Word frequency analysis in Data Analysis Python - Practice Problems & Coding Challenges

Challenge - 5 Problems

❓ Predict Output

intermediate

Output of word frequency count using Counter

What is the output of this Python code that counts word frequencies in a list?

Data Analysis Python

from collections import Counter
words = ['apple', 'banana', 'apple', 'orange', 'banana', 'apple']
freq = Counter(words)
print(freq)

A Counter({'apple': 3, 'banana': 2, 'orange': 1})

B {'apple': 3, 'banana': 2, 'orange': 1}

C Counter({'apple': 2, 'banana': 2, 'orange': 2})

D {'apple': 2, 'banana': 2, 'orange': 1}

💡 Hint

Remember that Counter counts how many times each word appears.

Counter creates a dictionary-like object showing counts of each word. 'apple' appears 3 times, 'banana' 2 times, and 'orange' once.

❓ data_output

intermediate

Number of unique words in a text

Given this list of words, how many unique words are there?

Data Analysis Python

words = ['data', 'science', 'data', 'analysis', 'science', 'python', 'python', 'python']
unique_words = len(set(words))
print(unique_words)

A 8

B 3

C 5

D 4

💡 Hint

Use set to find unique items in a list.

The unique words are 'data', 'science', 'analysis', and 'python', so total 4.

❓ visualization

advanced

Bar chart of word frequencies

Which option shows the correct bar chart for the word frequencies in this data?

Data Analysis Python

import matplotlib.pyplot as plt
from collections import Counter
words = ['cat', 'dog', 'cat', 'bird', 'dog', 'dog']
counter = Counter(words)
plt.bar(counter.keys(), counter.values())
plt.show()

A Bar chart with bars: cat=3, dog=2, bird=1

B Bar chart with bars: cat=2, dog=3, bird=1

C Bar chart with bars: cat=1, dog=2, bird=3

D Bar chart with bars: cat=2, dog=2, bird=2

💡 Hint

Count how many times each word appears before plotting.

The word 'dog' appears 3 times, 'cat' 2 times, and 'bird' once, so the bar heights match these counts.

🔧 Debug

advanced

Identify the error in word frequency code

What error does this code raise when counting word frequencies?

Data Analysis Python

words = ['red', 'blue', 'red', 'green']
freq = {}
for word in words:
    freq[word] += 1
print(freq)

A SyntaxError

B TypeError

C KeyError

D No error, prints correct counts

💡 Hint

Check what happens when you try to add to a dictionary key that does not exist yet.

The dictionary freq is empty initially, so freq[word] += 1 tries to add to a missing key, causing KeyError.

🚀 Application

expert

Find the most frequent word in a large text

Given a large text split into words, which code snippet correctly finds the most frequent word?

Data Analysis Python

text = 'data science is fun and data science is powerful and data is everywhere'
words = text.split()

A

from collections import Counter
counter = Counter(words)
most_common = counter.most_common(1)[0][0]
print(most_common)

B

freq = {}
for w in words:
    freq[w] = freq.get(w, 0) + 1
most_common = max(freq)
print(most_common)

C

from collections import Counter
counter = Counter(words)
most_common = max(counter)
print(most_common)

D

freq = {}
for w in words:
    freq[w] = freq.get(w, 0) + 1
most_common = max(freq.values())
print(most_common)

💡 Hint

Use Counter's most_common method to get the word with highest count.

Option A uses Counter and most_common(1) to get the word with highest frequency. Other options either find max key or max count but do not get the word correctly.

Practice

1.

What does word frequency analysis help us understand about a text?

easy

A. How many times each word appears in the text

B. The total number of characters in the text

C. The number of sentences in the text

D. The length of the longest word in the text

Solution

1. Step 1: Understand the purpose of word frequency analysis

   Word frequency analysis counts how often each word appears in a text.

2. Step 2: Compare options with the purpose

   Only How many times each word appears in the text matches this purpose exactly.

3. Final Answer:

   How many times each word appears in the text -> Option A

4. Quick Check:

   Word frequency = count of each word [OK]

Hint: Focus on counting words, not characters or sentences [OK]

Common Mistakes:

• Confusing word count with character count
• Thinking it counts sentences
• Assuming it finds longest word length

2.

