Calculate the information entropy of text or data sequences to evaluate uncertainty and randomness.
Entropy is computed from character frequency and shown in bit/char
Enter text to see the result
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When you need to quantify data randomness or evaluate password strength, traditional methods often rely on subjective judgment. The Shannon Entropy Calculator accurately calculates the information entropy (in bits per symbol) using mathematical formulas. This value reflects the average amount of information contained in each symbol within the data. Shannon entropy is defined as: H(X) = -Σ[P(x_i)*log₂(P(x_i))], where P(x_i) is the probability of the symbol x_i occurring. A higher result indicates that the data is more unpredictable.
How do I know if an entropy value is high or low?
Values above 4 bits/symbol are considered high-entropy data (close to random), while values below 1 bit are low-entropy (highly regular). Typical examples: "AAAA" has an entropy of 0, and "ABAB" has an entropy of 1.
Is the calculation result affected by text length? The theoretical value is independent of length, but short texts may lead to probability estimation bias due to a small sample size. We recommend using samples with >100 characters for testing.
This tool calculates at the character level; Chinese characters, English letters, and symbols are all treated as independent symbols. For specific scenarios, we recommend preprocessing your data (e.g., converting everything to lowercase). The calculation results are not suitable for evaluating the entropy of non-character data.
In cryptographic applications, we recommend using this alongside NIST entropy testing standards: a strong password should reach 3.5 bits/character or higher. Typical test cases: "P@ssw0rd" ≈ 2.8 bits, "qW9$kx!L" ≈ 4.1 bits. Please note that actual security must also account for additional factors like dictionary attacks.
