Shannon Entropy Calculator
Shannon entropy (bits per symbol)
Client-side computation for analysis, verification, and learning. Do not paste production secrets into untrusted sites; prefer local tooling for key material.
Educational / engineering use. RSA mode uses tiny primes for illustration only—not for real key generation.
Sample string
Entropy
Alphabet size: 3 H (bits per symbol): 1.459148 Total bits if IID: 8.7549
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What is Shannon Entropy Calculator?
Calculates the average information content (uncertainty) in a data source, measured in bits. Higher entropy means more randomness useful in compression analysis, cryptography, and evaluating password strength.
Shannon Entropy Formula
Where p_i is probability of symbol i. Result in bits. For equal probabilities: H = log2(n).
Step-by-Step
- Count symbol frequencies.
- Convert counts to probabilities p_i.
- Compute p_i log2(p_i) for each symbol.
- Sum all terms.
- Negate the sum to get entropy in bits/symbol.
Worked Example
Input: "AABBC"
≈ 1.522 bits.
Where Shannon Entropy Calculator Is Applied
- Data compression algorithms (Huffman coding, ZIP)
- Measuring password strength and randomness
- Malware detection (high-entropy sections indicate packed/encrypted code)
- Machine learning — decision tree splitting (information gain)
- Cryptanalysis — identifying encryption strength
- Image compression (JPEG, PNG optimization)
- Network anomaly detection (traffic entropy analysis)
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