Use comprehensions and functional tools appropriately.
PART 3 • LEVEL 12
⚡ Advanced Python & Placement
Write concise Python, reason about efficiency and turn interview requirements into correct, testable solutions.
numbers = [4, 7, 4, 9, 7, 2]
unique_sorted = sorted(set(numbers))
second_largest = unique_sorted[-2]
print(second_largest)PLACEMENT HABITUnderstand → Plan → Code → Verify
LEARNING OUTCOMES
By the End of This Level
You will combine Python fluency with interview-ready problem solving.
Explain generators, decorators and regular expressions.
Select useful standard-library collections and data models.
Analyse complexity and communicate a solution clearly.
01
CONCISE TRANSFORMATION
Comprehensions
Comprehensions build collections from an iterable using an expression and optional condition.
EXAMPLE 1
Squares of Even Numbers
numbers = [1, 2, 3, 4, 5, 6]
squares = [n * n for n in numbers if n % 2 == 0]
print(squares)Output
[4, 16, 36]
Keep it readable
Use a normal loop when the comprehension needs several conditions, side effects or difficult-to-read nested logic.
02
FUNCTIONS AS VALUESLambda,
Lambda, map(), filter() and sorted()
Python can pass functions as values, enabling compact transformations, selections and custom ordering.
| Tool | Purpose | Typical expression |
|---|---|---|
lambda | Create a small anonymous function | lambda x: x * x |
map() | Transform every item lazily | map(str.upper, names) |
filter() | Keep items satisfying a predicate | filter(is_valid, data) |
sorted() | Return a sorted list | sorted(rows, key=lambda r: r[1]) |
EXAMPLE 2
Sort Students by Score
students = [("Asha", 82), ("Ravi", 75), ("Bhavya", 91)]
ranked = sorted(students, key=lambda row: row[1], reverse=True)
print(ranked)First item
('Bhavya', 91)03
LAZY DATA FLOW
Iterators and Generators
An iterator supplies one value at a time; a generator creates an iterator using yield.
Finish the function
return sends one result and ends the current function call.
def square(n):
return n * nPause and resume
yield produces a value while preserving the generator’s execution state.
def countdown(n):
while n:
yield n
n -= 1Why generators matter
They can process large streams without constructing the complete result in memory first.
04
WRAP BEHAVIOUR
Decorators
A decorator receives a callable and returns a callable, adding behaviour without rewriting the original function body.
EXAMPLE 3
Log a Function Call
def log_call(function):
def wrapper(*args, **kwargs):
print("Calling", function.__name__)
return function(*args, **kwargs)
return wrapper
@log_call
def greet(name):
return f"Hello, {name}"Decoration
greet = log_call(greet)
Preserve metadata
Use functools.wraps inside production decorators so the wrapped function retains its name and documentation.
05
MATCH TEXT PATTERNS
Regular Expressions
The re module searches, validates, extracts and replaces text using a compact pattern language.
| Function | Use | Important behaviour |
|---|---|---|
re.fullmatch() | Validate the entire string | Best for complete-format checks |
re.search() | Find the first match anywhere | Returns a match object or None |
re.findall() | Collect non-overlapping matches | Returns a list |
re.sub() | Replace matching text | Returns a new string |
EXAMPLE 4
Validate a College Email
import re
email = "[email protected]"
pattern = r"[A-Za-z0-9._%+-]+@college\.edu"
print(bool(re.fullmatch(pattern, email)))Output
True
06
USE THE STANDARD LIBRARY
collections and itertools
Standard-library tools express common counting, grouping, queue and iteration patterns reliably.
Counter
Counts hashable values and exposes the most common items.
defaultdict
Creates a default value automatically for a missing key.
deque
Supports efficient insertion and removal at both ends.
itertools
Provides efficient iterator building blocks such as combinations.
Interview advantage
State the complexity benefit of the selected structure; do not use an advanced tool only to shorten the code.
07
COMMUNICATE INTENT
Type Hints and Dataclasses
Type hints document expected values, while @dataclass generates common data-model methods.
EXAMPLE 5
Represent a Candidate
from dataclasses import dataclass
@dataclass
class Candidate:
name: str
score: int
def qualified(candidate: Candidate) -> bool:
return candidate.score >= 70Generated automatically
__init__ __repr__ __eq__
Hints are not runtime enforcement
Tools such as editors and static type checkers use annotations, but Python normally does not reject a value only because its type differs from a hint.
