π JaidedAI/EasyOCR β an open-source Python library for Optical Character Recognition (OCR) that's easy to use and supports over 80 languages out of the box.
### π Key Features:
πΈ Extracts text from images and scanned documents β including handwritten notes and unusual fonts
πΈ Supports a wide range of languages like English, Russian, Chinese, Arabic, and more
πΈ Built on PyTorch β uses modern deep learning models (not the old-school Tesseract)
πΈ Simple to integrate into your Python projects
### β Example Usage:
### π Ideal For:
β Text extraction from photos, scans, and documents
β Embedding OCR capabilities in apps (e.g. automated data entry)
π GitHub: https://github.com/JaidedAI/EasyOCR
π Follow us for more: @DataScienceN
#Python #OCR #MachineLearning #ComputerVision #EasyOCR
### π Key Features:
πΈ Extracts text from images and scanned documents β including handwritten notes and unusual fonts
πΈ Supports a wide range of languages like English, Russian, Chinese, Arabic, and more
πΈ Built on PyTorch β uses modern deep learning models (not the old-school Tesseract)
πΈ Simple to integrate into your Python projects
### β Example Usage:
import easyocr
reader = easyocr.Reader(['en', 'ru']) # Choose supported languages
result = reader.readtext('image.png')
### π Ideal For:
β Text extraction from photos, scans, and documents
β Embedding OCR capabilities in apps (e.g. automated data entry)
π GitHub: https://github.com/JaidedAI/EasyOCR
π Follow us for more: @DataScienceN
#Python #OCR #MachineLearning #ComputerVision #EasyOCR
β€3π1π1
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β Uses Segment Anything (SAM) by Meta for object segmentation
β Leverages Inpaint-Anything for realistic background generation
β Works in your browser with an intuitive Gradio UI
#AI #ImageEditing #ComputerVision #Gradio #OpenSource #Python
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β€11
In this comprehensive, step-by-step tutorial, you will learn how to build a real-time folder monitoring and intruder detection system using Python.
Create a background program that:
- Monitors a specific folder on your computer.
- Instantly captures a photo using the webcam whenever someone opens that folder.
- Saves the photo with a timestamp in a secure folder.
- Runs automatically when Windows starts.
- Keeps running until you manually stop it (e.g., via Task Manager or a hotkey).
Read and get code: https://hackmd.io/@husseinsheikho/Build-a-Folder-Monitoring
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π Comprehensive Guide: How to Prepare for an Image Processing Job Interview β 500 Most Common Interview Questions
Let's start: https://hackmd.io/@husseinsheikho/IP
#ImageProcessing #ComputerVision #OpenCV #Python #InterviewPrep #DigitalImageProcessing #MachineLearning #AI #SignalProcessing #ComputerGraphics
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β
β
#Python #OpenCV #Automation #ML #AI #DEEPLEARNING #MACHINELEARNING #ComputerVision
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β€9π4π―1π1
python-docx: Create and Modify Word Documents #python
python-docx is a Python library for reading, creating, and updating Microsoft Word 2007+ (.docx) files.
Installation
Example
https://t.me/DataScienceN π
python-docx is a Python library for reading, creating, and updating Microsoft Word 2007+ (.docx) files.
Installation
pip install python-docx
Example
from docx import Document
document = Document()
document.add_paragraph("It was a dark and stormy night.")
<docx.text.paragraph.Paragraph object at 0x10f19e760>
document.save("dark-and-stormy.docx")
document = Document("dark-and-stormy.docx")
document.paragraphs[0].text
'It was a dark and stormy night.'
https://t.me/DataScienceN π
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π Download a free Python 3 cheat sheet PDF put together by the Real Python team.
π·οΈ #Python
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π Compact Python cheat sheet covering setup, syntax, data types, variables, strings, control flow, functions, classes, errors, and I/O.
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π Compact Python cheat sheet covering setup, syntax, data types, variables, strings, control flow, functions, classes, errors, and I/O.
π·οΈ #Python
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Released Real-Time Voice Cloning utility
Which clones speech and reproduces any phrases with your intonation in just a few seconds of recording.
It runs on #Python, generates speech in real time, and is completely local, no clouds or restrictions.π«
π GitHub: https://github.com/CorentinJ/Real-Time-Voice-Cloning
π https://t.me/CodeProgrammer
Which clones speech and reproduces any phrases with your intonation in just a few seconds of recording.
It runs on #Python, generates speech in real time, and is completely local, no clouds or restrictions.
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β€8
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This GitHub repository is a real treasure trove of free programming books.
