Python Tip.
The
Example👇
Here's how it would look without
dataclass saves time and eliminates boilerplate code when a class simply stores data.
👉 @DataScience4
The
@dataclass decorator automatically generates standard methods like init, repr, and eq based on the class attributes.Example
# with dataclass
from dataclasses import dataclass
@dataclass
class Point:
x: int
y: int
Here's how it would look without
@dataclass:class Point:
def __init__(self, x: int, y: int):
self.x = x
self.y = y
def __repr__(self):
return f"Point(x={self.x}, y={self.y})"
def __eq__(self, other):
return (self.x, self.y) == (other.x, other.y)
dataclass saves time and eliminates boilerplate code when a class simply stores data.
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Mastering Python Generators
Of course! Let's master Python generators. This guide will take you from the absolute basics to advanced, practical applications with 20 distinct examples. 🔰 Mastering Python Generators What is a Generator?
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✖️ MODIFYING A LIST WHILE LOOPING OVER IT SKIPS ITEMS.
Because of this, Python's iterator gets confused. When you remove an element, the next element shifts into its place, but the loop moves on to the next index, causing the shifted element to be skipped entirely.
The code looks logical, but the result is buggy — a classic iteration trap.
Correct — iterate over a copy* of the list, or build a new list.
Follow for more Python tips daily!
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By: @DataScience4✨
Because of this, Python's iterator gets confused. When you remove an element, the next element shifts into its place, but the loop moves on to the next index, causing the shifted element to be skipped entirely.
The code looks logical, but the result is buggy — a classic iteration trap.
Correct — iterate over a copy* of the list, or build a new list.
Follow for more Python tips daily!
# hidden error — removing items while iterating skips elements
numbers = [1, 2, 3, 2, 4, 2, 5]
for num in numbers:
if num == 2:
numbers.remove(num) # seems like it should remove all 2s
# a '2' was skipped and remains in the list!
print(numbers) # [1, 3, 4, 2, 5]
# 🖕 correct version — iterate over a copy
numbers_fixed = [1, 2, 3, 2, 4, 2, 5]
# The [:] makes a crucial copy!
for num in numbers_fixed[:]:
if num == 2:
numbers_fixed.remove(num)
print(numbers_fixed) # [1, 3, 4, 5]
# A more Pythonic way is to use a list comprehension:
# [n for n in numbers if n != 2]
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By: @DataScience4
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✨ Lazy Imports Land in Python and Other Python News for December 2025 ✨
📖 PEP 810 brings lazy imports to Python 3.15, PyPI tightens 2FA security, and Django 6.0 reaches release candidate. Catch up on all the important Python news!
🏷️ #community #news
📖 PEP 810 brings lazy imports to Python 3.15, PyPI tightens 2FA security, and Django 6.0 reaches release candidate. Catch up on all the important Python news!
🏷️ #community #news
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🔰 For Loop In Python (10 Best Tips & Tricks)
Here are 10 tips to help you write cleaner, more efficient, and more "Pythonic"
---
1️⃣. Use
Instead of using
---
2️⃣. Use
To loop through two or more lists at the same time,
---
3️⃣. Iterate Directly Over Dictionaries with
To get both the key and value from a dictionary, use the
---
4️⃣. Use List Comprehensions for Simple Loops
If your
---
5️⃣. Use the
If you need to loop a certain number of times but don't care about the loop variable, use
---
6️⃣. Unpack Tuples Directly in the Loop
If you're iterating over a list of tuples or lists, you can unpack the values directly into named variables for better readability.
---
7️⃣. Use
A
---
8️⃣. Iterate Over a Copy to Safely Modify
Never modify a list while you are iterating over it directly. This can lead to skipped items. Instead, iterate over a copy.
---
9️⃣. Use
To loop over a sequence in reverse, use the built-in
Here are 10 tips to help you write cleaner, more efficient, and more "Pythonic"
for loops.---
1️⃣. Use
enumerate() for Index and ValueInstead of using
range(len(sequence)) to get an index, enumerate gives you both the index and the item elegantly.# Less Pythonic 👎
items = ["a", "b", "c"]
for i in range(len(items)):
print(i, items[i])
# More Pythonic 👍
for i, item in enumerate(items):
print(i, item)
---
2️⃣. Use
zip() to Iterate Over Multiple ListsTo loop through two or more lists at the same time,
zip() is the perfect tool. It stops when the shortest list runs out.names = ["Alice", "Bob", "Charlie"]
ages = [25, 30, 35]
for name, age in zip(names, ages):
print(f"{name} is {age} years old.")
