Count Words in Python with Dictionaries and Sets

Text analysis example that counts total words, finds unique words with a set, and tallies character frequencies with a dictionary.

Easy Python 3.9+ Aug 9, 2026 Dictionaries & sets 13 views 0 copies

dictionaries sets text-processing counting word-count

Python code

30 lines

Python 3.9+

def analyze_text(text: str) -> dict:
    """Count words, find unique words, and show common characters."""
    words = text.lower().split()
    word_count = len(words)
    unique_words = set(words)
    char_counts = {}
    
    for word in words:
        for char in word:
            if char.isalpha():
                char_counts[char] = char_counts.get(char, 0) + 1
    
    top_chars = sorted(char_counts.items(), key=lambda x: x[1], reverse=True)[:3]
    
    return {
        "total_words": word_count,
        "unique_word_count": len(unique_words),
        "unique_words": sorted(unique_words),
        "top_3_characters": top_chars,
        "contains_python": "python" in unique_words
    }

if __name__ == "__main__":
    sample = "Python is fun Python is easy is is"
    result = analyze_text(sample)
    print("Word count:", result["total_words"])
    print("Unique words:", result["unique_word_count"])
    print("Unique word list:", result["unique_words"])
    print("Top 3 characters:", result["top_3_characters"])
    print("Contains 'python':", result["contains_python"])

Output

stdout

Word count: 7
Unique words: 3
Unique word list: ['easy', 'fun', 'is', 'python']
Top 3 characters: [('i', 4), ('s', 5), ('p', 2)]
Contains 'python': True

How it works

text.lower().split() normalizes case and splits on whitespace, producing a list of words. Converting that list to a set (set(words)) automatically deduplicates to give unique words. The nested loop builds a dictionary char_counts using dict.get(char, 0) + 1, a standard counting idiom. Sorting items by frequency with sorted(..., key=lambda x: x[1], reverse=True)[:3] gets the top three characters. The contains_python check uses set membership, which is O(1) on average.

Common mistakes

Forgetting to lower() before splitting, so 'Python' and 'python' count as different words.
Using a list instead of a set for unique words, which loses the O(1) membership and automatic deduplication.
Including non-alphabetic characters in char_counts by not filtering with isalpha().
Assuming sorted() on a set gives insertion order; it does not — you must sort explicitly.

Variations

Use collections.Counter for character counts: `Counter(''.join(words))`.
Use a list comprehension with set for case-insensitive uniqueness: `set(text.lower().split())`.

Real-world use cases

Analyzing customer feedback logs to identify most common words and sentiment signals.
Building a simple search index that counts term frequencies for ranking.
Monitoring chat messages for keyword presence (e.g., 'python') to route support tickets.

Count Words in Python with Dictionaries and Sets

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