Rectangle Pattern Plot using Python

import matplotlib.pyplot as plt

rows,cols=4,6

plt.figure(figsize=(6,4))

for i in range(rows):

    for j in range(cols):

        plt.scatter(j,-i,s=500,c='blue')

plt.xlim(-0.5,cols-0.5)

plt.ylim(-rows+0.5,0.5)

plt.axis('off')

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

plt.title('Reactangular pattern plot',fontsize=14)

plt.show()

#source code --> clcoding.com 

Code Explanation:

Importing Matplotlib:

import matplotlib.pyplot as plt

This imports Matplotlib's pyplot module, which is used for plotting.

Defining the grid size:

rows, cols = 4, 6

The pattern consists of 4 rows and 6 columns, forming a 4x6 rectangular grid.

Creating a figure:

plt.figure(figsize=(6, 4))

This creates a figure with a 6x4 inch size.

Generating the pattern using nested loops:

for i in range(rows):

    for j in range(cols):

        plt.scatter(j, -i, s=500, c='blue')

The outer loop (i) iterates over the rows.

The inner loop (j) iterates over the columns.

plt.scatter(j, -i, s=500, c='blue') places a blue dot at each (j, -i) coordinate:

j represents the x-coordinate (column index).

-i represents the y-coordinate (negative row index, keeping the origin at the top-left).

s=500 sets the dot size.

c='blue' sets the color to blue.

Setting the plot limits:

plt.xlim(-0.5, cols - 0.5)

plt.ylim(-rows + 0.5, 0.5)

plt.xlim(-0.5, cols - 0.5) ensures that the x-axis starts slightly before 0 and extends to cols - 1.

plt.ylim(-rows + 0.5, 0.5) adjusts the y-axis to properly contain all points.

Hiding the axis and adjusting the aspect ratio:

plt.axis('off')

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

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

plt.gca().set_aspect('equal', adjustable='datalim') ensures that the spacing between points remains uniform.

Adding a title:

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

Sets the title of the plot to "Rectangle Pattern Plot" with a font size of 14.

Displaying the plot:

plt.show()

Renders and displays the pattern.

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Inverted Pyramid Pattern plot using python

 import matplotlib.pyplot as plt

rows = 5

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

for i in range(rows, 0, -1):

    for j in range(rows - i, rows + i - 1):

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

plt.xlim(-0.5, 2 * rows - 1.5)

plt.ylim(-rows + 0.5, 0.5)

plt.axis('off')

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

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

plt.show()

#source code --> clcoding.com 

Code explanation:

1. Importing Matplotlib

import matplotlib.pyplot as plt

This imports matplotlib.pyplot, which allows us to create scatter plots.

2. Defining the Number of Rows

rows = 5

rows = 5: Controls the height of the inverted pyramid.

3. Creating the Figure

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

Initializes a 6x6 inches plot.

Generating the Inverted Pyramid Shape

4. Looping to Generate the Pattern

for i in range(rows, 0, -1):  # Loop from rows down to 1

    for j in range(rows - i, rows + i - 1):  # Controls the number of dots per row

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

The outer loop (i) runs in reverse from rows down to 1, ensuring that the top row is widest and the bottom row is smallest.

The inner loop (j) determines how many dots are printed per row:

rows - i: Ensures proper horizontal spacing.

rows + i - 1: Expands outward as i decreases.

plt.scatter(j, -(rows - i), s=800, c='red') places red dots at calculated positions.

s=800: Controls the size of the dots.

-(rows - i): Ensures correct vertical placement.

Adjusting the Plot Appearance

5. Setting Axis Limits

plt.xlim(-0.5, 2 * rows - 1.5)

plt.ylim(-rows + 0.5, 0.5)

plt.xlim(-0.5, 2 * rows - 1.5): Ensures centered horizontal alignment.

plt.ylim(-rows + 0.5, 0.5): Ensures the entire inverted pyramid is visible.

6. Removing Axes and Adjusting Aspect Ratio

plt.axis('off')

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

plt.axis('off'): Hides grid lines and axis labels for a clean display.

plt.gca().set_aspect('equal', adjustable='datalim'): Maintains equal spacing between dots.

7. Adding a Title

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

Displays the title of the plot.

8. Displaying the Pattern

plt.show()

Renders and displays the inverted pyramid pattern.

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Hollow Rectangle Pattern Plot using python

 

import matplotlib.pyplot as plt

def plot_hollow_rectangle(width, height):

    plt.figure(figsize=(6, 6))

    for x in range(width):

        plt.scatter(x, 0, s=200, c='black')

        plt.scatter(x, height-1, s=200, c='black')

        for y in range(height):

        plt.scatter(0, y, s=200, c='black')

        plt.scatter(width-1, y, s=200, c='black')

     plt.xlim(-1, width)

    plt.ylim(-1, height)

    plt.axis('off')

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

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

    plt.show()

plot_hollow_rectangle(10, 6)

#source code --> clcoding.com 

Code Explanation:

1. Importing Required Library

import matplotlib.pyplot as plt

This imports matplotlib.pyplot, a popular library for creating plots and visualizations.

