Topic: Linked Lists in Python – Part 1: Introduction and Singly Linked List Basics
---
What is a Linked List?
• A linked list is a linear data structure where each element (called a node) contains:
• The data value.
• A pointer (or reference) to the next node.
• Unlike arrays, linked lists don’t require contiguous memory and can grow dynamically.
---
Why Use Linked Lists?
• Efficient insertions/deletions at the beginning or middle.
• No need for pre-defining size, unlike arrays.
• Used in memory-efficient applications like OS kernels, compilers, and real-time systems.
---
Types of Linked Lists
• Singly Linked List – Each node points to the next.
• Doubly Linked List – Nodes have next and previous pointers.
• Circular Linked List – The last node points back to the head.
---
Basic Structure of a Node
---
Building a Singly Linked List
---
Traversing the List
Usage:
---
Inserting at the Beginning
---
Summary
• A singly linked list stores data as a sequence of nodes linked by references.
• Supports dynamic memory usage, fast insertions, and flexible resizing.
• The key is managing node connections safely and efficiently.
---
Exercise
• Implement a method
---
#DataStructures #LinkedList #DSA #Python #CodingBasics
https://news.1rj.ru/str/DataScience4
---
What is a Linked List?
• A linked list is a linear data structure where each element (called a node) contains:
• The data value.
• A pointer (or reference) to the next node.
• Unlike arrays, linked lists don’t require contiguous memory and can grow dynamically.
---
Why Use Linked Lists?
• Efficient insertions/deletions at the beginning or middle.
• No need for pre-defining size, unlike arrays.
• Used in memory-efficient applications like OS kernels, compilers, and real-time systems.
---
Types of Linked Lists
• Singly Linked List – Each node points to the next.
• Doubly Linked List – Nodes have next and previous pointers.
• Circular Linked List – The last node points back to the head.
---
Basic Structure of a Node
class Node:
def __init__(self, data):
self.data = data
self.next = None
---
Building a Singly Linked List
class LinkedList:
def __init__(self):
self.head = None
def append(self, data):
new_node = Node(data)
if not self.head:
self.head = new_node
return
current = self.head
while current.next:
current = current.next
current.next = new_node
---
Traversing the List
def display(self):
current = self.head
while current:
print(current.data, end=" -> ")
current = current.next
print("None")
Usage:
ll = LinkedList()
ll.append(10)
ll.append(20)
ll.append(30)
ll.display() # Output: 10 -> 20 -> 30 -> None
---
Inserting at the Beginning
def insert_at_beginning(self, data):
new_node = Node(data)
new_node.next = self.head
self.head = new_node
---
Summary
• A singly linked list stores data as a sequence of nodes linked by references.
• Supports dynamic memory usage, fast insertions, and flexible resizing.
• The key is managing node connections safely and efficiently.
---
Exercise
• Implement a method
length() that returns the number of nodes in the list.---
#DataStructures #LinkedList #DSA #Python #CodingBasics
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❤3
Topic: Linked Lists in Python – Part 2: Insertion, Deletion, and Search Operations
---
Recap from Part 1
• A singly linked list consists of nodes, where each node holds data and a pointer to the next node.
• We've implemented basic append and display functions.
Now we’ll explore insertion at specific positions, deletion, and searching.
---
1. Insert at a Specific Position
---
2. Delete a Node by Value
---
3. Delete a Node by Index
---
4. Search for an Element
---
5. Complete Class with All Methods
*(You can reuse the method definitions above.)*
---
Summary
• You can manipulate linked lists with insertions and deletions at any position.
• Searching through a singly linked list is O(n).
• Always check for edge cases: empty list, index bounds, and duplicates.
---
Exercise
• Write a method
---
#DSA #LinkedList #Python #Insertion #Deletion #Search
https://news.1rj.ru/str/DataScience4
---
Recap from Part 1
• A singly linked list consists of nodes, where each node holds data and a pointer to the next node.
• We've implemented basic append and display functions.
Now we’ll explore insertion at specific positions, deletion, and searching.
---
1. Insert at a Specific Position
def insert_at_position(self, index, data):
if index < 0:
raise IndexError("Index cannot be negative")
new_node = Node(data)
if index == 0:
new_node.next = self.head
self.head = new_node
return
current = self.head
for _ in range(index - 1):
if not current:
raise IndexError("Index out of bounds")
current = current.next
new_node.next = current.next
current.next = new_node
---
2. Delete a Node by Value
def delete_by_value(self, value):
if not self.head:
return
if self.head.data == value:
self.head = self.head.next
return
current = self.head
while current.next and current.next.data != value:
current = current.next
if current.next:
current.next = current.next.next
---
3. Delete a Node by Index
def delete_by_index(self, index):
if index < 0:
raise IndexError("Index cannot be negative")
if not self.head:
raise IndexError("List is empty")
if index == 0:
self.head = self.head.next
return
current = self.head
for _ in range(index - 1):
if not current.next:
raise IndexError("Index out of bounds")
current = current.next
if current.next:
current.next = current.next.next
---
4. Search for an Element
def search(self, value):
current = self.head
index = 0
while current:
if current.data == value:
return index
current = current.next
index += 1
return -1 # Not found
---
5. Complete Class with All Methods
class LinkedList:
def __init__(self):
self.head = None
def append(self, data): ...
def display(self): ...
def insert_at_position(self, index, data): ...
def delete_by_value(self, value): ...
def delete_by_index(self, index): ...
def search(self, value): ...
*(You can reuse the method definitions above.)*
---
Summary
• You can manipulate linked lists with insertions and deletions at any position.
• Searching through a singly linked list is O(n).
• Always check for edge cases: empty list, index bounds, and duplicates.
---
Exercise
• Write a method
reverse() that reverses the linked list in-place and test it on a list of 5+ elements.---
#DSA #LinkedList #Python #Insertion #Deletion #Search
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❤2
Topic: Linked Lists in Python – Part 3: Reversing, Detecting Loops, and Middle Node
---
In this part, we’ll explore advanced operations that are frequently asked in coding interviews using singly linked lists.
---
1. Reverse a Linked List (Iterative)
• Time Complexity: O(n)
• Space Complexity: O(1)
---
2. Find the Middle of the Linked List
• Use the slow and fast pointer approach.
---
3. Detect a Loop in the Linked List
• Use Floyd’s Cycle Detection Algorithm (a.k.a. Tortoise and Hare).
---
4. Find the N-th Node from the End
---
5. Full Example: Testing All Methods
---
Summary
• Use pointer manipulation for efficient algorithms in linked lists.
• Common techniques:
• Fast/slow pointers
• Reversal by in-place re-linking
• Two-pointer gap approach for nth from end
---
Exercise
• Write a method
---
#DSA #LinkedList #ReverseList #LoopDetection #TwoPointerTechnique
https://news.1rj.ru/str/DataScience4
---
In this part, we’ll explore advanced operations that are frequently asked in coding interviews using singly linked lists.
---
1. Reverse a Linked List (Iterative)
def reverse(self):
prev = None
current = self.head
while current:
next_node = current.next
current.next = prev
prev = current
current = next_node
self.head = prev
• Time Complexity: O(n)
• Space Complexity: O(1)
---
2. Find the Middle of the Linked List
• Use the slow and fast pointer approach.
def find_middle(self):
slow = fast = self.head
while fast and fast.next:
slow = slow.next
fast = fast.next.next
return slow.data if slow else None
---
3. Detect a Loop in the Linked List
• Use Floyd’s Cycle Detection Algorithm (a.k.a. Tortoise and Hare).
def has_loop(self):
slow = fast = self.head
while fast and fast.next:
slow = slow.next
fast = fast.next.next
if slow == fast:
return True
return False
---
4. Find the N-th Node from the End
def nth_from_end(self, n):
first = self.head
second = self.head
for _ in range(n):
if not first:
return None
first = first.next
while first:
first = first.next
second = second.next
return second.data
---
5. Full Example: Testing All Methods
ll = LinkedList()
for value in [10, 20, 30, 40, 50]:
ll.append(value)
ll.display()
ll.reverse()
ll.display()
print("Middle:", ll.find_middle())
print("Has Loop?", ll.has_loop())
print("3rd from End:", ll.nth_from_end(3))
---
Summary
• Use pointer manipulation for efficient algorithms in linked lists.
• Common techniques:
• Fast/slow pointers
• Reversal by in-place re-linking
• Two-pointer gap approach for nth from end
---
Exercise
• Write a method
is_palindrome() that checks if the linked list reads the same forwards and backwards (without converting it to a list or using extra space).---
#DSA #LinkedList #ReverseList #LoopDetection #TwoPointerTechnique
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❤5
Topic: Linked Lists in Python – Part 4: Merging, Sorting, and Advanced Interview Problems
---
In this final part of the Linked List series, we’ll tackle more advanced and practical use cases like merging sorted lists, sorting a linked list, and interview-level challenges.