08
SOLVE UNDER INTERVIEW CONDITIONS
Placement Problem-Solving Strategy
A strong solution is correct, explainable, efficient for the constraints and verified with meaningful test cases.
Growth of operations
Explain the dominant work using notation such as O(n) or O(n log n).
Extra memory used
Include auxiliary collections, recursion depth and created copies—not the input itself unless requested.
COMMON MISTAKES
Placement Errors to Avoid
Coding before clarifying requirements
Confirm input format, duplicate handling, case sensitivity and expected output first.
Ignoring edge cases
Test empty input, one item, duplicates, negative values and already sorted data where relevant.
Claiming the wrong complexity
Remember that sorting is normally O(n log n) and nested full scans are commonly O(n²).
Writing clever but unclear code
Prefer meaningful names and a solution you can confidently dry-run and explain.
📌 Advanced Python — Quick Revision
Review these ten final rules before attempting the quiz and placement problems.
Use comprehensions for clear one-step collection building.
A lambda contains one expression and returns its value.
sorted() returns a new list; list.sort() mutates a list.
An iterator supplies values one at a time.
yield pauses a generator and preserves its state.
A decorator wraps one callable with additional behaviour.
Use raw strings for readable regular-expression patterns.
Counter is useful for frequency problems.
Type hints communicate intent but normally do not enforce types.
Verify correctness and complexity with edge cases.
KNOWLEDGE CHECK
🧠 Level 12 Quiz
Select one answer for each question, then check your score.
1. Which keyword makes a function a generator?
2. Which expression creates a new sorted list?
3. Which collection is efficient for adding and removing at both ends?
4. What is the usual time complexity of comparison-based sorting?
5. Which regular-expression function validates an entire string?
6. What should you do before writing code in an interview?
💻 CodeBhavya Placement Challenge Set
Solve each interview-style problem in the browser. The checker uses the displayed sample input and exact expected output.
0 / 5Solved independently
0Completed with solution
0 / 500Best score
0%Progress
1. Palindrome Check
Determine whether the supplied word reads the same from left to right and right to left.
Sample input:
levelExpected output: Palindrome💡 Reverse the word with slicing and compare it with the original value.
💻 Solve It Yourself — Palindrome Check
Python Code Editor
Program Output
Run your program to see the output.
Test Case
No test checked yet.—
Best Score0 / 100
Attempts0
StatusNot Solved
Consider whether the comparison should be case-sensitive.
Official Python Program
text = input()
reversed_text = text[::-1]
if text == reversed_text:
print("Palindrome")
else:
print("Not Palindrome")2. Second Largest Distinct Number
Find the second largest distinct value among six integers.
Sample inputs:
4, 7, 4, 9, 7, 2Expected output: Second largest = 7💡 Remove duplicates, sort the remaining values and read the second value from the end.
💻 Solve It Yourself — Second Largest Distinct Number
Python Code Editor
Program Output
Run your program to see the output.
Test Case
No test checked yet.—
Best Score0 / 100
Attempts0
StatusNot Solved
Duplicates must not occupy both largest positions.
Official Python Program
numbers = [int(input()), int(input()), int(input()), int(input()), int(input()), int(input())]
unique = list(set(numbers))
unique.sort()
print("Second largest =", unique[-2])3. Pair With Target Sum
Find the first pair of values whose sum equals the target.
Sample inputs:
2, 7, 11, 15; target 9Expected output: Pair = 2 7💡 Compare each number with only the values that appear after it.
💻 Solve It Yourself — Pair With Target Sum
Python Code Editor
Program Output
Run your program to see the output.
Test Case
No test checked yet.—
Best Score0 / 100
Attempts0
StatusNot Solved
Dry-run the selected indexes before printing the values.
Official Python Program
numbers = [int(input()), int(input()), int(input()), int(input())]
target = int(input())
found = False
for i in range(len(numbers)):
for j in range(i + 1, len(numbers)):
if numbers[i] + numbers[j] == target:
print("Pair =", numbers[i], numbers[j])
found = True
break
if found:
break4. Remove Duplicates in Order
Remove repeated values while preserving the order of their first occurrence.
