Character Frequency Analyzer vs related tools: which one to use

How to use

1. Identify what your downstream consumer actually expects — a format, an encoding, a checksum or a human-readable label.
2. Match that expectation to the tool whose output is already in that form; converting twice loses information.
3. When two tools look equivalent, prefer the one whose options expose the standard your consumer cites.
4. Keep the related tools open side by side and run the same input through both; differences appear immediately.

Examples

A pangram: every letter appears, so the entropy is close to the maximum for English text.

Input

the quick brown fox jumps over the lazy dog

Output — Results

43 characters, 27 distinct

Top 27:
  ␣   U+0020        8   18.60%  ████████████████████████
  o   U+006F        4    9.30%  ████████████
  e   U+0065        3    6.98%  █████████
  h   U+0068        2    4.65%  ██████
  r   U+0072        2    4.65%  ██████
  t   U+0074        2    4.65%  ██████
  u   U+0075        2    4.65%  ██████
  a   U+0061        1    2.33%  ███
  b   U+0062        1    2.33%  ███
  c   U+0063        1    2.33%  ███
  d   U+0064        1    2.33%  ███
  f   U+0066        1    2.33%  ███
  g   U+0067        1    2.33%  ███
  i   U+0069        1    2.33%  ███
  j   U+006A        1    2.33%  ███
  k   U+006B        1    2.33%  ███
  l   U+006C        1    2.33%  ███
  m   U+006D        1    2.33%  ███
  n   U+006E        1    2.33%  ███
  p   U+0070        1    2.33%  ███
  q   U+0071        1    2.33%  ███
  s   U+0073        1    2.33%  ███
  v   U+0076        1    2.33%  ███
  w   U+0077        1    2.33%  ███
  x   U+0078        1    2.33%  ███
  y   U+0079        1    2.33%  ███
  z   U+007A        1    2.33%  ███

Shannon entropy: 4.3855 bits/character
(8.0 would mean perfectly random bytes; English prose is typically 4.0–4.5)

Run

Description

Count characters, words, line lengths or n-grams and compute Shannon entropy.

Character Frequency Analyzer → · How to use → · Text Tools →

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