot at (j, -i):

j → Represents the horizontal (x-axis) position.

-i → Represents the vertical (y-axis) position (negative value makes it start from the top).

s=800 → Sets the dot size to 800 for better visibility.

c='red' → Sets the dot color to red.

5. Setting the Axis Limits

plt.xlim(-0.5, rows - 0.5)  # X-axis range

plt.ylim(-rows + 0.5, 0.5)  # Y-axis range

Ensures the plot area is properly adjusted to fit the triangle.

6. Removing the Axes & Formatting the Plot

plt.axis('off')  # Hides the axes

plt.gca().set_aspect('equal', adjustable='datalim')  # Ensures equal spacing

plt.title("Inverted Right-Angled Triangle Pattern Plot", fontsize=14)  # Adds title

plt.axis('off') → Removes the x and y axes for a cleaner look.

plt.gca().set_aspect('equal', adjustable='datalim') → Ensures that dot spacing remains uniform.

plt.title("Inverted Right-Angled Triangle Pattern Plot", fontsize=14) → Adds a title with font size 14.

7. Displaying the Final Output

plt.show()

Renders and displays the triangle pattern.

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Pyramid Pattern Plot using Python

 import matplotlib.pyplot as plt

rows = 5

plt.figure(figsize=(6, 6))

for i in range(rows):

    for j in range(2 * i + 1):  

        x = j - i  

        y = -i

        plt.scatter(x, y, s=500, c='gold')  

plt.xlim(-rows, rows)

plt.ylim(-rows, 1)

plt.axis('off')

plt.gca().set_aspect('equal', adjustable='datalim')

plt.title("Pyramid Pattern Plot", fontsize=14)

plt.show()

#source code --> clcoding.com 

Code Explanation:

1. Import Required Library

import matplotlib.pyplot as plt

matplotlib.pyplot is used for plotting the pattern.

2. Define the Number of Rows

rows = 5

This sets the height of the pyramid (i.e., the number of rows).

3. Create the Figure

plt.figure(figsize=(6, 6))

Creates a 6x6 inch figure to display the plot properly.

4. Generate the Pyramid Pattern Using Nested Loops

for i in range(rows):  # Loop through each row

    for j in range(2 * i + 1):  # Increasing dots to form a pyramid

        x = j - i  # Centers the pyramid

        y = -i  # Moves rows downward

        plt.scatter(x, y, s=500, c='gold')  # Plot gold-colored dots

Outer loop (i) → Controls the number of rows.

Inner loop (j) → Controls the number of dots per row:

Row 0 → 1 dot

Row 1 → 3 dots

Row 2 → 5 dots

Row 3 → 7 dots

Row 4 → 9 dots

The pattern follows the formula:

Dots in row 

Dots in row i=2i+1

Plotting the dots:

plt.scatter(x, y, s=500, c='gold')

s=500 → Sets the dot size.

c='gold' → Colors the dots gold.

5. Adjust the Axis Limits

plt.xlim(-rows, rows)

plt.ylim(-rows, 1)

X-axis limit: (-rows, rows) → Ensures the pyramid is centered.

Y-axis limit: (-rows, 1) → Ensures the pyramid is properly positioned.

6. Format the Plot

plt.axis('off')  # Hides the x and y axes

plt.gca().set_aspect('equal', adjustable='datalim')  

plt.title("Pyramid Pattern Plot", fontsize=14)  

Removes the axis for a clean look.

Ensures equal spacing for uniform dot distribution.

Adds a title to the plot.

7. Display the Final Output

plt.show()

Displays the pyramid pattern.

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Python Quiz on Pip

 

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Python Coding challenge - Day 381| What is the output of the following Python Code?

Code Explanation:

Importing Required Modules

from abc import ABC, abstractmethod  

ABC (Abstract Base Class) is imported from the abc module.

@abstractmethod is used to define an abstract method.

Defining an Abstract Class (P)

class P(ABC):  

    @abstractmethod  

    def calc(self, x):  

        pass  

P is an abstract class because it inherits from ABC.

calc(self, x) is an abstract method (it has no implementation).

Any subclass of P must override calc() before being instantiated.

Creating a Subclass (Q)

class Q(P):  

    def calc(self, x):  

        return x * 2  

Q inherits from P and provides an implementation for calc().

Since calc() is now implemented, Q is no longer abstract and can be instantiated.

Instantiating Q and Calling calc()

print(Q().calc(5))  

An object of Q is created: Q().

The calc(5) method is called, which returns 5 * 2 = 10.

Final Output

10

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Python Coding challenge - Day 380| What is the output of the following Python Code?

 

Code Explanation:

Importing Required Modules

from abc import ABC, abstractmethod  

ABC (Abstract Base Class) is imported from the abc module.

@abstractmethod is used to define an abstract method.

Defining an Abstract Class (A)

class A(ABC):  

    @abstractmethod  

    def show(self):  

        pass  

A is an abstract class because it inherits from ABC.

show(self) is an abstract method, meaning any subclass must override it before being instantiated.