---
1. Merging Two Sorted Linked Lists
---
2. Sorting a Linked List (Merge Sort – O(n log n))
• Attach this to your
---
3. Removing Duplicates from a Sorted List
---
4. Intersection Point of Two Linked Lists
---
5. Flatten a Multilevel Linked List
Imagine a list where each node has
---
Summary
• You now know how to merge, sort, and deduplicate linked lists.
• Techniques like merge sort, two-pointer traversal, and recursive flattening are essential for mastering linked lists.
• These problems are frequently asked in interviews at top tech companies.
---
Exercise
• Given two unsorted linked lists, write a function that returns a new linked list containing only the elements present in both lists (intersection), without using extra space or sets.
---
#DSA #LinkedList #MergeSort #AdvancedDSA #CodingInterview
https://news.1rj.ru/str/DataScience4
---
In this final part of the Linked List series, we’ll tackle more advanced and practical use cases like merging sorted lists, sorting a linked list, and interview-level challenges.
---
1. Merging Two Sorted Linked Lists
def merge_sorted_lists(l1, l2):
dummy = Node(0)
tail = dummy
while l1 and l2:
if l1.data < l2.data:
tail.next = l1
l1 = l1.next
else:
tail.next = l2
l2 = l2.next
tail = tail.next
tail.next = l1 or l2
return dummy.next
---
2. Sorting a Linked List (Merge Sort – O(n log n))
def merge_sort(head):
if not head or not head.next:
return head
# Split list
slow, fast = head, head.next
while fast and fast.next:
slow = slow.next
fast = fast.next.next
mid = slow.next
slow.next = None
left = merge_sort(head)
right = merge_sort(mid)
return merge_sorted_lists(left, right)
• Attach this to your
LinkedList class to sort self.head.---
3. Removing Duplicates from a Sorted List
def remove_duplicates(self):
current = self.head
while current and current.next:
if current.data == current.next.data:
current.next = current.next.next
else:
current = current.next
---
4. Intersection Point of Two Linked Lists
def get_intersection_node(headA, headB):
if not headA or not headB:
return None
a, b = headA, headB
while a != b:
a = a.next if a else headB
b = b.next if b else headA
return a # Can be None or the intersecting node
---
5. Flatten a Multilevel Linked List
Imagine a list where each node has
next and child pointers. Recursively flatten:def flatten(node):
if not node:
return None
next_node = node.next
if node.child:
child_tail = flatten(node.child)
node.next = node.child
node.child = None
child_tail.next = flatten(next_node)
return child_tail if child_tail.next else child_tail
else:
node.next = flatten(next_node)
return node
---
Summary
• You now know how to merge, sort, and deduplicate linked lists.
• Techniques like merge sort, two-pointer traversal, and recursive flattening are essential for mastering linked lists.
• These problems are frequently asked in interviews at top tech companies.
---
Exercise
• Given two unsorted linked lists, write a function that returns a new linked list containing only the elements present in both lists (intersection), without using extra space or sets.
---
#DSA #LinkedList #MergeSort #AdvancedDSA #CodingInterview
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❤2
Topic: Python OpenCV – Part 1: Introduction, Image Reading, and Basic Operations
---
What is OpenCV?
• OpenCV (Open Source Computer Vision Library) is a powerful computer vision and image processing library.
• It supports image and video capture, analysis, object detection, face recognition, and much more.
• Commonly used with Python, C++, and machine learning pipelines.
---
Installing OpenCV
---
1. Reading and Displaying Images
---
2. Image Shape and Type
---
3. Converting Color Spaces
---
4. Saving an Image
---
5. Drawing Shapes
---
6. Resize and Flip
---
Summary
• OpenCV allows you to read, display, modify, and save images easily.
• You can perform basic tasks like drawing, resizing, flipping, and color transformations.
• These operations are the building blocks for image analysis, preprocessing, and machine vision applications.
---
Exercise
• Write a program that:
1. Loads an image.
2. Converts it to grayscale.
3. Draws a blue circle in the center.
4. Saves the new image to disk.
---
#Python #OpenCV #ImageProcessing #ComputerVision #Beginners
https://news.1rj.ru/str/DataScience4
---
What is OpenCV?
• OpenCV (Open Source Computer Vision Library) is a powerful computer vision and image processing library.
• It supports image and video capture, analysis, object detection, face recognition, and much more.
• Commonly used with Python, C++, and machine learning pipelines.
---
Installing OpenCV
pip install opencv-python
---
1. Reading and Displaying Images
import cv2
# Read the image
image = cv2.imread('image.jpg')
# Display the image in a window
cv2.imshow('My Image', image)
cv2.waitKey(0) # Wait for any key to close the window
cv2.destroyAllWindows()
---
2. Image Shape and Type
print(image.shape) # (height, width, channels)
print(image.dtype) # uint8 (8-bit integers for each channel)
---
3. Converting Color Spaces
# Convert BGR to Grayscale
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
# Convert BGR to RGB (for matplotlib or image correction)
rgb = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
---
4. Saving an Image
cv2.imwrite('gray_image.png', gray)---
5. Drawing Shapes
# Draw a red rectangle
cv2.rectangle(image, (50, 50), (200, 200), (0, 0, 255), 2)
# Draw a filled circle
cv2.circle(image, (150, 150), 40, (255, 0, 0), -1)
# Draw text
cv2.putText(image, 'OpenCV!', (40, 300), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 255, 0), 2)
cv2.imshow('Drawn Image', image)
cv2.waitKey(0)
cv2.destroyAllWindows()
---
6. Resize and Flip
# Resize image
resized = cv2.resize(image, (300, 300))
# Flip image horizontally
flipped = cv2.flip(image, 1)
---
Summary
• OpenCV allows you to read, display, modify, and save images easily.
• You can perform basic tasks like drawing, resizing, flipping, and color transformations.
• These operations are the building blocks for image analysis, preprocessing, and machine vision applications.
---
Exercise
• Write a program that:
1. Loads an image.
2. Converts it to grayscale.
3. Draws a blue circle in the center.
4. Saves the new image to disk.
---
#Python #OpenCV #ImageProcessing #ComputerVision #Beginners
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❤2
Topic: Python OpenCV – Part 2: Image Thresholding, Blurring, and Edge Detection
---
1. Image Thresholding
• Converts grayscale images to binary (black & white) by setting a pixel value threshold.
• Common thresholding types:
*
*
*
*
*
---
2. Image Blurring (Smoothing)
• Helps reduce image noise and detail.
---
3. Edge Detection with Canny
• Canny edge detection is one of the most popular techniques for detecting edges in an image.
* The two thresholds (100 and 200) are used for hysteresis (strong vs. weak edges).
---
4. Combining Blur + Edge Detection
• Blurring before edge detection reduces noise and improves accuracy.
---
Summary
• Thresholding simplifies images to black & white based on intensity.
• Blurring smooths out images and reduces noise.
• Canny Edge Detection is a reliable method for identifying edges.
• These tools are fundamental for object detection, image segmentation, and OCR tasks.
---
Exercise
• Write a program that:
1. Loads an image in grayscale.
2. Applies Gaussian blur.
3. Uses Canny to detect edges.
4. Saves the final edge-detected image to disk.
---
#Python #OpenCV #ImageProcessing #EdgeDetection #ComputerVision
https://news.1rj.ru/str/DataScience4
---
1. Image Thresholding
• Converts grayscale images to binary (black & white) by setting a pixel value threshold.
import cv2
image = cv2.imread('image.jpg', 0) # load as grayscale
_, binary = cv2.threshold(image, 127, 255, cv2.THRESH_BINARY)
cv2.imshow("Binary Image", binary)
cv2.waitKey(0)
cv2.destroyAllWindows()
• Common thresholding types:
*
THRESH_BINARY*
THRESH_BINARY_INV*
THRESH_TRUNC*
THRESH_TOZERO*
THRESH_OTSU (automatic threshold)_, otsu = cv2.threshold(image, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU)
---
2. Image Blurring (Smoothing)
• Helps reduce image noise and detail.
# Gaussian Blur
blurred = cv2.GaussianBlur(image, (5, 5), 0)
# Median Blur
median = cv2.medianBlur(image, 5)
# Bilateral Filter (preserves edges)
bilateral = cv2.bilateralFilter(image, 9, 75, 75)
---
3. Edge Detection with Canny
• Canny edge detection is one of the most popular techniques for detecting edges in an image.
edges = cv2.Canny(image, 100, 200)
cv2.imshow("Edges", edges)
cv2.waitKey(0)
cv2.destroyAllWindows()
* The two thresholds (100 and 200) are used for hysteresis (strong vs. weak edges).
---
4. Combining Blur + Edge Detection
blurred = cv2.GaussianBlur(image, (5, 5), 0)
edges = cv2.Canny(blurred, 50, 150)
• Blurring before edge detection reduces noise and improves accuracy.
---
Summary
• Thresholding simplifies images to black & white based on intensity.
