About File & Text Entropy Calculator
Shannon Entropy, in the context of information theory, is a measure of the rate at which information is produced by a source of data. It can be used, in a broad sense, to detect whether data is likely to be structured or unstructured. 8 is the maximum, representing highly unstructured, 'random' data. English language text usually falls somewhere between 3.5 and 5. Properly encrypted or compressed data should have an entropy of over 7.5.
Input: binary bytes (text input uses UTF-8; a file retains its original bytes). Output: structured JSON.
Format reference: Entropy background and specification
Example
Use “Try example” to load this public sample and its settings, then run it.
Settings used in this example
| Parameter | Example value | Input and choices |
|---|---|---|
| Visualisation | Shannon scale | Shannon scale, Histogram (Bar), Histogram (Line), Curve, Image |
Input
ABAB
Result
The result is structured data. Inspect its fields in the output.
Parameters
| Parameter | Default value | Input and choices |
|---|---|---|
| Visualisation | Shannon scale | Shannon scale, Histogram (Bar), Histogram (Line), Curve, Image |
Before interpreting the result
These tools have specialized conventions. Read the operation description and parameter choices before interpreting the result.
How to use this page
- Paste your input or open a local file.
- Adjust tool options if needed, then choose Run tool.
- Copy the result or download the original output bytes.
Inputs and results are not added to the public catalogue or saved in the URL. Processing happens in your browser; HTTP Request and DNS over HTTPS send the requests you explicitly run. Files up to 128 MiB can be opened; decompression and image tools may have smaller processing limits. Large results have a shortened preview. Text and HTML downloads use UTF-8; binary downloads preserve output bytes. Pasted text follows browser newline handling; open a file when exact input bytes matter.