2. Defining the Function

def plot_hollow_rectangle(width, height):

This function takes two parameters: width (number of columns) and height (number of rows) to define the size of the rectangle.

3. Creating the Figure

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

This initializes a new figure with a fixed size of 6x6 inches for consistent visualization.

4. Plotting the Top and Bottom Borders

for x in range(width):

    plt.scatter(x, 0, s=200, c='black')

    plt.scatter(x, height-1, s=200, c='black')

The loop iterates through all x positions from 0 to width-1:

plt.scatter(x, 0, s=200, c='black') → Plots points at the bottom of the rectangle.

plt.scatter(x, height-1, s=200, c='black') → Plots points at the top of the rectangle.

5. Plotting the Left and Right Borders

for y in range(height):

    plt.scatter(0, y, s=200, c='black')

    plt.scatter(width-1, y, s=200, c='black')

The loop iterates through all y positions from 0 to height-1:

plt.scatter(0, y, s=200, c='black') → Plots points at the left border.

plt.scatter(width-1, y, s=200, c='black') → Plots points at the right border.

6. Adjusting the Plot Limits

plt.xlim(-1, width)

plt.ylim(-1, height)

This ensures the plot is within the expected range, adding some padding for visibility.

7. Removing Axes and Setting Aspect Ratio

plt.axis('off')

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

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

plt.gca().set_aspect('equal', adjustable='datalim') → Ensures that the aspect ratio of the points remains equal, preventing distortions.

8. Adding a Title

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

This adds a title to the plot for better understanding.

9. Displaying the Plot

plt.show()

This renders and displays the final hollow rectangle pattern.

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Python Quiz on Try..Except

 

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Hollow Triangle Pattern Plot using python

 

import matplotlib.pyplot as plt

def plot_hollow_triangle(rows=5):

    # Define triangle vertices

    x1, y1 = 0, 0

    x2, y2 = rows - 1, 0

    x3, y3 = (rows - 1) / 2, np.sqrt(3) * (rows - 1) / 2  

    points = set()

    for i in range(rows):

        points.add((i, 0))

        lx = x1 + (x3 - x1) * i / (rows - 1)

        ly = y1 + (y3 - y1) * i / (rows - 1)

        points.add((lx, ly))

        rx = x2 + (x3 - x2) * i / (rows - 1)

        ry = y2 + (y3 - y2) * i / (rows - 1)

        points.add((rx, ry))

x_vals, y_vals = zip(*points)

    plt.figure(figsize=(6, 6))

    plt.scatter(x_vals, y_vals, color='black',s=400)

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

    plt.xticks([])

    plt.yticks([])

    plt.gca().set_frame_on(False) 

    plt.show()

plot_hollow_triangle(rows=10)

#source code --> clcoding.com 

Code Explanation:

1. Importing Libraries

import matplotlib.pyplot as plt

matplotlib.pyplot is used for plotting the dot pattern.

2. Function Definition

def plot_hollow_triangle(rows=5):

Defines a function plot_hollow_triangle(rows=5), where rows determines the size of the triangle.

3. Defining Triangle Vertices

x1, y1 = 0, 0

x2, y2 = rows - 1, 0

x3, y3 = (rows - 1) / 2, np.sqrt(3) * (rows - 1) / 2

These coordinates represent the three vertices of an equilateral triangle:

(x1, y1) = (0, 0): Left vertex (bottom-left)

(x2, y2) = (rows - 1, 0): Right vertex (bottom-right)

4. Storing Boundary Points

points = set()

A set named points is created to store (x, y) coordinates for plotting.

5. Generating Points for Triangle Edges

for i in range(rows):

Iterates i times to generate dots along the three edges of the triangle.

Bottom Edge (Base)

points.add((i, 0))

Places i dots along the bottom edge (x-axis).

Left Edge (Interpolated)

lx = x1 + (x3 - x1) * i / (rows - 1)

ly = y1 + (y3 - y1) * i / (rows - 1)

points.add((lx, ly))

Uses linear interpolation to calculate intermediate points from (x1, y1) → (x3, y3).

Right Edge (Interpolated)

rx = x2 + (x3 - x2) * i / (rows - 1)

ry = y2 + (y3 - y2) * i / (rows - 1)

points.add((rx, ry))

Uses linear interpolation to calculate intermediate points from (x2, y2) → (x3, y3).

6. Plotting the Points

x_vals, y_vals = zip(*points)

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

plt.scatter(x_vals, y_vals, color='black')

Extracts x and y coordinates from points and plots them using plt.scatter().

7. Adding a Title

plt.title("Hollow Equilateral Triangle Dot Pattern", fontsize=14, fontweight='bold')

Adds a title to the plot with bold font and size 14.

8. Removing Borders and Ticks

plt.xticks([])

plt.yticks([])

plt.gca().set_frame_on(False)

Removes x and y ticks for a clean look.

Removes the surrounding border (frame).

9. Displaying the Plot

plt.show()

Displays the triangle pattern.

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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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