Sample inputs:
3, 1, 3, 2, 1, 5Expected output: Unique = [3, 1, 2, 5]💡 Append a value only when it is not already present in the result list.
💻 Solve It Yourself — Remove Duplicates in Order
Python Code Editor
Program Output
Run your program to see the output.
Test Case
No test checked yet.—
Best Score0 / 100
Attempts0
StatusNot Solved
A plain set does not promise the required first-occurrence order.
Official Python Program
numbers = [int(input()), int(input()), int(input()), int(input()), int(input()), int(input())]
unique = []
for number in numbers:
if number not in unique:
unique.append(number)
print("Unique =", unique)5. Common Sorted Values
Find the distinct values appearing in both lists and display them in ascending order.
Sample inputs: first
1, 2, 3; second 2, 3, 4Expected output: Common = [2, 3]💡 Convert both lists to sets, intersect them and sort the resulting values.
💻 Solve It Yourself — Common Sorted Values
Python Code Editor
Program Output
Run your program to see the output.
Test Case
No test checked yet.—
Best Score0 / 100
Attempts0
StatusNot Solved
Use a set for uniqueness and sorting only for presentation.
Official Python Program
first = [int(input()), int(input()), int(input())]
second = [int(input()), int(input()), int(input())]
common = sorted(set(first) & set(second))
print("Common =", common)🎯 Final Level Summary
Use Python’s strengths
Choose clear built-ins and standard-library structures that match the problem.
Reason before coding
Clarify requirements, select a strategy and state its complexity.
Test deliberately
Verify normal cases, boundaries, duplicates and missing-result situations.
Communicate clearly
Explain the invariant, important decisions and trade-offs while solving.
Placement formula:Correctness + Clarity + Complexity + Communication
🎬 Placement Problem — Visual Flow
Follow a disciplined interview solution from requirement clarification to final verification.
🧠 How to Solve a Placement Problem
Clarify→Examples→Plan→Implement→Verify
Step 1 of 5
1. Clarify the Requirement
Confirm the input, expected output, constraints and duplicate-handling rules.
🔎 Program Tracing — Pair With Target Sum
Trace the nested-loop solution until it finds the pair 2 + 7 = 9.
Python Program
Live State
Click Next to begin tracing.
Output
—
Step 0 of 6
🎤 Advanced Python — Interview Questions
Answer aloud before opening each explanation.
1.
What is the difference between an iterable and an iterator?
An iterable can produce an iterator through iter(). An iterator stores traversal state and returns successive values through next() until raising StopIteration.
2.
Why are generators memory-efficient?
A generator produces values lazily and preserves only the state needed to resume, instead of constructing the complete result collection first.
3.
What is a decorator?
A decorator is a callable that receives another callable and returns a callable, commonly adding cross-cutting behaviour such as logging, validation or caching.
4.
What is the difference between sort() and sorted()?
list.sort() changes one list in place and returns None. sorted() accepts any iterable and returns a new sorted list.
5.
What are *args and **kwargs?
*args collects extra positional arguments into a tuple, while **kwargs collects extra keyword arguments into a dictionary.
6.
How do you explain algorithm complexity in an interview?
Identify the dominant operation as input grows, state time and auxiliary-space complexity, then explain the trade-off and how the chosen data structure affects it.
💡 Placement Success Tips
Restate the problem and confirm assumptions before proposing code.
Start with a correct direct approach, then improve it when constraints require.
Use meaningful variable names so the interviewer can follow your reasoning.
Dry-run one normal case and at least one edge case aloud.
Know the complexity of Python list, set and dictionary operations you use.
After coding, reread every branch and confirm the exact output format.
✍️ Advanced Python — Extra Practice Questions
- Check whether two strings are anagrams after ignoring spaces and case.
- Find the first non-repeating character in a string.
- Move all zero values to the end of a list while preserving other values.
- Find the missing value from the numbers
1throughn. - Merge two sorted lists without calling
sort(). - Generate the first
nFibonacci values usingyield. - Create a decorator that measures and prints function execution time.
- Use a regular expression to validate a ten-digit mobile number.
- Group words that are anagrams of one another.
- Compare a brute-force pair-sum solution with a set-based
O(n)approach.
LEVEL 12 • COURSE CHECKPOINT
Python Learning Path Complete
You have progressed from Python foundations to classes, advanced tools and placement problem solving. Keep strengthening these skills through regular coding practice.