---
3️⃣. Iterate Directly Over Dictionaries with
.items()To get both the key and value from a dictionary, use the
.items() method. It's much cleaner than accessing the key and then looking up the value.# Less Pythonic 👎
config = {"host": "localhost", "port": 8080}
for key in config:
print(key, "->", config[key])
# More Pythonic 👍
for key, value in config.items():
print(key, "->", value)
---
4️⃣. Use List Comprehensions for Simple Loops
If your
for loop just creates a new list, a list comprehension is almost always a better choice. It's more concise and often faster.# Standard for loop
squares = []
for i in range(5):
squares.append(i * i)
# squares -> [0, 1, 4, 9, 16]
# List comprehension 👍
squares_comp = [i * i for i in range(5)]
# squares_comp -> [0, 1, 4, 9, 16]
---
5️⃣. Use the
_ Underscore for Unused VariablesIf you need to loop a certain number of times but don't care about the loop variable, use
_ as a placeholder by convention.# I don't need 'i', I just want to repeat 3 times
for _ in range(3):
print("Hello!")
---
6️⃣. Unpack Tuples Directly in the Loop
If you're iterating over a list of tuples or lists, you can unpack the values directly into named variables for better readability.
points = [(1, 2), (3, 4), (5, 6)]
# Unpacking directly into x and y
for x, y in points:
print(f"x: {x}, y: {y}")
---
7️⃣. Use
break and a for-else BlockA
for loop can have an else block that runs only if the loop completes without hitting a break. This is perfect for search operations.numbers = [1, 3, 5, 7, 9]
for num in numbers:
if num % 2 == 0:
print("Even number found!")
break
else: # This runs only if the 'break' was never hit
print("No even numbers in the list.")
---
8️⃣. Iterate Over a Copy to Safely Modify
Never modify a list while you are iterating over it directly. This can lead to skipped items. Instead, iterate over a copy.
# This will not work correctly! 👎
numbers = [1, 2, 3, 2, 4]
for num in numbers:
if num == 2:
numbers.remove(num) # Skips the second '2'
# Correct way: iterate over a slice copy [:] 👍
numbers = [1, 2, 3, 2, 4]
for num in numbers[:]:
if num == 2:
numbers.remove(num)
print(numbers) # [1, 3, 4]
---
9️⃣. Use
reversed() for Reverse IterationTo loop over a sequence in reverse, use the built-in
reversed() function. It's more readable and efficient than creating a reversed slice.❤2👍1
# Less readable
items = ["a", "b", "c"]
for item in items[::-1]:
print(item)
# More readable 👍
for item in reversed(items):
print(item)
---
🔟. Use
continue to Skip the Rest of an IterationThe
continue keyword ends the current iteration and moves to the next one. It's great for skipping items that don't meet a condition, reducing nested if statements.# Using 'if'
for i in range(10):
if i % 2 == 0:
print(i, "is even")
# Using 'continue' can be cleaner
for i in range(10):
if i % 2 != 0:
continue # Skip odd numbers
print(i, "is even")
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By: @DataScience4 ✨
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This channels is for Programmers, Coders, Software Engineers.
0️⃣ Python
1️⃣ Data Science
2️⃣ Machine Learning
3️⃣ Data Visualization
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The main mistake is turning lambda into a logic dump: adding side effects, print calls, long conditions, and calculations to it.
Such lambdas are hard to read, impossible to debug properly, and they violate the very idea of being a short and clean function. Everything complex should be moved into a regular function. Subscribe for more tips every day !
# you can't do this - lambda with state changes
data = [1, 2, 3]
logs = []
# dangerous antipattern
process = lambda x: logs.append(f"processed {x}") or (x * 10)
result = [process(n) for n in data]
print("RESULT:", result)
print("LOGS:", logs)
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Python Cheat Sheet: The Ternary Operator 🚀
Shorten your
####📜 The Standard
This is the classic, multi-line way to assign a value based on a condition.