Here you'll find hundreds of books on topics like #AI, #blockchain, app development, #game development, #Python #webdevelopment, #promptengineering, and many moreβ
GitHub: https://github.com/EbookFoundation/free-programming-books
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Here you'll find hundreds of books on topics like #AI, #blockchain, app development, #game development, #Python #webdevelopment, #promptengineering, and many more
GitHub: https://github.com/EbookFoundation/free-programming-books
https://t.me/CodeProgrammer
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π4β€3
Forwarded from Python Data Science Jobs & Interviews
1. What is the output of the following code?
2. Which of the following is NOT a valid way to create a dictionary in Python?
A)
B)
C)
D)
3. Write a function that takes a list of integers and returns a new list containing only even numbers.
4. What will be printed by this code?
5. What is the purpose of the
6. Which built-in function can be used to remove duplicates from a list while preserving order?
7. Explain the difference between
8. What does the
9. Write a generator function that yields Fibonacci numbers up to a given limit.
10. What is the output of this code?
11. Which of the following is true about Pythonβs GIL (Global Interpreter Lock)?
A) It allows multiple threads to execute Python bytecode simultaneously.
B) It prevents race conditions in multithreaded programs.
C) It limits CPU-bound multi-threaded performance.
D) It is disabled in PyPy.
12. How would you implement a context manager using a class?
13. What is the result of
14. Write a recursive function to calculate the factorial of a number.
15. What is the difference between
16. Explain how Python handles memory management for objects.
17. What is the output of this code?
18. Describe the use of
19. Write a program that reads a text file and counts the frequency of each word.
20. What is monkey patching in Python and when might it be useful?
#Python #AdvancedPython #ProgrammingTest #CodingChallenge #PythonInterview #PythonDeveloper #CodeQuiz #HighLevelPython #LearnPython #PythonSkills #PythonExpert
By: @DataScienceQ π
x = [1, 2, 3]
y = x
y[0] = 4
print(x)
2. Which of the following is NOT a valid way to create a dictionary in Python?
A)
dict(a=1, b=2) B)
{a: 1, b: 2} C)
dict([('a', 1), ('b', 2)]) D)
{1: 'a', 2: 'b'}3. Write a function that takes a list of integers and returns a new list containing only even numbers.
4. What will be printed by this code?
def func(a, b=[]):
b.append(a)
return b
print(func(1))
print(func(2))
5. What is the purpose of the
__slots__ attribute in a Python class?6. Which built-in function can be used to remove duplicates from a list while preserving order?
7. Explain the difference between
map(), filter(), and reduce() with examples.8. What does the
@staticmethod decorator do in Python?9. Write a generator function that yields Fibonacci numbers up to a given limit.
10. What is the output of this code?
import copy
a = [1, 2, [3, 4]]
b = copy.deepcopy(a)
b[2][0] = 5
print(a[2][0])
11. Which of the following is true about Pythonβs GIL (Global Interpreter Lock)?
A) It allows multiple threads to execute Python bytecode simultaneously.
B) It prevents race conditions in multithreaded programs.
C) It limits CPU-bound multi-threaded performance.
D) It is disabled in PyPy.
12. How would you implement a context manager using a class?
13. What is the result of
bool([]) and why?14. Write a recursive function to calculate the factorial of a number.
15. What is the difference between
is and == in Python?16. Explain how Python handles memory management for objects.
17. What is the output of this code?
class A:
def __init__(self):
self.x = 1
class B(A):
def __init__(self):
super().__init__()
self.y = 2
obj = B()
print(hasattr(obj, 'x') and hasattr(obj, 'y'))
18. Describe the use of
*args and **kwargs in function definitions.19. Write a program that reads a text file and counts the frequency of each word.
20. What is monkey patching in Python and when might it be useful?
#Python #AdvancedPython #ProgrammingTest #CodingChallenge #PythonInterview #PythonDeveloper #CodeQuiz #HighLevelPython #LearnPython #PythonSkills #PythonExpert
By: @DataScienceQ π
β€9
Forwarded from Data Science Jupyter Notebooks
π₯ Trending Repository: best-of-ml-python
π Description: π A ranked list of awesome machine learning Python libraries. Updated weekly.
π Repository URL: https://github.com/lukasmasuch/best-of-ml-python
π Website: https://ml-python.best-of.org
π Readme: https://github.com/lukasmasuch/best-of-ml-python#readme
π Statistics:
π Stars: 22.3K stars
π Watchers: 444
π΄ Forks: 3K forks
π» Programming Languages: Not available
π·οΈ Related Topics:
==================================
π§ By: https://t.me/DataScienceM
π Description: π A ranked list of awesome machine learning Python libraries. Updated weekly.
