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8️⃣ programming Languages

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❌ YOU CAN'T USE LAMBDA LIKE THIS IN PYTHON

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)

https://t.iss.one/DataScience4 🔰

Python Cheat Sheet: The Ternary Operator 🚀

Shorten your if/else statements for compact, one-line value selection. It's also known as a conditional expression.

#### 📜 The Standard if/else Block

This 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

---

#### ✅ The Ternary Operator (One-Line if/else)

The same logic can be written in a single, clean line.

Syntax:
value_if_true if condition else value_if_false

Let'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

---

💡 More Examples

The ternary operator is an expression, meaning it returns a value and can be used almost anywhere.

1. Inside a Function return

def 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]

---

🧠 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!):

# This is very hard to read!
x = 10
message = "High" if x > 50 else ("Medium" if x > 5 else "Low")

✅ BETTER (Use a standard 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 ✨

🐍 A visualizer that shows the code's execution

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.

⛓ Link to the service

tags: #useful #python

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Python tip:

To create fields that should not be included in the generated init method, use field(init=False).
This is convenient for computed attributes.

Example below 👇

from dataclasses import dataclass, field

@dataclass
class Rectangle:
    width: int
    height: int
    area: int = field(init=False)

    def __post_init__(self):
        self.area = self.width * self.height

👉 @DataScience4

A collection of useful built-in Python functions that most juniors haven't touched

1️⃣ all and any: a mini rule engine
— all(iterable) returns True if all elements are true
— any(iterable) returns True if at least one is true
Example:

password = "P@ssw0rd123"
checks = [
    len(password) >= 8,
    any(c.isdigit() for c in password),
    any(c.isupper() for c in password),
]

if all(checks):
    print("password is ok")

— You can quickly build readable policy checks without a forest of ifs

2️⃣ enumerate: a handy counter instead of manual indexing
Instead of for i in range(len(list)):

users = ["alice", "bob", "charlie"]
for idx, user in enumerate(users, start=1):
    print(idx, user)

— You get both the index and the value at once. Less chance to shoot yourself in the foot with off-by-one errors

3️⃣ zip: linking multiple sequences

names  = ["alice", "bob", "charlie"]
scores = [10, 20, 15]
for name, score in zip(names, scores):
    print(name, score)

— You can glue several lists into one stream of tuples, do parallel iteration, nicely combine data without manual indices

4️⃣ reversed: reverse without copying

data = [1, 2, 3, 4]
for x in reversed(data):
    print(x)

— reversed returns an iterator, not a new list, which is convenient when you don't want to allocate extra memory

5️⃣ set and frozenset: uniqueness and fast lookup

items = ["a", "b", "a", "c"]
unique = set(items)  # {'a', 'b', 'c'}
if "b" in unique:
    ...

— A great way to kill duplicates and speed up membership checks, plus frozenset is hashable

👩‍💻 @DataScience4

🐍 Fun Illustration of Python List Methods 🎯

https://t.iss.one/DataScience4 ⭐

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