Which Python code correctly imports the tool to count word frequencies?

?

easy

A. import wordcount from collections

B. import Counter from collections

C. from collections import wordfreq

D. from collections import Counter

Solution

1. Step 1: Recall the correct import syntax for Counter

   The correct way is 'from collections import Counter'.

2. Step 2: Check each option's syntax

   Only from collections import Counter uses valid Python import syntax and correct name.

3. Final Answer:

   from collections import Counter -> Option D

4. Quick Check:

   Correct import = from collections import Counter [OK]

Hint: Remember 'from collections import Counter' exactly [OK]

Common Mistakes:

• Using wrong import order
• Trying to import non-existent modules
• Using invalid syntax like 'import Counter from'

3.

What is the output of this code?

from collections import Counter
text = "apple banana apple orange banana apple"
words = text.split()
count = Counter(words)
print(count)

medium

A. Counter({'apple': 1, 'banana': 1, 'orange': 1})

B. {'apple': 3, 'banana': 2, 'orange': 1}

C. Counter({'apple': 3, 'banana': 2, 'orange': 1})

D. {'apple': 1, 'banana': 2, 'orange': 3}

Solution

1. Step 1: Split the text into words

   The text splits into ['apple', 'banana', 'apple', 'orange', 'banana', 'apple'].

2. Step 2: Count each word using Counter

   'apple' appears 3 times, 'banana' 2 times, 'orange' 1 time.

3. Final Answer:

   Counter({'apple': 3, 'banana': 2, 'orange': 1}) -> Option C

4. Quick Check:

   Count matches word occurrences [OK]

Hint: Count words carefully; Counter shows counts with 'Counter()' [OK]

Common Mistakes:

• Ignoring Counter output format
• Miscounting word frequencies
• Confusing dictionary and Counter print styles

4.

Find the error in this code that tries to count words:

from collections import Counter
text = "Hello world! Hello"
words = text.split()
count = Counter(words.lower())
print(count)

medium

A. Cannot call lower() on a list; should call on each word

B. Missing import statement for Counter

C. split() should be split(' ')

D. Counter cannot count strings

Solution

1. Step 1: Identify the type of 'words'

   words is a list of strings after split().

2. Step 2: Check the use of lower()

   Calling words.lower() is invalid because lists have no lower() method; lower() applies to strings.

3. Final Answer:

   Cannot call lower() on a list; should call on each word -> Option A

4. Quick Check:

   lower() works on strings, not lists [OK]

Hint: Apply lower() to each word, not the whole list [OK]

Common Mistakes:

• Calling lower() on list instead of string
• Ignoring error messages
• Thinking split() needs arguments

5.

You want to count words ignoring case and punctuation in this text:

text = "Hello, hello! How are you? Are you fine?"

Which code correctly cleans and counts words?

hard

A. import re
from collections import Counter
words = re.findall(r'\w+', text)
count = Counter(words)
print(count)

B. import string
from collections import Counter
clean_text = text.lower().translate(str.maketrans('', '', string.punctuation))
words = clean_text.split()
count = Counter(words)
print(count)

C. from collections import Counter
words = text.split()
count = Counter(words)
print(count)

D. from collections import Counter
words = text.lower().split()
count = Counter(words)
print(count)

Solution

1. Step 1: Understand the need to remove punctuation and lowercase

   To count words ignoring case and punctuation, text must be lowercased and punctuation removed.

2. Step 2: Check each option's approach

   import string from collections import Counter clean_text = text.lower().translate(str.maketrans('', '', string.punctuation)) words = clean_text.split() count = Counter(words) print(count) uses string.punctuation with translate() to remove punctuation and lowercases text before splitting, which is correct.

3. Final Answer:

   Code that lowercases and removes punctuation before counting -> Option B

4. Quick Check:

   Clean text + Counter = accurate word frequency [OK]

Hint: Remove punctuation and lowercase before splitting words [OK]

Common Mistakes:

• Not removing punctuation
• Counting words with mixed cases separately
• Using split() without cleaning text