π Repository URL: https://github.com/lukasmasuch/best-of-ml-python
π Website: https://ml-python.best-of.org
π Readme: https://github.com/lukasmasuch/best-of-ml-python#readme
π Statistics:
π Stars: 22.3K stars
π Watchers: 444
π΄ Forks: 3K forks
π» Programming Languages: Not available
π·οΈ Related Topics:
#python #nlp #data_science #machine_learning #deep_learning #tensorflow #scikit_learn #keras #ml #data_visualization #pytorch #transformer #data_analysis #gpt #automl #jax #data_visualizations #gpt_3 #chatgpt
==================================
π§ By: https://t.me/DataScienceM
β€7
In Python, enhanced
#python #forloops #enumerate #bestpractices
βοΈ @DataScience4
for loops with enumerate() provide both the index and value of items in an iterable, making it ideal for tasks needing positional awareness without manual counters. This is more Pythonic and efficient than using range(len()) for list traversals.fruits = ['apple', 'banana', 'cherry']
for index, fruit in enumerate(fruits):
print(f"{index}: {fruit}")
# Output:
# 0: apple
# 1: banana
# 2: cherry
# With start offset:
for index, fruit in enumerate(fruits, start=1):
print(f"{index}: {fruit}")
# 1: apple
# 2: banana
# 3: cherry
#python #forloops #enumerate #bestpractices
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In Python, lists are versatile mutable sequences with built-in methods for adding, removing, searching, sorting, and moreβcovering all common scenarios like dynamic data manipulation, queues, or stacks. Below is a complete breakdown of all list methods, each with syntax, an example, and output, plus key built-in functions for comprehensive use.
π Adding Elements
β¦ append(x): Adds a single element to the end.
β¦ extend(iterable): Adds all elements from an iterable to the end.
β¦ insert(i, x): Inserts x at index i (shifts elements right).
π Removing Elements
β¦ remove(x): Removes the first occurrence of x (raises ValueError if not found).
β¦ pop(i=-1): Removes and returns the element at index i (default: last).
β¦ clear(): Removes all elements.
π Searching and Counting
β¦ count(x): Returns the number of occurrences of x.
β¦ index(x[, start[, end]]): Returns the lowest index of x in the slice (raises ValueError if not found).
π Ordering and Copying
β¦ sort(key=None, reverse=False): Sorts the list in place (ascending by default; stable sort).
β¦ reverse(): Reverses the elements in place.
β¦ copy(): Returns a shallow copy of the list.
π Built-in Functions for Lists (Common Cases)
β¦ len(lst): Returns the number of elements.
β¦ min(lst): Returns the smallest element (raises ValueError if empty).
β¦ max(lst): Returns the largest element.
β¦ sum(lst[, start=0]): Sums the elements (start adds an offset).
β¦ sorted(lst, key=None, reverse=False): Returns a new sorted list (non-destructive).
These cover all standard operations (O(1) for append/pop from end, O(n) for most others). Use slicing
#python #lists #datastructures #methods #examples #programming
β @DataScience4
π Adding Elements
β¦ append(x): Adds a single element to the end.
lst = [1, 2]
lst.append(3)
print(lst) # Output: [1, 2, 3]
β¦ extend(iterable): Adds all elements from an iterable to the end.
lst = [1, 2]
lst.extend([3, 4])
print(lst) # Output: [1, 2, 3, 4]
β¦ insert(i, x): Inserts x at index i (shifts elements right).
lst = [1, 3]
lst.insert(1, 2)
print(lst) # Output: [1, 2, 3]
π Removing Elements
β¦ remove(x): Removes the first occurrence of x (raises ValueError if not found).
lst = [1, 2, 2]
lst.remove(2)
print(lst) # Output: [1, 2]
β¦ pop(i=-1): Removes and returns the element at index i (default: last).
lst = [1, 2, 3]
item = lst.pop(1)
print(item, lst) # Output: 2 [1, 3]
β¦ clear(): Removes all elements.
lst = [1, 2, 3]
lst.clear()
print(lst) # Output: []
π Searching and Counting
β¦ count(x): Returns the number of occurrences of x.
lst = [1, 2, 2, 3]
print(lst.count(2)) # Output: 2
β¦ index(x[, start[, end]]): Returns the lowest index of x in the slice (raises ValueError if not found).
lst = [1, 2, 3, 2]
print(lst.index(2)) # Output: 1
π Ordering and Copying
β¦ sort(key=None, reverse=False): Sorts the list in place (ascending by default; stable sort).
lst = [3, 1, 2]
lst.sort()
print(lst) # Output: [1, 2, 3]
β¦ reverse(): Reverses the elements in place.
