Entropy Calculator

This tool computes the Shannon entropy of text, a binary string, or a byte sequence. Shannon entropy measures the average information content per symbol, in bits. Higher entropy means more unpredictability; for example a long random string has entropy close to log2 of its alphabet size, while repeated characters have entropy near zero. The Text tab also shows a top-10 character frequency breakdown.

1. Pick a tab: Text, Binary, or Bytes. 2. Type or paste your input into the textarea for that tab. 3. Read the Entropy value, which updates as you type. The Text tab reports bits per character and shows a bar chart of the ten most frequent characters; the Binary tab reports entropy over the symbols 0 and 1; the Bytes tab reports entropy over byte values 0..255.

Shannon entropy H = -sum( p_i * log2(p_i) ) over every distinct symbol i, where p_i is the count of symbol i divided by the total length. The Text tab treats each UTF-16 code unit as a symbol; the Binary tab treats each character (expected 0 or 1) as a symbol; the Bytes tab takes the low 8 bits of each character's code unit as the symbol. For an alphabet of N equally likely symbols the entropy is log2(N), so 8 bits/byte for uniformly random bytes and 1 bit/char for a fair coin-flip string.