Hash table applications in Data Structures Theory - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to create a hash table that stores key-value pairs.

Data Structures Theory

hash_table = [1]()

Drag options to blanks, or click blank then click option'

Adict

Bset

Clist

Dtuple

Attempts:

3 left

💡 Hint

Common Mistakes

Using list instead of dict, which does not support key-value pairs.

Using set, which stores only unique values without keys.

The dict type in Python is a built-in hash table that stores key-value pairs efficiently.

2fill in blank

medium

Complete the code to check if a key exists in a hash table.

Data Structures Theory

if [1] in hash_table:

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Aitem

Bindex

Ckey

Dvalue

Attempts:

3 left

💡 Hint

Common Mistakes

Checking for a value instead of a key.

Using an index which is not applicable in hash tables.

To check if a key exists in a hash table (dictionary), you use the key itself with the in keyword.

3fill in blank

hard

Fix the error in the code to add a key-value pair to the hash table.

Data Structures Theory

hash_table[[1]] = 'value'

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A'key'

Bkey

Cvalue

Ditem

Attempts:

3 left

💡 Hint

Common Mistakes

Using an unquoted key which causes a NameError.

Using a value as a key.

Keys in a hash table must be immutable and usually strings are used as keys. Here, the key should be a string literal, so it needs quotes.

4fill in blank

hard

Fill both blanks to create a hash table comprehension that maps words to their lengths for words longer than 3 characters.

Data Structures Theory

{word: [1] for word in words if len(word) [2] 3}

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Alen(word)

B<

C>

Dword

Attempts:

3 left

💡 Hint

Common Mistakes

Using the word itself instead of its length as the value.

Using the wrong comparison operator in the condition.

The comprehension maps each word to its length using len(word). The condition filters words longer than 3 characters using >.

5fill in blank

hard

Fill all three blanks to create a hash table that stores uppercase keys and their values only if the value is positive.

Data Structures Theory

{ [1]: [2] for [3], [2] in data.items() if [2] > 0 }

Drag options to blanks, or click blank then click option'

Akey.upper()

Bvalue

Ckey

Ditem

Attempts:

3 left

💡 Hint

Common Mistakes

Using the same variable name for key and value.

Not converting keys to uppercase.

Using incorrect variable names in the loop.

The comprehension uses key.upper() to make keys uppercase, value as the dictionary value, and iterates over key, value pairs from data.items(). The condition filters values greater than zero.

Practice

1. What is the main advantage of using a hash table in data storage?

easy

A. It encrypts data for security

B. It stores data in a sorted order automatically

C. It compresses data to save space

D. It allows very fast data lookup using keys

Solution

1. Step 1: Understand hash table purpose

   Hash tables use keys to quickly find stored data without searching all items.

2. Step 2: Compare options with hash table features

   Only fast lookup matches hash table's main advantage; sorting, compression, encryption are unrelated.

3. Final Answer:

   It allows very fast data lookup using keys -> Option D

4. Quick Check:

   Fast lookup = It allows very fast data lookup using keys [OK]

Hint: Hash tables are all about quick key-based access [OK]

Common Mistakes:

• Confusing hash tables with sorted data structures
• Thinking hash tables compress or encrypt data
• Assuming hash tables store data in order

2. Which of the following is the correct way to check if a key exists in a hash table?

easy

A. if hash_table.has_key(key):

B. if key in hash_table:

C. if key.exists(hash_table):

D. if hash_table.contains(key):

Solution

1. Step 1: Recall common syntax for key existence

   In many languages like Python, 'if key in hash_table:' is the standard way.

2. Step 2: Evaluate other options

   Methods like has_key() are outdated or language-specific; others are incorrect syntax.

3. Final Answer:

   if key in hash_table: -> Option B

4. Quick Check:

   Standard syntax = if key in hash_table: [OK]

Hint: Use 'in' keyword to check keys in hash tables [OK]

Common Mistakes:

• Using outdated methods like has_key()
• Trying non-existent methods like exists() or contains()
• Confusing key existence with value checks

3. Consider a hash table used to count word occurrences in a text. After processing the text, what will be the value of counts['apple'] if the word 'apple' appears 3 times?

medium

A. 3

B. 1

C. 0

D. undefined

Solution

1. Step 1: Understand counting with hash tables

   Each time 'apple' appears, the count increments by 1 in the hash table.

2. Step 2: Calculate final count for 'apple'

   Since 'apple' appears 3 times, the count stored is 3.

3. Final Answer:

   3 -> Option A

4. Quick Check:

   Count of 'apple' = 3 [OK]

Hint: Count increments match word occurrences [OK]

Common Mistakes:

• Assuming count starts at 0 and stays 0
• Confusing count with boolean existence
• Thinking the value is undefined if key exists

4. A hash table implementation is returning incorrect results when two different keys produce the same hash value. What is the likely problem?

medium

A. The hash function is too slow

B. Keys are not strings

C. Collision handling is missing or incorrect

D. The table size is too large

Solution

1. Step 1: Identify issue with same hash values

   When two keys have the same hash, it's called a collision.

2. Step 2: Understand collision handling importance

   If collisions are not handled properly, data can overwrite or be lost, causing errors.

3. Final Answer:

   Collision handling is missing or incorrect -> Option C

4. Quick Check:

   Collision handling = missing or incorrect [OK]

Hint: Collisions need special handling to avoid errors [OK]

Common Mistakes:

• Blaming hash function speed instead of collision handling
• Thinking key type causes collisions
• Assuming larger table size fixes collisions alone

5. You want to find all unique items from a large list quickly. Which hash table application is best suited for this task?

hard

A. Using a hash table to store items as keys and ignore duplicates

B. Using a hash table to count occurrences of each item

C. Using a hash table to sort the list items

D. Using a hash table to encrypt the list items

Solution

1. Step 1: Understand the goal of finding unique items

   We want to keep only one copy of each item, ignoring duplicates.

2. Step 2: Choose hash table usage that stores keys uniquely

   Storing items as keys automatically ignores duplicates because keys are unique.

3. Step 3: Eliminate other options

   Counting occurrences is extra work; sorting or encrypting does not find unique items efficiently.

4. Final Answer:

   Using a hash table to store items as keys and ignore duplicates -> Option A

5. Quick Check:

   Unique items = store as keys ignoring duplicates [OK]

Hint: Store items as keys to keep only unique entries [OK]

Common Mistakes:

• Using counting when only uniqueness is needed
• Thinking sorting helps find unique items faster
• Confusing encryption with uniqueness