Learn to organize data efficiently through 20 practice problems. Master lists, stacks, queues, and dictionaries—the foundation of algorithmic problem-solving and technical interviews.
A new guide teaching data structures in Python is now available.
46 pages covering lists, stacks, queues, and dictionaries. You'll solve 20 practice problems demonstrating real-world applications—from balanced parentheses checking to LRU cache implementation.
Each structure includes clear explanations, complete implementations, and progressively challenging problems. Type every solution yourself to build problem-solving muscle memory.
Simple variables work for individual values. But programs manage thousands of related values—student records for entire schools, product inventories for shops, user data for websites.
Data structures organize related data efficiently. Choose the right structure and operations become fast. Choose poorly and programs slow down dramatically.
Finding one student among 1000 students illustrates this. Searching a list checks students one by one—potentially 1000 checks. Using a dictionary with roll numbers as keys finds any student instantly—just 1 check. The difference between 1000 operations and 1 operation matters enormously.
CBSE Class 11-12 students taking Computer Science (Code 083). Data structures appear throughout both classes.
Anyone preparing for technical interviews. Every tech company tests data structure knowledge—Google, Amazon, Microsoft, Indian startups. These 20 problems mirror common interview questions.
Self-learners building algorithmic problem-solving skills. Competitive programmers strengthening fundamentals.
Teachers needing complete implementations with explanations for classroom instruction.
Lists and tuples - Python's ordered sequences. Mutable lists for changing data versus immutable tuples for fixed data. Slicing, sorting, list comprehensions. When to use which structure.
Stacks - Last-In-First-Out behavior. Like a stack of plates—add and remove from top only. Perfect for browser back buttons, text editor undo, expression evaluation. Implement from scratch using Python lists.
Queues - First-In-First-Out behavior. Like customers in line—first to arrive gets served first. Perfect for printer job scheduling, fair task management, customer service simulation. Circular queue optimization for fixed buffers.
Dictionaries - Key-value pairs with instant lookup. Phone books work this way—look up by name, instantly get number. Perfect for counting frequencies, grouping related items, fast lookups by ID.
Problems 1-4: List operations. Remove duplicates preserving order. Find second largest without sorting. Merge two sorted lists efficiently. Rotate list right by k positions.
Problems 5-8: Stack applications. Implement stack with O(1) minimum operation. Evaluate postfix expressions like calculators do. Reverse words in sentences. Check if strings are palindromes.
Problems 9-12: Queue applications. Implement queue using only two stacks. Generate binary numbers using queue properties. Simulate printer handling urgent and regular jobs. Find first non-repeating character in data stream.
Problems 13-16: Dictionary applications. Count character frequencies efficiently. Group anagrams by sorted letters. Solve two-sum problem in linear time instead of quadratic. Find first character appearing exactly once.
Problems 17-20: Combined structures solving complex scenarios. Implement LRU cache with O(1) get and put operations. Build browser history manager with back and forward buttons. Create task scheduler handling multiple priority levels. Analyze student performance across subjects using nested dictionaries.
Every technical interview tests these concepts. Not toy questions—real problems companies use to evaluate candidates.
Stack problems appear constantly. Balanced parentheses, expression evaluation, implementing undo functionality. Master stacks and you've answered 20% of interview questions.
Dictionary problems dominate interviews. Two-sum, grouping anagrams, counting frequencies, finding duplicates. These problems test whether you recognize when hash lookups beat linear search.
Combined problems test overall problem-solving. LRU cache, browser history, task scheduler. These require choosing appropriate structures and combining them intelligently.
This guide prepares you for actual technical interviews, not just exams.
Structure choice affects program speed dramatically:
O(1) constant time - Same speed regardless of data size. Dictionary lookup, stack push/pop, queue enqueue/dequeue. Ideal performance.
O(n) linear time - Speed proportional to data size. Searching unsorted list, counting items, finding minimum. Acceptable for most cases.
O(n²) quadratic time - Speed proportional to data squared. Nested loops, bubble sort, checking all pairs. Avoid when O(n) solutions exist.
Understanding Big O notation helps you write scalable code that remains fast as data grows from hundreds to millions of records.
These aren't academic exercises. Every software system uses these structures:
Browser back button - Stack storing page history. Click back, pop most recent page. Clear demonstration of LIFO behavior.
Printer job scheduling - Queue ensuring fair order. First document sent prints first. FIFO behavior prevents job starvation.
Autocomplete suggestions - Dictionary (trie structure) enabling instant prefix matching. Type "pyt" and see "python" suggestions immediately.
Social media feeds - Queues managing incoming posts. Stacks handling undo operations. Dictionaries caching user data for fast access.
Understanding data structures means understanding how all software actually works internally.
Use lists when - You need ordered sequences. Position matters. Frequent iteration through all elements. Duplicates allowed.
Use tuples when - Data shouldn't change after creation. Dictionary keys needed (lists can't be keys). Slight performance advantage over lists.
Use stacks when - Last item added should be first item processed. Undo operations. Navigation history. Expression parsing.
Use queues when - First item added should be first item processed. Fair scheduling. Customer service simulation. Breadth-first algorithms.
Use dictionaries when - Need instant lookup by key. Counting frequencies. Grouping related items. Avoiding duplicate keys.
Python basics required before starting. Variables, functions, loops, conditionals. If you understand those, you're ready for data structures.
All code runs in standard Python 3.x without external libraries. Type examples in IDLE, VS Code, or any Python environment.
46 pages. 2.6 MB. Free.
Download Data Structures Guide →
Two other Computer Science guides are available:
Python Object-Oriented Programming - Build classes that use these data structures internally. Create systems combining OOP and efficient data organization.
SQL Database Management - Store data persistently in databases. Understand how database indexing mirrors dictionary behavior.
Together these three cover the complete CBSE Computer Science programming curriculum for the senior-secondary level.
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Master lists, stacks, queues, and dictionaries through 20 practice problems. Build efficient applications that organize data intelligently.
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