🔥 Advanced Level

# Built-in modules
import math
print(math.pi)          # 3.141592653589793
print(math.sqrt(144))   # 12.0
print(math.factorial(5))  # 120

import random
print(random.randint(1, 100))     # Random number
print(random.choice(["A","B","C"]))  # Random pick
items = [1, 2, 3, 4, 5]
random.shuffle(items)               # Shuffle list

import datetime
now = datetime.datetime.now()
print(now.strftime("%d-%m-%Y %H:%M"))  # 13-02-2026 00:00

import os
print(os.getcwd())        # Current directory
print(os.listdir("."))    # Files in directory

# Creating your own module — save as mymath.py
# def add(a, b): return a + b
# def multiply(a, b): return a * b
# Then: import mymath; mymath.add(5, 3)

# Installing packages with pip
# pip install requests
# pip install pandas numpy matplotlib

import time

# Timer decorator — measures how much time a function takes
def timer(func):
    def wrapper(*args, **kwargs):
        start = time.time()
        result = func(*args, **kwargs)
        end = time.time()
        print(f"⏱️ {func.__name__} took {end-start:.4f}s")
        return result
    return wrapper

@timer
def slow_function():
    time.sleep(1)
    print("Done!")

slow_function()
# Done!
# ⏱️ slow_function took 1.0012s

# Logger decorator
def logger(func):
    def wrapper(*args, **kwargs):
        print(f"📝 Calling: {func.__name__}({args}, {kwargs})")
        result = func(*args, **kwargs)
        print(f"📤 Returned: {result}")
        return result
    return wrapper

@logger
def multiply(a, b):
    return a * b

multiply(5, 3)

# Generator function — memory efficient!
def countdown(n):
    while n > 0:
        yield n
        n -= 1

for num in countdown(5):
    print(num, end=" ")  # 5 4 3 2 1

# Infinite generator
def fibonacci():
    a, b = 0, 1
    while True:
        yield a
        a, b = b, a + b

fib = fibonacci()
for _ in range(10):
    print(next(fib), end=" ")
# 0 1 1 2 3 5 8 13 21 34

# Generator expression (like list comprehension but lazy)
squares_gen = (x**2 for x in range(1000000))
print(next(squares_gen))  # 0  — Does not load everything in memory!

# Lambda — Anonymous one-liner functions
square = lambda x: x ** 2
add = lambda a, b: a + b
print(square(5))   # 25
print(add(3, 7))   # 10

# map() — Apply function to every element
numbers = [1, 2, 3, 4, 5]
squared = list(map(lambda x: x**2, numbers))
print(squared)  # [1, 4, 9, 16, 25]

# filter() — Filter based on condition
evens = list(filter(lambda x: x % 2 == 0, range(1, 21)))
print(evens)  # [2, 4, 6, 8, 10, 12, 14, 16, 18, 20]

# reduce() — Combine all elements
from functools import reduce
total = reduce(lambda a, b: a + b, [1, 2, 3, 4, 5])
print(total)  # 15

# Practical example — Sort by second element
students = [("Rahul", 85), ("Priya", 92), ("Amit", 78)]
students.sort(key=lambda x: x[1], reverse=True)
print(students)  # [('Priya', 92), ('Rahul', 85), ('Amit', 78)]

import re

# Find pattern
text = "My phone: 9876543210, email: test@gmail.com"

# Find phone number
phone = re.search(r"\d{10}", text)
print(phone.group())  # 9876543210

# Find email
email = re.search(r"[\w.]+@[\w.]+", text)
print(email.group())  # test@gmail.com

# Find all numbers
nums = re.findall(r"\d+", "I have 3 cats and 5 dogs")
print(nums)  # ['3', '5']

# Validate email
def is_valid_email(email):
    pattern = r'^[\w.]+@[\w]+\.[\w.]+$'
    return bool(re.match(pattern, email))

print(is_valid_email("test@gmail.com"))   # True
print(is_valid_email("invalid@"))         # False

# Replace text
result = re.sub(r"\d+", "***", "Phone: 12345")
print(result)  # Phone: ***

# pip install requests
import requests

# GET request — Fetch data
response = requests.get("https://jsonplaceholder.typicode.com/users/1")
if response.status_code == 200:
    data = response.json()
    print(f"Name: {data['name']}")
    print(f"Email: {data['email']}")

# Weather API example (pseudo code)
# api_key = "your_api_key"
# url = f"http://api.openweathermap.org/data/2.5/weather?q=Delhi&appid={api_key}"
# weather = requests.get(url).json()
# print(f"Temp: {weather['main']['temp']}K")

# pip install beautifulsoup4
from bs4 import BeautifulSoup
import requests

# Scrape a webpage
url = "https://quotes.toscrape.com/"
response = requests.get(url)
soup = BeautifulSoup(response.text, "html.parser")

# Find all quotes
quotes = soup.find_all("span", class_="text")
authors = soup.find_all("small", class_="author")

for quote, author in zip(quotes[:5], authors[:5]):
    print(f'"{quote.text}" — {author.text}')

import threading
import time

def download_file(filename):
    print(f"⬇️ Downloading {filename}...")
    time.sleep(2)  # Simulate download
    print(f"✅ {filename} downloaded!")

# Without threading — 6 seconds total
# download_file("file1.pdf")
# download_file("file2.pdf")
# download_file("file3.pdf")

# With threading — ~2 seconds total! 🚀
threads = []
for f in ["file1.pdf", "file2.pdf", "file3.pdf"]:
    t = threading.Thread(target=download_file, args=(f,))
    threads.append(t)
    t.start()

for t in threads:
    t.join()
print("All downloads complete! 🎉")

# pip install numpy pandas matplotlib scikit-learn
import numpy as np
import pandas as pd

# NumPy — Fast numerical computing
arr = np.array([1, 2, 3, 4, 5])
print(arr * 2)        # [2, 4, 6, 8, 10]
print(np.mean(arr))   # 3.0
print(np.std(arr))    # 1.414

# Pandas — Data manipulation
data = {
    "Name": ["Rahul", "Priya", "Amit"],
    "Marks": [85, 92, 78],
    "City": ["Delhi", "Mumbai", "Pune"]
}
df = pd.DataFrame(data)
print(df)
print(df.describe())          # Statistics
print(df[df["Marks"] > 80])   # Filter

from sklearn.linear_model import LinearRegression
import numpy as np

# Training data — Study hours vs Marks
hours = np.array([1, 2, 3, 4, 5, 6, 7, 8]).reshape(-1, 1)
marks = np.array([20, 35, 45, 55, 65, 72, 80, 88])

# Train model
model = LinearRegression()
model.fit(hours, marks)

# Predict!
study_hours = 10
predicted_marks = model.predict([[study_hours]])
print(f"📈 {study_hours} hours study → {predicted_marks[0]:.0f} marks predicted!")
# ~100 marks predicted!