Python Data Visualization Cheatsheet For EDA

Closures: Functions That Remember

Closures can be mystifying. Imagine a function inside another function, and the inner function remembers the outer function’s variables, even after the outer function has finished running.

Closures capture variables by reference, which is why beginners often stumble when using loops inside closures. They’re powerful once you understand that the inner function “remembers” its environment.

Context Managers: The “Clean-Up Crew” of Python

Ever forget to close a file and wonder why your program is misbehaving?

Context managers prevent this headache.

When you use with, Python ensures that resources are properly acquired and released automatically. Think of it as hiring a clean-up crew: they take care of the dirty work while you focus on the important tasks.

with open('data.txt') as f:
    data = f.read()
# file is automatically closed here

You don’t have to remember to call f.close(). This small pattern prevents bugs, improves readability, and is a hallmark of professional Python code.

Python Roadmap

Data Cleaning in Python

await Is Not Optional in Async

💻 You’re racing 10 API calls with asyncio… and it still takes 10 seconds. Sound familiar?

async def fetch():
return requests.get(url).json() # ← Blocks the entire event loop

✅ Fix: await every I/O. Swap requests for httpx (same API, truly async).

import httpx
async def fetch():
async with httpx.AsyncClient() as client:
r = await client.get(url)
return r.json()

▶️ Now 10 calls = 1 second.

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👨‍🏫 The Zero to Mastery Python Cheat Sheet is a clean, colorful cheatsheet packed with practical code snippets for everyday tasks like loops, functions, and list comprehension.

🤩 It’s visually organized with clear sections and real examples, which makes it a favorite for beginners and intermediates who want to code faster and smarter.

Decorators Are Not Magic. They’re Callbacks in Disguise

You’ve used @lru_cache to speed up a slow function, and it worked... until your app started eating RAM because the cache never forgot anything.

from functools import lru_cache

@lru_cache
def fib(n):
return fib(n-1) + fib(n-2) # ← Cache grows forever!

👉Here’s what’s really happening:
A decorator is just a function that wraps another function. When you write @lru_cache, Python replaces your fib with a new version that remembers every answer it’s ever given. Cool😄 until n goes from 1 to 100,000.

✅ Fix it like a pro:
from functools import lru_cache

@lru_cache(maxsize=128) # Only keep last 128 results
def fib(n):
if n > 1000:
return manual_calc(n) # Skip cache for huge inputs
return fib(n-1) + fib(n-2)

Now the cache stays small, predictable, and safe.

📌Bonus: Write your own @timerdecorator in 5 lines. no more time.time() spam.

🔥 Python vs SQL: Who Cleans Data Better? 🧹

Python Data Structures: Quick Visual Guide 🐍

🔹 Lists: Ordered, mutable, created with [ ]
→ Access/modify via index: myList[0], myList[-1]
→ Methods: .append(), .sort(), .pop()
→ Mixed types allowed
→ Loop: for item in myList:

🔹 Tuples: Immutable, ordered → (1, 2, 3)

🔹 Sets: Unordered, unique elements

🔹 Dictionaries: Key-value pairs, fast lookups

🔹 Arrays: Mainly for numeric data (array/NumPy)

🔑 Key Points:
✅ Indexing: 0 to len-1 (forward), -1 backward
✅ Assignment myList[i] = x modifies in place
✅ Lists are the most versatile & commonly used

This is the perfect cheat sheet for beginners and for quick revision!