• Blurring smooths out images and reduces noise.
• Canny Edge Detection is a reliable method for identifying edges.
• These tools are fundamental for object detection, image segmentation, and OCR tasks.
---
Exercise
• Write a program that:
1. Loads an image in grayscale.
2. Applies Gaussian blur.
3. Uses Canny to detect edges.
4. Saves the final edge-detected image to disk.
---
#Python #OpenCV #ImageProcessing #EdgeDetection #ComputerVision
https://news.1rj.ru/str/DataScience4
❤2
Topic: Python OpenCV – Part 3: Contours, Morphological Operations, and Image Masking
---
1. Finding Contours
• Contours are curves joining all continuous points along a boundary having the same color or intensity.
---
2. Morphological Operations
• Used to remove noise or fill gaps.
* Erosion: Removes pixels on object boundaries.
* Dilation: Adds pixels to object boundaries.
• Other useful operations:
* Opening: erosion followed by dilation (removes noise).
* Closing: dilation followed by erosion (closes small holes).
---
3. Image Masking
• Use a mask to isolate part of an image.
---
Summary
• Contours help detect shapes and boundaries.
• Morphological operations clean up binary images by removing noise or filling holes.
• Masks isolate regions of interest for further processing.
---
Exercise
• Load an image with multiple shapes, use contour detection to count the number of distinct shapes, then draw bounding rectangles around each.
---
#Python #OpenCV #Contours #Morphology #ImageMasking
https://news.1rj.ru/str/DataScience4
---
1. Finding Contours
• Contours are curves joining all continuous points along a boundary having the same color or intensity.
import cv2
image = cv2.imread('shapes.png')
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
# Threshold to get binary image
_, thresh = cv2.threshold(gray, 127, 255, cv2.THRESH_BINARY)
# Find contours
contours, hierarchy = cv2.findContours(thresh, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
# Draw contours
cv2.drawContours(image, contours, -1, (0, 255, 0), 3)
cv2.imshow("Contours", image)
cv2.waitKey(0)
cv2.destroyAllWindows()
---
2. Morphological Operations
• Used to remove noise or fill gaps.
* Erosion: Removes pixels on object boundaries.
* Dilation: Adds pixels to object boundaries.
kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (5, 5))
eroded = cv2.erode(thresh, kernel, iterations=1)
dilated = cv2.dilate(thresh, kernel, iterations=1)
• Other useful operations:
* Opening: erosion followed by dilation (removes noise).
* Closing: dilation followed by erosion (closes small holes).
opening = cv2.morphologyEx(thresh, cv2.MORPH_OPEN, kernel)
closing = cv2.morphologyEx(thresh, cv2.MORPH_CLOSE, kernel)
---
3. Image Masking
• Use a mask to isolate part of an image.
mask = cv2.cvtColor(thresh, cv2.COLOR_GRAY2BGR)
masked_img = cv2.bitwise_and(image, mask)
cv2.imshow("Masked Image", masked_img)
cv2.waitKey(0)
cv2.destroyAllWindows()
---
Summary
• Contours help detect shapes and boundaries.
• Morphological operations clean up binary images by removing noise or filling holes.
• Masks isolate regions of interest for further processing.
---
Exercise
• Load an image with multiple shapes, use contour detection to count the number of distinct shapes, then draw bounding rectangles around each.
---
#Python #OpenCV #Contours #Morphology #ImageMasking
https://news.1rj.ru/str/DataScience4
❤3
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Topic: Python OpenCV – Part 4: Video Processing, Webcam Capture, and Real-Time Operations
---
1. Reading Video from File
---
2. Capturing Video from Webcam
---
3. Saving Video to File
---
4. Real-Time Video Processing
• Example: Convert webcam feed to grayscale live.
---
Summary
• You can capture video from files or webcams easily with OpenCV.
• Real-time processing lets you modify frames on the fly (filters, detections).
• Saving video requires specifying codec, frame rate, and resolution.
---
Exercise
• Write a program that captures webcam video, applies Canny edge detection to each frame in real-time, and saves the processed video to disk. Quit when pressing 'q'.
---
#Python #OpenCV #VideoProcessing #Webcam #RealTime
https://news.1rj.ru/str/DataScience4
---
1. Reading Video from File
import cv2
cap = cv2.VideoCapture('video.mp4')
while cap.isOpened():
ret, frame = cap.read()
if not ret:
break
cv2.imshow('Video Frame', frame)
if cv2.waitKey(25) & 0xFF == ord('q'):
break
cap.release()
cv2.destroyAllWindows()
---
2. Capturing Video from Webcam
cap = cv2.VideoCapture(0) # 0 is usually the built-in webcam
while True:
ret, frame = cap.read()
if not ret:
break
cv2.imshow('Webcam Feed', frame)
if cv2.waitKey(1) & 0xFF == ord('q'):
break
cap.release()
cv2.destroyAllWindows()
---
3. Saving Video to File
fourcc = cv2.VideoWriter_fourcc(*'XVID')
out = cv2.VideoWriter('output.avi', fourcc, 20.0, (640, 480))
cap = cv2.VideoCapture(0)
while True:
ret, frame = cap.read()
if not ret:
break
out.write(frame) # write frame to output file
cv2.imshow('Recording', frame)
if cv2.waitKey(1) & 0xFF == ord('q'):
break
cap.release()
out.release()
cv2.destroyAllWindows()
---
4. Real-Time Video Processing
• Example: Convert webcam feed to grayscale live.
cap = cv2.VideoCapture(0)
while True:
ret, frame = cap.read()
if not ret:
break
gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
cv2.imshow('Grayscale Webcam', gray)
if cv2.waitKey(1) & 0xFF == ord('q'):
break
cap.release()
cv2.destroyAllWindows()
---
Summary
• You can capture video from files or webcams easily with OpenCV.
• Real-time processing lets you modify frames on the fly (filters, detections).
• Saving video requires specifying codec, frame rate, and resolution.
---
Exercise
• Write a program that captures webcam video, applies Canny edge detection to each frame in real-time, and saves the processed video to disk. Quit when pressing 'q'.
---
#Python #OpenCV #VideoProcessing #Webcam #RealTime
https://news.1rj.ru/str/DataScience4
❤3
Topic: 20 Important Python OpenCV Interview Questions with Brief Answers
---
1. What is OpenCV?
OpenCV is an open-source library for computer vision, image processing, and machine learning.
2. How do you read and display an image using OpenCV?
Use
3. What color format does OpenCV use by default?
OpenCV uses BGR format instead of RGB.
4. How to convert an image from BGR to Grayscale?
Use
5. What is the difference between `cv2.imshow()` and matplotlib’s `imshow()`?
6. How do you write/save an image to disk?
Use
7. What are image thresholds?
Techniques to segment images into binary images based on pixel intensity.
8. What is Gaussian Blur, and why is it used?
A smoothing filter to reduce image noise and detail.
9. Explain the Canny Edge Detection process.
It detects edges using gradients, non-maximum suppression, and hysteresis thresholding.
10. How do you capture video from a webcam using OpenCV?
Use
11. What are contours in OpenCV?
Curves joining continuous points with the same intensity, used for shape detection.
12. How do you find and draw contours?
Use
13. What are morphological operations?
Operations like erosion and dilation that process shapes in binary images.
14. What is the difference between erosion and dilation?
Erosion removes pixels on object edges; dilation adds pixels.
15. How to perform image masking in OpenCV?
Use
16. What is the use of `cv2.waitKey()`?
Waits for a key event for a specified time; needed to display images properly.
17. How do you resize an image?
Use
18. Explain how to save a video using OpenCV.
Use
19. How to convert an image from OpenCV BGR format to RGB for matplotlib?
Use
20. What is the difference between `cv2.threshold()` and `cv2.adaptiveThreshold()`?
---
Summary
These questions cover basic to intermediate OpenCV concepts essential for interviews and practical use.
---
#Python #OpenCV #InterviewQuestions #ComputerVision #ImageProcessing
https://news.1rj.ru/str/DataScience4
---
1. What is OpenCV?
OpenCV is an open-source library for computer vision, image processing, and machine learning.
2. How do you read and display an image using OpenCV?
Use
cv2.imread() to read and cv2.imshow() to display images.3. What color format does OpenCV use by default?
OpenCV uses BGR format instead of RGB.
4. How to convert an image from BGR to Grayscale?
Use
cv2.cvtColor(image, cv2.COLOR_BGR2GRAY).5. What is the difference between `cv2.imshow()` and matplotlib’s `imshow()`?
cv2.imshow() uses BGR and OpenCV windows; matplotlib uses RGB and inline plotting.6. How do you write/save an image to disk?
Use
cv2.imwrite('filename.jpg', image).7. What are image thresholds?
Techniques to segment images into binary images based on pixel intensity.
8. What is Gaussian Blur, and why is it used?
A smoothing filter to reduce image noise and detail.
9. Explain the Canny Edge Detection process.
It detects edges using gradients, non-maximum suppression, and hysteresis thresholding.
