Code With Python – Telegram
Code With Python
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This channel delivers clear, practical content for developers, covering Python, Django, Data Structures, Algorithms, and DSA – perfect for learning, coding, and mastering key programming skills.
Admin: @HusseinSheikho || @Hussein_Sheikho
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function calling | AI Coding Glossary

📖 A model feature that lets the model choose a tool and emit JSON arguments so your app runs the API call and returns results.

🏷️ #Python
Topic: Editors & IDEs

📖 Discover popular and niche editors and integrated development environments (IDEs) for Python. Learn about new tools or delve deeper into your favorite editor. This knowledge will streamline your Python development process.

🏷️ #21_resources
Topic: Python DevOps Tutorials

📖 Explore Python as a tool for development and operations (DevOps) tasks. This knowledge will allow you to streamline the process of application development, deployment, and monitoring.

🏷️ #30_resources
Forwarded from PyData Careers
Interview Question

What is the difference between __str__ and __repr__ methods in Python classes, and when would you implementstr
__str__ returns a human-readable string representation of an object (e.g., via print(obj)), making it user-friendly for displayrepr__repr__ aims for a more detailed, unambiguous string that's ideally executable as code (like repr(obj)), useful for debugging—imstr __str__ for end-user outrepr__repr__ for developer tools or str __str__ is defined.

tags: #interview #python #magicmethods #classes

➡️ @DataScienceQ 🤎
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Topic: Python Data Visualization

📖 Learn to create data visualizations using Python in these tutorials. Explore various libraries and use them to communicate your data visually with Python. By mastering data visualization, you can effectively present complex data in an understandable format.

🏷️ #25_resources
Topic: Data Structures

📖 Learn about Python's built-in data structures and how to implement abstract structures like stacks, queues, hash tables, etc. Understanding these will enhance your problem-solving skills in Python and equip you with additional tools in your Python tool belt.

🏷️ #39_resources
Topic: Python Community Articles

📖 Get to know your fellow coders through Python community articles and interviews. Dive into the Python community. This will connect you with the broader Python community, opening opportunities for collaboration and learning.

🏷️ #106_resources
Topic: Python Career

📖 Practice your Python skills for coding interviews and explore Python-related topics that can boost your career as a developer. By following these tutorials, you can improve your chances of succeeding in a Python programming career.

🏷️ #21_resources
Topic: Python Testing Tutorials

📖 Learn how to test different types of Python applications, from command-line apps to web applications. Discover best practices and techniques for testing your Python applications. This will help you build robust and bug-free applications.

🏷️ #25_resources
Topic: Core Python Tutorials

📖 Explore pure Python tutorials focusing on the core language features. Dive into the heart of the Python language. Understanding these core features will give you a solid foundation for advanced Python programming.

🏷️ #586_resources
Topic: Python Projects

📖 Explore project-based Python tutorials and gain practical coding skills. Work on Python projects that help you gain real-world programming experience. These projects include full source code and step-by-step instructions, and will make you more confident in tackling real-world coding challenges.

🏷️ #68_resources
Topic: NumPy

📖 Master NumPy so you can perform complex mathematical operations on large data sets. NumPy is an industry-standard Python library that supports large multidimensional arrays and matrices, and mathematical functions to operate on them.

🏷️ #32_resources
Topic: Python GUI Programming

📖 Learn to create GUIs using various Python frameworks. From Tkinter to PyQT or wxPython, get started with GUI programming in Python. With these skills, you can develop user-friendly interfaces for your applications.

🏷️ #25_resources
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In Python, you can unpack sequences using *, to work with a variable number of elements. The * can be placed anywhere and it will collect all the extra elements into a separate variable.

a, b, c = 10, 2, 3      # Standard unpacking

a, *b = 10, 2, 3        # b = [2, 3]

a, *b, c = 10, 2, 3, 4  # b = [2, 3]

*a, b, c = 10, 2, 3, 4  # a = [10, 2]


👉  @DataScience4
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In Python, generators are memory-efficient iterables created with functions using yield instead of return, allowing lazy evaluation for large datasets or infinite sequences. They're ideal for advanced scenarios like streaming data or coroutines.

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

# Usage: list(fibonacci(10)) -> [0, 1, 1, 2, 3, 5, 8, 13, 21, 34]


🆘 @DataScience4
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In Python, enhanced 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

✉️ @DataScience4
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In Python, lists are versatile mutable sequences with built-in methods for adding, removing, searching, sorting, and more—covering 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.

📚 Adding Elements
append(x): Adds a single element to the end.

  lst = [1, 2]
lst.append(3)
print(lst) # Output: [1, 2, 3]


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]


insert(i, x): Inserts x at index i (shifts elements right).

  lst = [1, 3]
lst.insert(1, 2)
print(lst) # Output: [1, 2, 3]


📚 Removing Elements
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]


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]


clear(): Removes all elements.

  lst = [1, 2, 3]
lst.clear()
print(lst) # Output: []


📚 Searching and Counting
count(x): Returns the number of occurrences of x.

  lst = [1, 2, 2, 3]
print(lst.count(2)) # Output: 2


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


📚 Ordering and Copying
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]


reverse(): Reverses the elements in place.

  lst = [1, 2, 3]
lst.reverse()
print(lst) # Output: [3, 2, 1]


copy(): Returns a shallow copy of the list.

