Word frequency analysis in Data Analysis Python

Introduction

Word frequency analysis helps us find out how often each word appears in a text. This is useful to understand the main topics or common words in any document.

To find the most common words in customer reviews to see what people talk about the most.

To analyze speeches or articles to identify key themes or repeated ideas.

To check which words appear frequently in social media posts about a product.

To prepare text data before building a search engine or recommendation system.

Syntax

Data Analysis Python

from collections import Counter

text = "your text here"
words = text.lower().split()
word_counts = Counter(words)
print(word_counts)

Use text.lower() to count words without case differences (e.g., 'Word' and 'word' count as the same).

Counter automatically counts how many times each word appears.

Examples

This counts 'hello' twice because we convert all words to lowercase first.

Data Analysis Python

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

We remove punctuation before splitting to count words correctly.

Data Analysis Python

from collections import Counter
text = "Data science is fun. Data is powerful."
words = text.lower().replace('.', '').split()
word_counts = Counter(words)
print(word_counts)

Sample Program

This program counts how many times each word appears in the given text. It ignores case and punctuation for accurate counting.

Data Analysis Python

from collections import Counter

text = "Data science is fun. Data is powerful. Science helps us understand data."

# Convert text to lowercase and remove punctuation
clean_text = text.lower().replace('.', '')

# Split text into words
words = clean_text.split()

# Count word frequencies
word_counts = Counter(words)

# Print the word counts
print(word_counts)

Important Notes

Removing punctuation helps avoid counting words like 'data' and 'data.' separately.

Counting words is a first step in many text analysis tasks like sentiment analysis or topic modeling.

Summary

Word frequency analysis shows how often each word appears in text.

Use Counter from Python's collections to count words easily.

Clean text by lowering case and removing punctuation for better results.

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