10. How do you capture video from a webcam using OpenCV?
Use
cv2.VideoCapture(0) and read frames in a loop.11. What are contours in OpenCV?
Curves joining continuous points with the same intensity, used for shape detection.
12. How do you find and draw contours?
Use
cv2.findContours() and cv2.drawContours().13. What are morphological operations?
Operations like erosion and dilation that process shapes in binary images.
14. What is the difference between erosion and dilation?
Erosion removes pixels on object edges; dilation adds pixels.
15. How to perform image masking in OpenCV?
Use
cv2.bitwise_and() with an image and a mask.16. What is the use of `cv2.waitKey()`?
Waits for a key event for a specified time; needed to display images properly.
17. How do you resize an image?
Use
cv2.resize(image, (width, height)).18. Explain how to save a video using OpenCV.
Use
cv2.VideoWriter() with codec, fps, and frame size.19. How to convert an image from OpenCV BGR format to RGB for matplotlib?
Use
cv2.cvtColor(image, cv2.COLOR_BGR2RGB).20. What is the difference between `cv2.threshold()` and `cv2.adaptiveThreshold()`?
cv2.threshold() applies global threshold; adaptiveThreshold() applies varying thresholds locally.---
Summary
These questions cover basic to intermediate OpenCV concepts essential for interviews and practical use.
---
#Python #OpenCV #InterviewQuestions #ComputerVision #ImageProcessing
https://news.1rj.ru/str/DataScience4
❤3
Topic: Python – Reading Images from Datasets and Organizing Them
---
1. Reading Images from Folder Structure
Assuming your dataset folder looks like this:
You can use Python libraries like os, OpenCV, or PIL to read and organize images by their classes.
---
2. Code Example Using OpenCV
---
3. Optional: Resize Images for Uniformity
Use this inside the loop before appending
---
4. Using PIL (Pillow) Instead of OpenCV
---
5. Organizing Images in a Dictionary
---
6. Summary
• Use os.listdir() to iterate dataset directories.
• Read images with cv2.imread() or PIL.Image.open().
• Resize images to a uniform shape for model input.
• Store images and labels in lists or dictionaries for easy access.
---
Exercise
• Extend the code to save the loaded images and labels as numpy arrays for faster loading in the future.
---
#Python #ImageProcessing #DatasetHandling #OpenCV #PIL
https://news.1rj.ru/str/DataScience4
---
1. Reading Images from Folder Structure
Assuming your dataset folder looks like this:
dataset/
class1/
img1.jpg
img2.jpg
class2/
img3.jpg
img4.jpg
You can use Python libraries like os, OpenCV, or PIL to read and organize images by their classes.
---
2. Code Example Using OpenCV
import os
import cv2
dataset_path = "dataset"
data = []
labels = []
for class_name in os.listdir(dataset_path):
class_dir = os.path.join(dataset_path, class_name)
if os.path.isdir(class_dir):
for img_name in os.listdir(class_dir):
img_path = os.path.join(class_dir, img_name)
img = cv2.imread(img_path)
if img is not None:
data.append(img)
labels.append(class_name)
print(f"Total images: {len(data)}")
print(f"Total labels: {len(labels)}")
---
3. Optional: Resize Images for Uniformity
target_size = (128, 128)
resized_img = cv2.resize(img, target_size)
Use this inside the loop before appending
img to data.---
4. Using PIL (Pillow) Instead of OpenCV
from PIL import Image
img = Image.open(img_path)
img = img.resize((128, 128))
img_array = np.array(img)
---
5. Organizing Images in a Dictionary
dataset_dict = {}
for class_name in os.listdir(dataset_path):
class_dir = os.path.join(dataset_path, class_name)
if os.path.isdir(class_dir):
dataset_dict[class_name] = []
for img_name in os.listdir(class_dir):
img_path = os.path.join(class_dir, img_name)
img = cv2.imread(img_path)
if img is not None:
dataset_dict[class_name].append(img)---
6. Summary
• Use os.listdir() to iterate dataset directories.
• Read images with cv2.imread() or PIL.Image.open().
• Resize images to a uniform shape for model input.
• Store images and labels in lists or dictionaries for easy access.
---
Exercise
• Extend the code to save the loaded images and labels as numpy arrays for faster loading in the future.
---
#Python #ImageProcessing #DatasetHandling #OpenCV #PIL
https://news.1rj.ru/str/DataScience4
❤4
Topic: Python – Reading Images from Datasets and Organizing Them (Part 2): Using PyTorch and TensorFlow Data Loaders
---
1. Using PyTorch’s `ImageFolder` and `DataLoader`
PyTorch provides an easy way to load image datasets organized in folders by classes.
---
2. Iterating Through DataLoader
---
3. Using TensorFlow `image_dataset_from_directory`
TensorFlow Keras also provides utilities for loading datasets organized in folders.
---
4. Dataset Splitting
You can split datasets into training and validation sets easily:
---
5. Summary
• PyTorch’s ImageFolder + DataLoader offers a quick way to load and batch datasets.
• TensorFlow’s image\_dataset\_from\_directory provides similar high-level dataset loading.
• Both allow easy transformations, batching, and shuffling.
---
Exercise
• Write code to normalize images in TensorFlow dataset using
---
#Python #DatasetHandling #PyTorch #TensorFlow #ImageProcessing
https://news.1rj.ru/str/DataScience4
---
1. Using PyTorch’s `ImageFolder` and `DataLoader`
PyTorch provides an easy way to load image datasets organized in folders by classes.
from torchvision import datasets, transforms
from torch.utils.data import DataLoader
# Define transformations (resize, normalize, convert to tensor)
transform = transforms.Compose([
transforms.Resize((128, 128)),
transforms.ToTensor(),
transforms.Normalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225])
])
dataset = datasets.ImageFolder(root='dataset/', transform=transform)
# Create DataLoader for batching and shuffling
dataloader = DataLoader(dataset, batch_size=32, shuffle=True)
# Access class names
class_names = dataset.classes
print(class_names)
---
2. Iterating Through DataLoader
for images, labels in dataloader:
print(images.shape) # (batch_size, 3, 128, 128)
print(labels)
# Use images and labels for training or validation
break
---
3. Using TensorFlow `image_dataset_from_directory`
TensorFlow Keras also provides utilities for loading datasets organized in folders.
import tensorflow as tf
dataset = tf.keras.preprocessing.image_dataset_from_directory(
'dataset/',
image_size=(128, 128),
batch_size=32,
label_mode='int' # can be 'categorical', 'binary', or None
)
class_names = dataset.class_names
print(class_names)
for images, labels in dataset.take(1):
print(images.shape)
print(labels)
---
4. Dataset Splitting
You can split datasets into training and validation sets easily:
train_ds = tf.keras.preprocessing.image_dataset_from_directory(
'dataset/',
validation_split=0.2,
subset="training",
seed=123,
image_size=(128, 128),
batch_size=32
)
val_ds = tf.keras.preprocessing.image_dataset_from_directory(
'dataset/',
validation_split=0.2,
subset="validation",
seed=123,
image_size=(128, 128),
batch_size=32
)
---
5. Summary
• PyTorch’s ImageFolder + DataLoader offers a quick way to load and batch datasets.
• TensorFlow’s image\_dataset\_from\_directory provides similar high-level dataset loading.
• Both allow easy transformations, batching, and shuffling.
---
Exercise
• Write code to normalize images in TensorFlow dataset using
map() with Rescaling.---
#Python #DatasetHandling #PyTorch #TensorFlow #ImageProcessing
https://news.1rj.ru/str/DataScience4
❤4
Topic: Python – Reading Images from Datasets and Organizing Them (Part 3): Custom Dataset Class and Data Augmentation
---
1. Creating a Custom Dataset Class (PyTorch)
Sometimes you need more control over how images and labels are loaded and processed. You can create a custom dataset class by extending
---
2. Using Data Augmentation with `transforms`
Data augmentation helps improve model generalization by artificially increasing dataset diversity.
Pass this transform to the custom dataset:
---
3. Loading Dataset with DataLoader
---
4. Summary
• Custom dataset classes offer flexibility in how data is loaded and labeled.
• Data augmentation techniques such as flipping and rotation can be applied using torchvision transforms.
• Use DataLoader for batching and shuffling during training.
---
Exercise
• Extend the custom dataset to handle grayscale images and apply a random brightness adjustment transform.