  lst = [1, 2]
new_lst = lst.copy()
print(new_lst) # Output: [1, 2]


📚 Built-in Functions for Lists (Common Cases)
len(lst): Returns the number of elements.

  lst = [1, 2, 3]
print(len(lst)) # Output: 3


min(lst): Returns the smallest element (raises ValueError if empty).

  lst = [3, 1, 2]
print(min(lst)) # Output: 1


max(lst): Returns the largest element.

  lst = [3, 1, 2]
print(max(lst)) # Output: 3


sum(lst[, start=0]): Sums the elements (start adds an offset).

  lst = [1, 2, 3]
print(sum(lst)) # Output: 6


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

@DataScience4
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In Python, list comprehensions provide a concise way to create lists by applying an expression to each item in an iterable, often with conditions—making code more readable and efficient for tasks like filtering or transforming data, a frequent interview topic for assessing Pythonic style.

# Basic comprehension
squares = [x**2 for x in range(5)] # [0, 1, 4, 9, 16]

# With condition
evens = [x for x in range(10) if x % 2 == 0] # [0, 2, 4, 6, 8]

# Nested with transformation
matrix = [[1, 2], [3, 4]]
flattened = [num for row in matrix for num in row] # [1, 2, 3, 4]

# Equivalent to loop (interview comparison)
result = []
for x in range(5):
result.append(x**2)
# result = [0, 1, 4, 9, 16] # Same as first example


#python #listcomprehensions #interviewtips #pythonic #datastructures

👉 @DataScience4
In Python programming exams, follow these structured steps to solve problems methodically, staying focused and avoiding panic: Start by reading the problem twice to clarify inputs, outputs, and constraints—write them down simply. Break it into small sub-problems (e.g., "handle edge cases first"), plan pseudocode or a flowchart on paper, then implement step-by-step with test cases for each part, debugging one issue at a time while taking deep breaths to reset if stuck.

# Example: Solve "Find max in list" problem step-by-step
# Step 1: Understand - Input: list of nums; Output: max value; Constraints: empty list?

def find_max(numbers):
if not numbers: # Step 2: Handle edge case (empty list)
return None # Or raise ValueError

max_val = numbers # Step 3: Initialize with first element
for num in numbers[1:]: # Step 4: Loop through rest (sub-problem: compare)
if num > max_val:
max_val = num
return max_val # Step 5: Return result

# Step 6: Test cases
print(find_max([3, 1, 4, 1, 5])) # Output: 5
print(find_max([])) # Output: None
print(find_max()) # Output: 10

# If stuck: Comment code to trace, or simplify (e.g., use max() built-in first to verify)


This approach builds confidence—practice on platforms like LeetCode to make it habit! #python #problemsolving #codingexams #debugging #interviewtips

👉 @DataScience4
🔥2
In Python, for loops are versatile for iterating over iterables like lists, strings, or ranges, but advanced types include basic iteration, index-aware with enumerate(), parallel with zip(), nested for multi-level data, and comprehension-based—crucial for efficient data processing in interviews without overcomplicating.

# Basic for loop over iterable (list)
fruits = ["apple", "banana", "cherry"]
for fruit in fruits: # Iterates each element directly
print(fruit) # Output: apple \n banana \n cherry

# For loop with range() for numeric sequences
for i in range(3): # Generates 0, 1, 2 (start=0, stop=3, step=1)
print(i) # Output: 0 \n 1 \n 2

for i in range(1, 6, 2): # Start=1, stop=6, step=2
print(i) # Output: 1 \n 3 \n 5

# Index-aware with enumerate() (gets both index and value)
for index, fruit in enumerate(fruits, start=1): # start=1 for 1-based indexing
print(f"{index}: {fruit}") # Output: 1: apple \n 2: banana \n 3: cherry

# Parallel iteration with zip() (pairs multiple iterables)
names = ["Alice", "Bob", "Charlie"]
ages = [25, 30, 35]
for name, age in zip(names, ages): # Stops at shortest iterable
print(f"{name} is {age} years old") # Output: Alice is 25 years old \n Bob is 30 years old \n Charlie is 35 years old

# Nested for loops (outer for rows, inner for columns; e.g., matrix)
matrix = [[1, 2, 3], [4, 5, 6], [7, 8, 9]]
for row in matrix: # Outer: each sublist
for num in row: # Inner: each element in row
print(num, end=' ') # Output: 1 2 3 4 5 6 7 8 9 (space-separated)

# For loop in list comprehension (concise iteration with optional condition)
squares = [x**2 for x in range(5)] # Basic comprehension
print(squares) # Output: [0, 1, 4, 9, 16]

evens_squared = [x**2 for x in range(10) if x % 2 == 0] # With condition (if)
print(evens_squared) # Output: [0, 4, 16, 36, 64]

# Nested comprehension (flattens 2D list)
flattened = [num for row in matrix for num in row] # Equivalent to nested for
print(flattened) # Output: [1, 2, 3, 4, 5, 6, 7, 8, 9]


#python #forloops #range #enumerate #zip #nestedloops #listcomprehension #interviewtips #iteration

👉 @DataScience4
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How to Serve a Website With FastAPI Using HTML and Jinja2

📖 Use FastAPI to render Jinja2 templates and serve dynamic sites with HTML, CSS, and JavaScript, then add a color picker that copies hex codes.

🏷️ #intermediate #api #front-end #web-dev
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