lst = [1, 2, 3]
lst.reverse()
print(lst) # Output: [3, 2, 1]
β¦ copy(): Returns a shallow copy of the list.
lst = [1, 2]
new_lst = lst.copy()
print(new_lst) # Output: [1, 2]
π Built-in Functions for Lists (Common Cases)
β¦ len(lst): Returns the number of elements.
lst = [1, 2, 3]
print(len(lst)) # Output: 3
β¦ min(lst): Returns the smallest element (raises ValueError if empty).
lst = [3, 1, 2]
print(min(lst)) # Output: 1
β¦ max(lst): Returns the largest element.
lst = [3, 1, 2]
print(max(lst)) # Output: 3
β¦ sum(lst[, start=0]): Sums the elements (start adds an offset).
lst = [1, 2, 3]
print(sum(lst)) # Output: 6
β¦ sorted(lst, key=None, reverse=False): Returns a new sorted list (non-destructive).
lst = [3, 1, 2]
print(sorted(lst)) # Output: [1, 2, 3]
These cover all standard operations (O(1) for append/pop from end, O(n) for most others). Use slicing
lst[start:end:step] for advanced extraction, like lst[1:3] outputs ``.#python #lists #datastructures #methods #examples #programming
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In Python, handling CSV files is straightforward using the built-in
#python #csv #pandas #datahandling #fileio #interviewtips
π @DataScience4
csv module for reading and writing tabular data, or pandas for advanced analysisβessential for data processing tasks like importing/exporting datasets in interviews.# Reading CSV with csv module (basic)
import csv
with open('data.csv', 'r') as file:
reader = csv.reader(file)
data = list(reader) # data = [['Name', 'Age'], ['Alice', '30'], ['Bob', '25']]
# Writing CSV with csv module
import csv
with open('output.csv', 'w', newline='') as file:
writer = csv.writer(file)
writer.writerow(['Name', 'Age']) # Header
writer.writerows([['Alice', 30], ['Bob', 25]]) # Data rows
# Advanced: Reading with pandas (handles headers, missing values)
import pandas as pd
df = pd.read_csv('data.csv') # df = DataFrame with columns 'Name', 'Age'
print(df.head()) # Output: First 5 rows preview
# Writing with pandas
df.to_csv('output.csv', index=False) # Saves without row indices
#python #csv #pandas #datahandling #fileio #interviewtips
π @DataScience4
β€4π4
The course gathers up-to-date information on #Python programming and creating advanced AI assistants based on it.
β’ Content: The course includes 9 lectures, supplemented with video materials, detailed presentations, and code examples. Learning to develop AI agents is accessible even for coding beginners.
β’ Topics: The lectures cover topics such as #RAG (Retrieval-Augmented Generation), embeddings, #agents, and the #MCP protocol.
The perfect weekend plan is to dive deep into #AI!
https://github.com/orgs/azure-ai-foundry/discussions/166
https://t.me/CodeProgrammer
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π6β€3π₯1π1
In Python, loops are essential for repeating code efficiently: for loops iterate over known sequences (like lists or ranges) when you know the number of iterations, while loops run based on a condition until it's false (ideal for unknown iteration counts or sentinel values), and nested loops handle multi-dimensional data by embedding one inside anotherβuse break/continue for control, and comprehensions for concise alternatives in interviews.