---
#Python #DatasetHandling #PyTorch #DataAugmentation #ImageProcessing
https://news.1rj.ru/str/DataScience4
---
1. Creating a Custom Dataset Class (PyTorch)
Sometimes you need more control over how images and labels are loaded and processed. You can create a custom dataset class by extending
torch.utils.data.Dataset.import os
from PIL import Image
from torch.utils.data import Dataset
class CustomImageDataset(Dataset):
def __init__(self, root_dir, transform=None):
self.root_dir = root_dir
self.transform = transform
self.image_paths = []
self.labels = []
self.class_to_idx = {}
classes = sorted(os.listdir(root_dir))
self.class_to_idx = {cls_name: idx for idx, cls_name in enumerate(classes)}
for cls_name in classes:
cls_dir = os.path.join(root_dir, cls_name)
for img_name in os.listdir(cls_dir):
img_path = os.path.join(cls_dir, img_name)
self.image_paths.append(img_path)
self.labels.append(self.class_to_idx[cls_name])
def __len__(self):
return len(self.image_paths)
def __getitem__(self, idx):
img_path = self.image_paths[idx]
image = Image.open(img_path).convert("RGB")
label = self.labels[idx]
if self.transform:
image = self.transform(image)
return image, label
---
2. Using Data Augmentation with `transforms`
Data augmentation helps improve model generalization by artificially increasing dataset diversity.
from torchvision import transforms
transform = transforms.Compose([
transforms.Resize((128, 128)),
transforms.RandomHorizontalFlip(),
transforms.RandomRotation(10),
transforms.ToTensor(),
transforms.Normalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225])
])
Pass this transform to the custom dataset:
dataset = CustomImageDataset(root_dir='dataset/', transform=transform)
---
3. Loading Dataset with DataLoader
from torch.utils.data import DataLoader
dataloader = DataLoader(dataset, batch_size=32, shuffle=True)
---
4. Summary
• Custom dataset classes offer flexibility in how data is loaded and labeled.
• Data augmentation techniques such as flipping and rotation can be applied using torchvision transforms.
• Use DataLoader for batching and shuffling during training.
---
Exercise
• Extend the custom dataset to handle grayscale images and apply a random brightness adjustment transform.
---
#Python #DatasetHandling #PyTorch #DataAugmentation #ImageProcessing
https://news.1rj.ru/str/DataScience4
❤2
Topic: 20 Important Python Questions on Reading and Organizing Images from Datasets
---
1. How can you read images from a directory using Python?
Use libraries like OpenCV (
2. How do you organize images by class labels if they are stored in subfolders?
Iterate over each subfolder, treat folder names as labels, and map images accordingly.
3. What is the difference between OpenCV and PIL for image reading?
OpenCV reads images in BGR format and uses NumPy arrays; PIL uses RGB and has more image manipulation utilities.
4. How do you resize images before feeding them to a model?
Use
5. What is a good practice to handle different image sizes in datasets?
Resize all images to a fixed size or use data loaders that apply transformations.
6. How to convert images to NumPy arrays?
In OpenCV, images are already NumPy arrays; with PIL, use
7. How do you normalize images?
Scale pixel values, typically to \[0,1] by dividing by 255 or standardize with mean and std.
8. How can you load large datasets efficiently?
Use generators or data loaders to load images batch-wise instead of loading all at once.
9. What is `torchvision.datasets.ImageFolder`?
A PyTorch utility to load images from a directory with subfolders as class labels.
10. How do you apply transformations and augmentations during image loading?
Use
11. How can you split datasets into training and validation sets?
Use libraries like
12. How do you handle corrupted or unreadable images during loading?
Use try-except blocks to catch exceptions and skip those files.
13. How do you batch images for training deep learning models?
Use
14. What are common image augmentations used during training?
Flips, rotations, scaling, cropping, color jittering, and normalization.
15. How do you convert labels (class names) to numeric indices?
Create a mapping dictionary from class names to indices.
16. How can you visualize images and labels after loading?
Use matplotlib’s
17. How to read images in grayscale?
With OpenCV:
18. How to save processed images after loading?
Use
19. How do you organize dataset information (images and labels) in Python?
Use lists, dictionaries, or pandas DataFrames.
20. How to handle imbalanced datasets?
Use class weighting, oversampling, or undersampling techniques during data loading.
---
Summary
Mastering image loading and organization is fundamental for effective data preprocessing in computer vision projects.
---
#Python #ImageProcessing #DatasetHandling #OpenCV #DeepLearning
https://news.1rj.ru/str/DataScience4
---
1. How can you read images from a directory using Python?
Use libraries like OpenCV (
cv2.imread) or PIL (Image.open).2. How do you organize images by class labels if they are stored in subfolders?
Iterate over each subfolder, treat folder names as labels, and map images accordingly.
3. What is the difference between OpenCV and PIL for image reading?
OpenCV reads images in BGR format and uses NumPy arrays; PIL uses RGB and has more image manipulation utilities.
4. How do you resize images before feeding them to a model?
Use
cv2.resize() or PIL’s resize() method.5. What is a good practice to handle different image sizes in datasets?
Resize all images to a fixed size or use data loaders that apply transformations.
6. How to convert images to NumPy arrays?
In OpenCV, images are already NumPy arrays; with PIL, use
np.array(image).7. How do you normalize images?
Scale pixel values, typically to \[0,1] by dividing by 255 or standardize with mean and std.
8. How can you load large datasets efficiently?
Use generators or data loaders to load images batch-wise instead of loading all at once.
9. What is `torchvision.datasets.ImageFolder`?
A PyTorch utility to load images from a directory with subfolders as class labels.
10. How do you apply transformations and augmentations during image loading?
Use
torchvision.transforms or TensorFlow preprocessing layers.11. How can you split datasets into training and validation sets?
Use libraries like
sklearn.model_selection.train_test_split or parameters in dataset loaders.12. How do you handle corrupted or unreadable images during loading?
Use try-except blocks to catch exceptions and skip those files.
13. How do you batch images for training deep learning models?
Use
DataLoader in PyTorch or TensorFlow datasets with batching enabled.14. What are common image augmentations used during training?
Flips, rotations, scaling, cropping, color jittering, and normalization.
15. How do you convert labels (class names) to numeric indices?
Create a mapping dictionary from class names to indices.
16. How can you visualize images and labels after loading?
Use matplotlib’s
imshow() and print labels alongside.17. How to read images in grayscale?
With OpenCV:
cv2.imread(path, cv2.IMREAD_GRAYSCALE).18. How to save processed images after loading?
Use
cv2.imwrite() or PIL.Image.save().19. How do you organize dataset information (images and labels) in Python?
Use lists, dictionaries, or pandas DataFrames.
20. How to handle imbalanced datasets?
Use class weighting, oversampling, or undersampling techniques during data loading.
---
Summary
Mastering image loading and organization is fundamental for effective data preprocessing in computer vision projects.
---
#Python #ImageProcessing #DatasetHandling #OpenCV #DeepLearning
https://news.1rj.ru/str/DataScience4
❤3
Topic: Data Structures – Trees – Part 1 of 4: Introduction and Binary Trees
---
### 1. What is a Tree in Data Structures?
A tree is a non-linear hierarchical data structure consisting of nodes connected by edges. It's widely used in real-world applications like:
• File systems
• Databases (e.g., B-Trees)
• Compilers (parse trees)
• Artificial intelligence (decision trees)
---
### 2. Terminologies in Trees
• Node: Basic unit of a tree
• Root: Topmost node (only one)
• Parent/Child: A node that connects to another below/above
• Leaf: A node with no children
• Edge: Connection between parent and child
• Subtree: Any child node and its descendants
• Depth: Distance from the root to a node
• Height: Longest path from a node to a leaf
• Degree: Number of children a node has
---
### 3. Binary Tree Basics
A Binary Tree is a tree in which each node has at most two children, referred to as:
• Left child
• Right child
#### Real-life example:
Imagine a family tree where each person can have two children.
---
### 4. Types of Binary Trees
• Full Binary Tree – every node has 0 or 2 children
• Perfect Binary Tree – all internal nodes have 2 children, and all leaves are at the same level
• Complete Binary Tree – all levels are filled except possibly the last, which is filled from left to right
• Skewed Tree – all nodes only have one child (left or right)
• Balanced Binary Tree – the height difference of left and right subtrees is minimal
---
### 5. Binary Tree Representation in Python
Using Class & Object:
---
### 6. Tree Traversals
Tree traversal means visiting all the nodes of a tree in a specific order.