---
####✅ The Ternary Operator (One-Line
The same logic can be written in a single, clean line.
Syntax:
Let's rewrite the example above:
---
💡 More Examples
The ternary operator is an expression, meaning it returns a value and can be used almost anywhere.
1. Inside a Function
2. Inside an f-string or
3. With List Comprehensions (Advanced)
This is where it becomes incredibly powerful for creating new lists.
---
🧠 When to Use It (and When Not To!)
• DO use it for simple, clear, and readable assignments. If it reads like a natural sentence, it's a good fit.
• DON'T use it for complex logic or nest them. It quickly becomes unreadable.
❌ BAD EXAMPLE (Avoid This!):
✅ BETTER (Use a standard
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By: @DataScience4✨
Shorten your
if/else statements for compact, one-line value selection. It's also known as a conditional expression.####
if/else BlockThis is the classic, multi-line way to assign a value based on a condition.
# Check if a user is an adult
age = 20
status = ""
if age >= 18:
status = "Adult"
else:
status = "Minor"
print(status)
# Output: Adult
---
####
if/else)The same logic can be written in a single, clean line.
Syntax:
value_if_true if condition else value_if_falseLet's rewrite the example above:
age = 20
# Assign 'Adult' if age >= 18, otherwise assign 'Minor'
status = "Adult" if age >= 18 else "Minor"
print(status)
# Output: Adult
---
The ternary operator is an expression, meaning it returns a value and can be used almost anywhere.
1. Inside a Function
returndef get_fee(is_member):
# Return 5 if they are a member, otherwise 15
return 5.00 if is_member else 15.00
print(f"Your fee is: ${get_fee(True)}")
# Output: Your fee is: $5.0
print(f"Your fee is: ${get_fee(False)}")
# Output: Your fee is: $15.0
2. Inside an f-string or
print()is_logged_in = False
print(f"User status: {'Online' if is_logged_in else 'Offline'}")
# Output: User status: Offline
3. With List Comprehensions (Advanced)
This is where it becomes incredibly powerful for creating new lists.
numbers = [1, 10, 5, 22, 3, -4]
# Create a new list labeling each number as "even" or "odd"
labels = ["even" if n % 2 == 0 else "odd" for n in numbers]
print(labels)
# Output: ['odd', 'even', 'odd', 'even', 'odd', 'even']
# Create a new list of only positive numbers, or 0 for negatives
sanitized = [n if n > 0 else 0 for n in numbers]
print(sanitized)
# Output: [1, 10, 5, 22, 3, 0]
---
• DO use it for simple, clear, and readable assignments. If it reads like a natural sentence, it's a good fit.
• DON'T use it for complex logic or nest them. It quickly becomes unreadable.
# This is very hard to read!
x = 10
message = "High" if x > 50 else ("Medium" if x > 5 else "Low")
if/elif/else for clarity):x = 10
if x > 50:
message = "High"
elif x > 5:
message = "Medium"
else:
message = "Low"
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By: @DataScience4
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"Data Structures and Algorithms in Python"
In this book, which is over 300 pages long, all the main data structures and algorithms are excellently explained.
There are versions for both C++ and Java.
Here's a copy for Python
https://t.me/DataScience4✅
In this book, which is over 300 pages long, all the main data structures and algorithms are excellently explained.
There are versions for both C++ and Java.
Here's a copy for Python
https://t.me/DataScience4
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🌟 Join @DeepLearning_ai & @MachineLearning_Programming! 🌟
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The tool allows you to run code directly in the browser and see its step-by-step execution: object creation, reference modification, call stack operation, and data movement between memory areas.
There's also a built-in AI assistant, which you can ask to explain why the code behaves the way it does, or to break down an incomprehensible piece of someone else's solution.
tags: #useful #python
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✨ PyInstaller | Python Tools ✨
📖 A freezing tool that bundles Python applications for distribution.
🏷️ #Python
📖 A freezing tool that bundles Python applications for distribution.
🏷️ #Python
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