Entropy Calculator
Analyze text randomness and calculate entropy metrics to understand predictability, complexity, and information content.
Understanding Text Entropy
What is Entropy?
- • Measures information content and randomness
- • Higher entropy = more unpredictable text
- • Lower entropy = more repetitive, predictable text
- • Useful for analyzing text complexity and patterns
Applications
- • Cryptographic analysis
- • Password strength assessment
- • Text compression analysis
- • Language pattern detection
Frequently Asked Questions
What is entropy and why is it important for text analysis?
Entropy is a measure of uncertainty or randomness in information. In text analysis, it quantifies how predictable or unpredictable your text is. High entropy indicates more random, complex text, while low entropy suggests repetitive, predictable patterns. This is crucial for cryptography, password strength assessment, compression analysis, and understanding text complexity.
How is Shannon entropy calculated?
Shannon entropy is calculated using the formula: H = -Σ(p(x) × log₂(p(x)))
Where:
• H is the entropy in bits
• p(x) is the probability of each character or word appearing
• The sum is taken over all unique characters or words
The result tells you the average number of bits needed to encode each character optimally.
What do the different entropy metrics mean?
Shannon Entropy: The raw entropy value in bits per symbol
Relative Entropy: Entropy as a percentage of the theoretical maximum
Predictability: How easily the next character/word can be guessed
Complexity: Overall assessment from Very Low to Very High
Randomness Score: A 0-100 scale indicating how random the text appears
What types of text have different entropy levels?
Low Entropy: Repetitive text, simple passwords, formatted data
Medium Entropy: Natural language, typical prose, structured documents
High Entropy: Random strings, strong passwords, compressed data, encrypted text
Our tool includes sample texts demonstrating each entropy level to help you understand the differences.
How can I customize the entropy analysis?
You can adjust several settings to focus your analysis:
Case Sensitivity: Whether uppercase and lowercase letters are treated differently
Punctuation: Include or exclude punctuation marks in the analysis
Spaces: Whether to count spaces as characters
Analysis Level: Character-level vs. word-level entropy calculation
Stop Words: Remove common words like “the,” “and,” “or” for cleaner analysis
What is the difference between character-level and word-level analysis?
Character-level analysis examines individual letters, numbers, and symbols to measure how randomly they appear. Word-level analysis looks at whole words and their distribution patterns. Character-level is better for password analysis and cryptographic applications, while word-level is more useful for analyzing writing style, vocabulary richness, and language patterns.
How can entropy analysis help with password security?
High-entropy passwords are harder to crack because they contain more unpredictable elements. Our calculator can help you assess password strength by measuring randomness. Strong passwords typically have high entropy (above 60 bits), use diverse character sets, and avoid predictable patterns. Low entropy indicates weak passwords that are vulnerable to attacks.
What do the frequency distributions show?
The frequency distributions show how often each character or word appears in your text. Even distributions (where all elements appear with similar frequency) result in higher entropy, while skewed distributions (where some elements are much more common) result in lower entropy. This helps identify patterns and repetitions that affect overall randomness.
How does entropy relate to text compression?
Entropy directly relates to how well text can be compressed. Low-entropy text with many repetitions compresses well, while high-entropy text resists compression. The relative entropy percentage indicates theoretical compression limits - text with 50% relative entropy could theoretically be compressed to half its original size.
Can I export the entropy analysis results?
Yes! You can copy the analysis results as JSON data or download a comprehensive report as a text file. The report includes all entropy metrics, frequency distributions, statistical summaries, and your analysis settings. This is useful for documentation, research, or further analysis in other tools.