• Inorder (LNR)
• Preorder (NLR)
• Postorder (LRN)
• Level Order (BFS using queue)
#### Example – Inorder Traversal (Left → Root → Right):
---
### 7. Build a Simple Binary Tree and Traverse It
---
### 8. Applications of Binary Trees
• Expression evaluation
• Search algorithms (Binary Search Trees)
• Priority queues (Heaps)
• Huffman encoding trees (data compression)
• Syntax trees in compilers
---
### 9. Key Characteristics
• Recursive nature makes tree problems suitable for recursion
• Not sequential – can't be represented with only arrays or lists
• Memory-efficient using pointers in linked structure
---
### 10. Summary
• Trees are hierarchical and non-linear
• Binary trees limit nodes to max 2 children
• Various types of binary trees serve different use cases
• Tree traversal is fundamental for solving tree problems
---
### Exercise
Create a class-based binary tree and implement:
• Inorder, Preorder, and Postorder traversals
• Function to count total nodes and leaf nodes
---
#DSA #BinaryTree #DataStructures #Python #TreeTraversal
https://news.1rj.ru/str/DataScience4
---
### 1. What is a Tree in Data Structures?
A tree is a non-linear hierarchical data structure consisting of nodes connected by edges. It's widely used in real-world applications like:
• File systems
• Databases (e.g., B-Trees)
• Compilers (parse trees)
• Artificial intelligence (decision trees)
---
### 2. Terminologies in Trees
• Node: Basic unit of a tree
• Root: Topmost node (only one)
• Parent/Child: A node that connects to another below/above
• Leaf: A node with no children
• Edge: Connection between parent and child
• Subtree: Any child node and its descendants
• Depth: Distance from the root to a node
• Height: Longest path from a node to a leaf
• Degree: Number of children a node has
---
### 3. Binary Tree Basics
A Binary Tree is a tree in which each node has at most two children, referred to as:
• Left child
• Right child
#### Real-life example:
Imagine a family tree where each person can have two children.
---
### 4. Types of Binary Trees
• Full Binary Tree – every node has 0 or 2 children
• Perfect Binary Tree – all internal nodes have 2 children, and all leaves are at the same level
• Complete Binary Tree – all levels are filled except possibly the last, which is filled from left to right
• Skewed Tree – all nodes only have one child (left or right)
• Balanced Binary Tree – the height difference of left and right subtrees is minimal
---
### 5. Binary Tree Representation in Python
Using Class & Object:
class Node:
def __init__(self, data):
self.data = data
self.left = None
self.right = None
# Example
root = Node(1)
root.left = Node(2)
root.right = Node(3)
---
### 6. Tree Traversals
Tree traversal means visiting all the nodes of a tree in a specific order.
• Inorder (LNR)
• Preorder (NLR)
• Postorder (LRN)
• Level Order (BFS using queue)
#### Example – Inorder Traversal (Left → Root → Right):
def inorder(root):
if root:
inorder(root.left)
print(root.data, end=" ")
inorder(root.right)
---
### 7. Build a Simple Binary Tree and Traverse It
# Tree Structure:
# 1
# / \
# 2 3
# / \
# 4 5
root = Node(1)
root.left = Node(2)
root.right = Node(3)
root.left.left = Node(4)
root.left.right = Node(5)
print("Inorder Traversal:")
inorder(root) # Output: 4 2 5 1 3
---
### 8. Applications of Binary Trees
• Expression evaluation
• Search algorithms (Binary Search Trees)
• Priority queues (Heaps)
• Huffman encoding trees (data compression)
• Syntax trees in compilers
---
### 9. Key Characteristics
• Recursive nature makes tree problems suitable for recursion
• Not sequential – can't be represented with only arrays or lists
• Memory-efficient using pointers in linked structure
---
### 10. Summary
• Trees are hierarchical and non-linear
• Binary trees limit nodes to max 2 children
• Various types of binary trees serve different use cases
• Tree traversal is fundamental for solving tree problems
---
### Exercise
Create a class-based binary tree and implement:
• Inorder, Preorder, and Postorder traversals
• Function to count total nodes and leaf nodes
---
#DSA #BinaryTree #DataStructures #Python #TreeTraversal
https://news.1rj.ru/str/DataScience4
❤7
Topic: Data Structures – Trees – Part 2 of 4: Binary Search Trees (BST)
---
### 1. What is a Binary Search Tree (BST)?
A Binary Search Tree (BST) is a binary tree where all nodes follow the below properties:
• The left child of a node contains only nodes with values less than the node's value.
• The right child of a node contains only nodes with values greater than the node's value.
• Both left and right subtrees must also be BSTs.
This property allows for efficient searching, insertion, and deletion.
---
### 2. Why Use BSTs?
• Search operations are faster than in linear structures (like lists).
• Ordered traversal becomes very efficient.
• Time complexity (on average):
• Search: O(log n)
• Insert: O(log n)
• Delete: O(log n)
*Worst case is O(n) for skewed trees.*
---
### 3. Implementing a BST in Python
#### Insert Function:
#### Example Tree:
This creates:
---
### 4. Searching in BST
---
### 5. Finding Minimum and Maximum
---
### 6. Deleting a Node in BST
There are 3 cases:
1. Node has no children
2. Node has one child
3. Node has two children
---
### 7. Inorder Traversal of BST
Inorder traversal of a BST gives sorted order:
---
### 8. Time and Space Complexity Summary
| Operation | Best Case | Average Case | Worst Case |
| --------- | --------- | ------------ | ---------- |
| Search | O(log n) | O(log n) | O(n) |
| Insert | O(log n) | O(log n) | O(n) |
| Delete | O(log n) | O(log n) | O(n) |
*Worst case occurs when tree is skewed (e.g., inserting sorted data)*
---
### 9. Applications of BST
• Search engines
• Sorted maps and sets
• Auto-complete features
• Database indexing
• Tree-based dictionaries
---
### 10. Summary
• BST enforces ordering which helps efficient operations
• Insertion, search, and deletion rely on recursive logic
• Traversals help in data processing
• Performance degrades in unbalanced trees — next part will cover balanced trees
---
### Exercise
• Write code to insert, delete, and search in a BST.
• Traverse the BST in inorder, preorder, and postorder.
• Add a function to count number of nodes and leaf nodes.
• Try inserting sorted data and observe tree structure (hint: use print or drawing).
#DSA #DataStructures #Tree #Python
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---
### 1. What is a Binary Search Tree (BST)?
A Binary Search Tree (BST) is a binary tree where all nodes follow the below properties:
• The left child of a node contains only nodes with values less than the node's value.
• The right child of a node contains only nodes with values greater than the node's value.
• Both left and right subtrees must also be BSTs.
This property allows for efficient searching, insertion, and deletion.
---
### 2. Why Use BSTs?
• Search operations are faster than in linear structures (like lists).
• Ordered traversal becomes very efficient.
• Time complexity (on average):
• Search: O(log n)
• Insert: O(log n)
• Delete: O(log n)
*Worst case is O(n) for skewed trees.*
---
### 3. Implementing a BST in Python
class Node:
def __init__(self, key):
self.key = key
self.left = None
self.right = None
#### Insert Function:
def insert(root, key):
if root is None:
return Node(key)
if key < root.key:
root.left = insert(root.left, key)
else:
root.right = insert(root.right, key)
return root
#### Example Tree:
root = None
for val in [50, 30, 70, 20, 40, 60, 80]:
root = insert(root, val)
This creates:
50
/ \
30 70
/ \ / \
20 40 60 80
---
### 4. Searching in BST
def search(root, key):
if root is None or root.key == key:
return root
if key < root.key:
return search(root.left, key)
else:
return search(root.right, key)
---
### 5. Finding Minimum and Maximum
def find_min(root):
while root.left:
root = root.left
return root.key
def find_max(root):
while root.right:
root = root.right
return root.key
---
### 6. Deleting a Node in BST
There are 3 cases:
1. Node has no children
2. Node has one child
3. Node has two children
def delete(root, key):
if root is None:
return root
if key < root.key:
root.left = delete(root.left, key)
elif key > root.key:
root.right = delete(root.right, key)
else:
# Node with only one child or no child
if root.left is None:
return root.right
elif root.right is None:
return root.left
# Node with two children
temp = find_min_node(root.right)
root.key = temp.key
root.right = delete(root.right, temp.key)
return root
def find_min_node(node):
while node.left:
node = node.left
return node
---
### 7. Inorder Traversal of BST
Inorder traversal of a BST gives sorted order:
def inorder(root):
if root:
inorder(root.left)
print(root.key, end=" ")
inorder(root.right)
---
### 8. Time and Space Complexity Summary
| Operation | Best Case | Average Case | Worst Case |
| --------- | --------- | ------------ | ---------- |
| Search | O(log n) | O(log n) | O(n) |
| Insert | O(log n) | O(log n) | O(n) |
| Delete | O(log n) | O(log n) | O(n) |
*Worst case occurs when tree is skewed (e.g., inserting sorted data)*
---
### 9. Applications of BST
• Search engines
• Sorted maps and sets
• Auto-complete features
• Database indexing
• Tree-based dictionaries
---
### 10. Summary
• BST enforces ordering which helps efficient operations
• Insertion, search, and deletion rely on recursive logic
• Traversals help in data processing
• Performance degrades in unbalanced trees — next part will cover balanced trees
---
### Exercise
• Write code to insert, delete, and search in a BST.
• Traverse the BST in inorder, preorder, and postorder.
• Add a function to count number of nodes and leaf nodes.
• Try inserting sorted data and observe tree structure (hint: use print or drawing).
#DSA #DataStructures #Tree #Python
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Topic: Data Structures – Trees – Part 3 of 4: Balanced Trees – AVL Trees & Tree Height Management
---
### 1. Why Balance Matters in Trees
In unbalanced trees, especially skewed trees, operations like search, insert, and delete can degrade to O(n) time complexity.