#python #loops #forloop #whileloop #nestedloops #comprehensions #interviewtips #controlflow
β https://t.me/CodeProgrammer
# For loop: Use for fixed iterations over iterables (e.g., processing lists)
fruits = ["apple", "banana", "cherry"]
for fruit in fruits: # Iterates each element
print(fruit) # Output: apple \n banana \n cherry
for i in range(3): # Numeric sequence (start=0, stop=3)
print(i) # Output: 0 \n 1 \n 2
# While loop: Use when iterations depend on a dynamic condition (e.g., user input, convergence)
count = 0
while count < 3: # Runs as long as condition is True
print(count)
count += 1 # Increment to avoid infinite loop! Output: 0 \n 1 \n 2
# Nested loops: Use for 2D data (e.g., matrices, grids); outer for rows, inner for columns
matrix = [[1, 2], [3, 4]]
for row in matrix: # Outer: each sublist
for num in row: # Inner: elements in row
print(num) # Output: 1 \n 2 \n 3 \n 4
# Control statements: break (exit loop), continue (skip iteration)
for i in range(5):
if i == 2:
continue # Skip 2
if i == 4:
break # Exit at 4
print(i) # Output: 0 \n 1 \n 3
# List comprehension: Concise for loop alternative (use for simple transformations/filtering)
squares = [x**2 for x in range(5) if x % 2 == 0] # Even squares
print(squares) # Output: [0, 4, 16]
#python #loops #forloop #whileloop #nestedloops #comprehensions #interviewtips #controlflow
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Python | Machine Learning | Coding | R
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Discover powerful insights with Python, Machine Learning, Coding, and Rβyour essential toolkit for data-driven solutions, smart alg
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β€2π2
In Python, the
Regex tips: Escape special chars with \ (e.g., . for literal dot); use raw strings (r""); test incrementally to avoid frustrationβcommon pitfalls include forgetting anchors (^/$) or overusing.*. For performance, compile patterns; in interviews, explain your pattern step-by-step for clarity. #python #regex #re_module #patterns #textprocessing #interviews #stringmatching
π± https://t.me/CodeProgrammer
re module handles regular expressions (regex) for pattern matching in stringsβvital for text processing like validating emails, extracting data from logs, or cleaning user input in interviews; it's compiled for efficiency but can be complex, so start simple and test with tools like regex101.com.import re
# Basic search: Find if pattern exists (returns Match object or None)
txt = "The rain in Spain"
match = re.search(r"Spain", txt) # r"" for raw string (avoids escaping issues)
if match:
print(match.group()) # Output: Spain (full match)
print(match.start(), match.end()) # Output: 12 17 (positions)
# findall: Extract all matches as list (non-overlapping)
txt = "The rain in Spain stays mainly in the plain"
emails = re.findall(r"\w+@\w+\.com", "Contact: user1@example.com or user2@test.com")
print(emails) # Output: ['user1@example.com', 'user2@test.com']
# split: Divide string at matches (like str.split but with patterns)
words = re.split(r"\s+", "Hello world\twith spaces") # \s+ matches whitespace
print(words) # Output: ['Hello', 'world', 'with', 'spaces']
# sub: Replace matches (count limits replacements; use \1 for groups)
cleaned = re.sub(r"\d+", "***", "Phone: 123-456-7890 or 098-765-4321", count=1)
print(cleaned) # Output: Phone: *** or 098-765-4321 (first number replaced)
# Metacharacters basics:. (any char except \n), ^ (start), $ (end), * (0+), + (1+),? (0-1)
match = re.search(r"^The.*Spain$", txt) # ^ start, $ end,. any, * 0+ of previous
print(match.group() if match else "No match") # Output: The rain in Spain
# Character classes: \d (digit), \w (word char), [a-z] (range), [^0-9] (not digit)
nums = re.findall(r"\d+", "abc123def456") # \d+ one or more digits
print(nums) # Output: ['123', '456']
words_only = re.findall(r"\w+", "Hello123! World?") # \w+ word chars (alphanum + _)
print(words_only) # Output: ['Hello123', 'World']
# Groups: () capture parts; use for extraction or alternation
date = re.search(r"(\d{4})-(\d{2})-(\d{2})", "Event on 2023-10-27")
if date:
print(date.groups()) # Output: ('2023', '10', '27') (tuples of captures)
print(date.group(1)) # Output: 2023 (first group)
# Alternation: | for OR (e.g., cat|dog)
animals = re.findall(r"cat|dog", "I have a cat and a dog")
print(animals) # Output: ['cat', 'dog']
# Flags: re.IGNORECASE (case-insensitive), re.MULTILINE (^/$ per line)
text = "Spain\nin\nSpain"
matches = re.findall(r"^Spain", text, re.MULTILINE) # ^ matches start of each line
print(matches) # Output: ['Spain', 'Spain']
# Advanced: Greedy vs non-greedy (*? or +?) to match minimal
html = "<div><p>Text</p></div>"
content = re.search(r"<div>.*?</div>", html) #.*? non-greedy (stops at first </div>)
print(content.group()) # Output: <div><p>Text</p></div>
# Edge cases: Empty string, no match
print(re.search(r"a", "")) # Output: None
print(re.findall(r"\d", "no numbers")) # Output: []
# Compile for reuse (faster for multiple uses)
pattern = re.compile(r"\w+@\w+\.com")
email = pattern.search("email@example.com")
print(email.group() if email else "No email") # Output: email@example.com
Regex tips: Escape special chars with \ (e.g., . for literal dot); use raw strings (r""); test incrementally to avoid frustrationβcommon pitfalls include forgetting anchors (^/$) or overusing.*. For performance, compile patterns; in interviews, explain your pattern step-by-step for clarity. #python #regex #re_module #patterns #textprocessing #interviews #stringmatching
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Python | Machine Learning | Coding | R
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