Since show() has no implementation (pass), A cannot be instantiated directly.

Creating a Subclass (B)

class B(A):  

    pass  

B is a subclass of A, but it does not implement the show() method.

Because show() is still missing, B remains an abstract class.

Python does not allow instantiating an abstract class, so obj = B() causes an error.

Attempting to Instantiate B

obj = B()  

Final Output:

Implement show() in B

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Python Coding challenge - Day 379| What is the output of the following Python Code?

 

Code Explanation:

Importing Required Modules

from abc import ABC, abstractmethod  

ABC (Abstract Base Class) is imported from the abc module.

@abstractmethod is used to define an abstract method.

Defining an Abstract Class (X)

class X(ABC):  

    @abstractmethod  

    def display(self):  

        pass  

X is an abstract class because it inherits from ABC.

display(self) is marked as an abstract method using @abstractmethod.

Since display() has no implementation (pass), any subclass of X must override it to be instantiated.

Creating a Subclass (Y)

class Y(X):  

    pass  

Y is a subclass of X, but it does not implement the display() method.

Because display() is still missing, Y remains abstract and cannot be instantiated.

Attempting to Instantiate Y

obj = Y()  

Since Y does not provide an implementation for display(), it remains an abstract class.

Python does not allow instantiating an abstract class, so this line raises a TypeError.

Final Output:

TypeError

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Python Coding challenge - Day 378| What is the output of the following Python Code?

Code Explanation:

Importing ABC and abstractmethod

from abc import ABC, abstractmethod  

ABC (Abstract Base Class) is imported from the abc module.

@abstractmethod is used to define an abstract method.

Defining an Abstract Base Class (A)

class A(ABC):  

    @abstractmethod  

    def show(self):  

        pass  

A is an abstract class because it inherits from ABC.

show(self) is defined as an abstract method using @abstractmethod.

Since show() has no implementation (pass), any subclass of A must override it before it can be instantiated.

Creating a Concrete Subclass (B)

class B(A):  

    def show(self):  

        print("Hello")  

B is a subclass of A that implements the show() method.

Since B provides an implementation for show(), it can be instantiated.

Creating an Object and Calling show()

B().show()

B() creates an instance of class B.

.show() calls the show() method in B, which prints "Hello".

Since show() only prints but does not return anything, there is no None printed.

Final Output

Hello

 

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Matplotlib Cheat Sheet With 50 Different Plots

 

Master data visualization with Matplotlib using this ultimate cheat sheet! This PDF book provides 50 different plot types, covering everything from basic line charts to advanced visualizations.

What’s Inside?

✅ 50 ready-to-use Matplotlib plots
✅ Clear and concise code snippets
✅ Easy-to-follow formatting for quick reference
✅ Covers bar charts, scatter plots, histograms, 3D plots, and more
✅ Perfect for beginners & advanced users

Whether you’re a data scientist, analyst, or Python enthusiast, this book will save you time and boost your visualization skills. Get your copy now and start creating stunning plots with ease!

Download : https://pythonclcoding.gumroad.com/l/xnbqr

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Python Coding Challange - Question With Answer(01140225)

 

Explanation:

1. Creating an Empty Dictionary:

   data = {}

   • We initialize an empty dictionary named data.

2. Adding Key-Value Pairs to the Dictionary:

   
   data[(2, 3)] = 5
   data[(3, 2)] = 10
   data[(1,)] = 15

   • (2, 3) → 5: A tuple (2, 3) is used as a key, storing the value 5.
   • (3, 2) → 10: A different tuple (3, 2) is used as a key, storing the value 10.
     (Note: (2, 3) and (3, 2) are different keys because tuples are order-sensitive.)
   • (1,) → 15: Another tuple (1,) is used as a key, storing the value 15.

3. Summing All Values in the Dictionary:

   
   result = 0
   for key in data: 
       result += data[key]

   • We initialize result = 0.
   • We iterate over the dictionary and add each value to result:
     • data[(2, 3)] = 5 → result = 0 + 5 = 5
     • data[(3, 2)] = 10 → result = 5 + 10 = 15
     • data[(1,)] = 15 → result = 15 + 15 = 30
   • Final result = 30.

4. Printing the Output:

   
   print(len(data) + result)

   • len(data) gives the number of unique keys in the dictionary.
     • There are 3 unique keys: (2,3), (3,2), and (1,).
   • The final calculation is:

     
     3 + 30 = 33

   • The program prints 33.

---

Final Output:

33

---

Key Takeaways:

1. Tuples as Dictionary Keys:

   • Tuples are immutable and can be used as dictionary keys.
   • (2, 3) and (3, 2) are different keys because tuple ordering matters.

2. Dictionary Iteration:

   • Iterating over a dictionary gives the keys, which we use to access values.

3. Using len(data):

   • The length of the dictionary is based on unique keys.

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