Balanced trees maintain height close to
---
### 2. AVL Tree – Introduction
An AVL Tree is a self-balancing Binary Search Tree named after inventors Adelson-Velsky and Landis.
Properties:
• For every node, the balance factor (height of left subtree – height of right subtree) must be -1, 0, or +1
• Automatically re-balances after insertions and deletions
---
### 3. Balance Factor Calculation
Cases:
• Balance Factor > 1 → Left-heavy
• Balance Factor < -1 → Right-heavy
---
### 4. Rotations in AVL Trees
To restore balance, AVL trees use rotations:
Single Rotations:
• Right Rotation (LL Case)
• Left Rotation (RR Case)
Double Rotations:
• Left-Right Rotation (LR Case)
• Right-Left Rotation (RL Case)
---
### 5. AVL Tree Node Structure
---
### 6. Helper Functions
---
### 7. Insertion in AVL Tree
---
### 8. Example AVL Tree Construction
After insertion, the AVL Tree balances itself using appropriate rotations.
---
### 9. Traversals
Use any standard traversal to see the output:
---
### 10. Summary
• AVL Trees ensure that BSTs stay balanced
• Balance is maintained using rotations after insertion
• Time complexity for all operations is O(log n)
• AVL Trees are ideal when frequent insertions and deletions are expected
---
### Exercise
• Build an AVL Tree from a set of values
• Add functions for
• Test insertions that trigger all four types of rotations
• Bonus: Implement AVL deletion (complex but possible)
---
#DSA #BalancedTree #BinarySearchTree #Python
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---
### 1. Why Balance Matters in Trees
In unbalanced trees, especially skewed trees, operations like search, insert, and delete can degrade to O(n) time complexity.
Balanced trees maintain height close to
log(n) for efficient performance.---
### 2. AVL Tree – Introduction
An AVL Tree is a self-balancing Binary Search Tree named after inventors Adelson-Velsky and Landis.
Properties:
• For every node, the balance factor (height of left subtree – height of right subtree) must be -1, 0, or +1
• Automatically re-balances after insertions and deletions
---
### 3. Balance Factor Calculation
balance = height(left subtree) - height(right subtree)
Cases:
• Balance Factor > 1 → Left-heavy
• Balance Factor < -1 → Right-heavy
---
### 4. Rotations in AVL Trees
To restore balance, AVL trees use rotations:
Single Rotations:
• Right Rotation (LL Case)
• Left Rotation (RR Case)
Double Rotations:
• Left-Right Rotation (LR Case)
• Right-Left Rotation (RL Case)
---
### 5. AVL Tree Node Structure
class Node:
def __init__(self, key):
self.key = key
self.left = None
self.right = None
self.height = 1
---
### 6. Helper Functions
def get_height(node):
if not node:
return 0
return node.height
def get_balance(node):
if not node:
return 0
return get_height(node.left) - get_height(node.right)
def right_rotate(z):
y = z.left
T3 = y.right
y.right = z
z.left = T3
z.height = 1 + max(get_height(z.left), get_height(z.right))
y.height = 1 + max(get_height(y.left), get_height(y.right))
return y
def left_rotate(z):
y = z.right
T2 = y.left
y.left = z
z.right = T2
z.height = 1 + max(get_height(z.left), get_height(z.right))
y.height = 1 + max(get_height(y.left), get_height(y.right))
return y
---
### 7. Insertion in AVL Tree
def insert(node, key):
if not node:
return Node(key)
if key < node.key:
node.left = insert(node.left, key)
elif key > node.key:
node.right = insert(node.right, key)
else:
return node # no duplicate keys
# Update height
node.height = 1 + max(get_height(node.left), get_height(node.right))
# Get balance factor
balance = get_balance(node)
# Balance the tree
# Case 1 - Left Left
if balance > 1 and key < node.left.key:
return right_rotate(node)
# Case 2 - Right Right
if balance < -1 and key > node.right.key:
return left_rotate(node)
# Case 3 - Left Right
if balance > 1 and key > node.left.key:
node.left = left_rotate(node.left)
return right_rotate(node)
# Case 4 - Right Left
if balance < -1 and key < node.right.key:
node.right = right_rotate(node.right)
return left_rotate(node)
return node
---
### 8. Example AVL Tree Construction
root = None
for val in [10, 20, 30, 40, 50, 25]:
root = insert(root, val)
After insertion, the AVL Tree balances itself using appropriate rotations.
---
### 9. Traversals
Use any standard traversal to see the output:
def inorder(root):
if root:
inorder(root.left)
print(root.key, end=" ")
inorder(root.right)
---
### 10. Summary
• AVL Trees ensure that BSTs stay balanced
• Balance is maintained using rotations after insertion
• Time complexity for all operations is O(log n)
• AVL Trees are ideal when frequent insertions and deletions are expected
---
### Exercise
• Build an AVL Tree from a set of values
• Add functions for
insert, get_height, get_balance, and inorder• Test insertions that trigger all four types of rotations
• Bonus: Implement AVL deletion (complex but possible)
---
#DSA #BalancedTree #BinarySearchTree #Python
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Topic: Data Structures – Trees – Part 4 of 4: Advanced Trees – Red-Black Trees and Trie
---
### 1. Introduction
This part covers two advanced and widely used tree data structures:
• Red-Black Trees – balanced search trees
• Trie (Prefix Tree) – efficient string storage and retrieval
---
### 2. Red-Black Trees (RBT)
---
#### What is a Red-Black Tree?
A Red-Black Tree is a self-balancing Binary Search Tree with extra color property:
* Each node is either red or black
* Root is always black
* Red nodes cannot have red children (no two reds in a row)
* Every path from root to leaves has the same number of black nodes (black-height)
---
#### Why Red-Black Trees?
* Guarantees O(log n) time for insert, delete, and search
* Slightly less rigid balancing than AVL but faster insertion/deletion
* Used in many libraries (e.g., C++ STL map/set)
---
#### Key Properties Recap
1. Every node is red or black
2. Root is black
3. Red nodes have black children
4. Every path from root to null leaves contains same black nodes count
---
#### Basic Operations
* Insertions and deletions are followed by color adjustments and rotations
* Ensures tree remains balanced with Red-Black properties intact
---
### 3. Trie (Prefix Tree)
---
#### What is a Trie?
A Trie is a tree-like data structure used to store a dynamic set of strings, where:
* Each node represents a character
* Path from root to leaf forms a word
* Used for prefix searches efficiently
---
#### Why Use a Trie?
• Fast lookup for words and prefixes
• Auto-complete features
• Spell checking
• IP routing (longest prefix matching)
---
#### Trie Node Structure (Python)
---
#### Basic Operations
Insert Word:
Search Word:
---
### 4. Example Usage of Trie
---
### 5. Advantages and Disadvantages
| Data Structure | Advantages | Disadvantages |
| -------------- | -------------------------------------- | ------------------------------- |
| Red-Black Tree | Balanced, efficient search and update | Complex implementation |
| Trie | Fast prefix search and auto-completion | Memory-heavy with many children |
---
### 6. Summary
* Red-Black Trees and AVL Trees both keep BSTs balanced but with different balancing rules
* Tries are specialized for string data, enabling efficient prefix operations
* Both structures are essential for advanced algorithms and systems
---
### 7. Exercise
• Implement basic Red-Black Tree insertion (research needed)
• Implement delete operation in Trie
• Use Trie to implement a simple autocomplete function
• Compare search time for BST vs Trie on string data sets
---
#DSA #RedBlackTree #Trie #AdvancedTrees #DataStructures #Python
https://news.1rj.ru/str/DataScience4
---
### 1. Introduction
This part covers two advanced and widely used tree data structures:
• Red-Black Trees – balanced search trees
• Trie (Prefix Tree) – efficient string storage and retrieval
---
### 2. Red-Black Trees (RBT)
---
#### What is a Red-Black Tree?
A Red-Black Tree is a self-balancing Binary Search Tree with extra color property:
* Each node is either red or black
* Root is always black
* Red nodes cannot have red children (no two reds in a row)
* Every path from root to leaves has the same number of black nodes (black-height)
---
#### Why Red-Black Trees?
* Guarantees O(log n) time for insert, delete, and search
* Slightly less rigid balancing than AVL but faster insertion/deletion
* Used in many libraries (e.g., C++ STL map/set)
---
#### Key Properties Recap
1. Every node is red or black
2. Root is black
3. Red nodes have black children
4. Every path from root to null leaves contains same black nodes count
---
#### Basic Operations
* Insertions and deletions are followed by color adjustments and rotations
* Ensures tree remains balanced with Red-Black properties intact
---
### 3. Trie (Prefix Tree)
---
#### What is a Trie?
A Trie is a tree-like data structure used to store a dynamic set of strings, where:
* Each node represents a character
* Path from root to leaf forms a word
* Used for prefix searches efficiently
---
#### Why Use a Trie?
• Fast lookup for words and prefixes
• Auto-complete features
• Spell checking
• IP routing (longest prefix matching)
---
#### Trie Node Structure (Python)
class TrieNode:
def __init__(self):
self.children = {}
self.is_end_of_word = False
---
#### Basic Operations
Insert Word:
def insert(root, word):
node = root
for char in word:
if char not in node.children:
node.children[char] = TrieNode()
node = node.children[char]
node.is_end_of_word = True
Search Word:
def search(root, word):
node = root
for char in word:
if char not in node.children:
return False
node = node.children[char]
return node.is_end_of_word
---
### 4. Example Usage of Trie
root = TrieNode()
insert(root, "hello")
insert(root, "helium")
print(search(root, "hello")) # True
print(search(root, "hel")) # False (prefix only)
---
### 5. Advantages and Disadvantages
| Data Structure | Advantages | Disadvantages |
| -------------- | -------------------------------------- | ------------------------------- |
| Red-Black Tree | Balanced, efficient search and update | Complex implementation |
| Trie | Fast prefix search and auto-completion | Memory-heavy with many children |
---
### 6. Summary
* Red-Black Trees and AVL Trees both keep BSTs balanced but with different balancing rules
* Tries are specialized for string data, enabling efficient prefix operations
* Both structures are essential for advanced algorithms and systems
---
### 7. Exercise
• Implement basic Red-Black Tree insertion (research needed)
• Implement delete operation in Trie
• Use Trie to implement a simple autocomplete function
• Compare search time for BST vs Trie on string data sets
---
#DSA #RedBlackTree #Trie #AdvancedTrees #DataStructures #Python
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Topic: Data Structures – Trees – Top 15 Interview Questions with Answers
---
### 1. What is a tree data structure?
A hierarchical structure with nodes connected by edges, having a root node and child nodes with no cycles.
---
### 2. What is the difference between binary tree and binary search tree (BST)?
A binary tree allows up to two children per node; BST maintains order where left child < node < right child.
---
### 3. What are the types of binary trees?
Full, perfect, complete, skewed (left/right), and balanced binary trees.
---
### 4. Explain tree traversal methods.
Inorder (LNR), Preorder (NLR), Postorder (LRN), and Level Order (BFS).
---
### 5. What is a balanced tree? Why is it important?
A tree where the height difference between left and right subtrees is minimal to ensure O(log n) operations.
---
### 6. What is an AVL tree?
A self-balancing BST maintaining balance factor (-1, 0, 1) with rotations to balance after insert/delete.
---
### 7. What are rotations in AVL trees?
Operations (Left, Right, Left-Right, Right-Left) used to rebalance the tree after insertion or deletion.
---
### 8. What is a Red-Black Tree?
A balanced BST with red/black nodes ensuring balance via color rules, offering O(log n) operations.
---
### 9. How does a Trie work?
A tree structure used for storing strings, where nodes represent characters, allowing fast prefix searches.
---
### 10. What is the height of a binary tree?
The number of edges on the longest path from root to a leaf node.
---
### 11. How do you find the lowest common ancestor (LCA) of two nodes?
By traversing from root, checking if nodes lie in different subtrees, or by storing parent pointers.
---
### 12. What is the difference between DFS and BFS on trees?
DFS explores as far as possible along branches; BFS explores neighbors level by level.
---
### 13. How do you detect if a binary tree is a BST?
Check if inorder traversal yields a sorted sequence or verify node values within valid ranges recursively.
---
### 14. What are leaf nodes?
Nodes with no children.
---
### 15. How do you calculate the number of nodes in a complete binary tree?
Using the formula: number\_of\_nodes = 2^(height + 1) - 1 (if perfect), else traverse and count.
---
### Exercise
Write functions for inorder, preorder, postorder traversals, check if tree is BST, and find LCA of two nodes.
---
#DSA #Trees #InterviewQuestions #BinaryTrees #Python #Algorithms
https://news.1rj.ru/str/DataScience4
---
### 1. What is a tree data structure?
A hierarchical structure with nodes connected by edges, having a root node and child nodes with no cycles.
---
### 2. What is the difference between binary tree and binary search tree (BST)?
A binary tree allows up to two children per node; BST maintains order where left child < node < right child.
---
### 3. What are the types of binary trees?
Full, perfect, complete, skewed (left/right), and balanced binary trees.
---
### 4. Explain tree traversal methods.
Inorder (LNR), Preorder (NLR), Postorder (LRN), and Level Order (BFS).
---
### 5. What is a balanced tree? Why is it important?
A tree where the height difference between left and right subtrees is minimal to ensure O(log n) operations.
---
### 6. What is an AVL tree?
A self-balancing BST maintaining balance factor (-1, 0, 1) with rotations to balance after insert/delete.
---
### 7. What are rotations in AVL trees?
Operations (Left, Right, Left-Right, Right-Left) used to rebalance the tree after insertion or deletion.
---
### 8. What is a Red-Black Tree?
A balanced BST with red/black nodes ensuring balance via color rules, offering O(log n) operations.
---
### 9. How does a Trie work?
A tree structure used for storing strings, where nodes represent characters, allowing fast prefix searches.
---
### 10. What is the height of a binary tree?
The number of edges on the longest path from root to a leaf node.
---
### 11. How do you find the lowest common ancestor (LCA) of two nodes?
By traversing from root, checking if nodes lie in different subtrees, or by storing parent pointers.
---
### 12. What is the difference between DFS and BFS on trees?
DFS explores as far as possible along branches; BFS explores neighbors level by level.
---
### 13. How do you detect if a binary tree is a BST?
Check if inorder traversal yields a sorted sequence or verify node values within valid ranges recursively.
---
### 14. What are leaf nodes?
Nodes with no children.
---
### 15. How do you calculate the number of nodes in a complete binary tree?
Using the formula: number\_of\_nodes = 2^(height + 1) - 1 (if perfect), else traverse and count.
---
### Exercise
Write functions for inorder, preorder, postorder traversals, check if tree is BST, and find LCA of two nodes.
---
#DSA #Trees #InterviewQuestions #BinaryTrees #Python #Algorithms
https://news.1rj.ru/str/DataScience4
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Topic: Python – Create IP Address Tracker GUI using Tkinter
---
### What You'll Build
A desktop app that allows the user to:
• Enter an IP address or domain
• Fetch geolocation data (country, city, ISP, etc.)
• Display it in a user-friendly Tkinter GUI
We'll use the
---
### Step-by-Step Code
---
### Requirements
Install the
---
### Exercise
• Enhance the app to export the result to a
• Add a map preview using a web view or link to Google Maps
• Add dark mode toggle for the GUI
---
#Python #Tkinter #IPTracker #Networking #GUI #DesktopApp
https://news.1rj.ru/str/DataScience4
---
### What You'll Build
A desktop app that allows the user to:
• Enter an IP address or domain
• Fetch geolocation data (country, city, ISP, etc.)
• Display it in a user-friendly Tkinter GUI
We'll use the
requests library and a free API like ip-api.com.---
### Step-by-Step Code
import tkinter as tk
from tkinter import messagebox
import requests
# Function to fetch IP information
def track_ip():
ip = entry.get().strip()
if not ip:
messagebox.showwarning("Input Error", "Please enter an IP or domain.")
return
try:
url = f"http://ip-api.com/json/{ip}"
response = requests.get(url)
data = response.json()
if data["status"] == "fail":
messagebox.showerror("Error", data["message"])
return
# Show info
result_text.set(
f"IP: {data['query']}\n"
f"Country: {data['country']}\n"
f"Region: {data['regionName']}\n"
f"City: {data['city']}\n"
f"ZIP: {data['zip']}\n"
f"ISP: {data['isp']}\n"
f"Timezone: {data['timezone']}\n"
f"Latitude: {data['lat']}\n"
f"Longitude: {data['lon']}"
)
except Exception as e:
messagebox.showerror("Error", str(e))
# GUI Setup
app = tk.Tk()
app.noscript("IP Tracker")
app.geometry("400x400")
app.resizable(False, False)
# Widgets
tk.Label(app, text="Enter IP Address or Domain:", font=("Arial", 12)).pack(pady=10)
entry = tk.Entry(app, width=40, font=("Arial", 12))
entry.pack()
tk.Button(app, text="Track IP", command=track_ip, font=("Arial", 12)).pack(pady=10)
result_text = tk.StringVar()
result_label = tk.Label(app, textvariable=result_text, justify="left", font=("Courier", 10))
result_label.pack(pady=10)
app.mainloop()
---
### Requirements
Install the
requests library if not already installed:pip install requests
---
### Exercise
• Enhance the app to export the result to a
.txt or .csv file• Add a map preview using a web view or link to Google Maps
• Add dark mode toggle for the GUI
---
#Python #Tkinter #IPTracker #Networking #GUI #DesktopApp
https://news.1rj.ru/str/DataScience4
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