Calculate Text Entropy
Measure information density with Shannon Entropy. Visualize character probability and estimate compression limits.
Probability Distribution
Top 20 contributing characters by probability
Shannon Entropy
Details
Measure Information Density
How random is your text? The Calculate Text Entropy tool uses Shannon's Information Theory formulas to quantify the unpredictability of any string. Whether you are a developer optimizing data compression, a security expert checking password strength, or a linguist analyzing language patterns, this tool provides deep statistical insights into your content.
Analysis Features
Shannon Entropy
Calculate the precise bit-depth per character to measure randomness and unpredictability.
Probability Viz
Interactive chart showing the frequency distribution of characters in your text sample.
Compression Est.
See theoretically how much your text could be compacted based on its redundancy.
Password Check
Use entropy as a scientific metric for password strength rather than just length.
File Analysis
Upload source code or logs to analyze the entropy of entire documents instantly.
Local Secure
Privacy-first analysis. Your potentially sensitive text never leaves your browser.
Common Use Cases
- Data Compression: Estimate the minimum size of a file before compressing it.
- Cryptography: Verify the quality of random number generators or keys.
- Linguistics: Compare the complexity and redundancy of different languages.
- Genomics: Analyze DNA sequences (A, C, G, T) for repetitive vs information-dense regions.
Frequently Asked Questions
What is Shannon Entropy?
Shannon Entropy is a concept from Information Theory that measures the unpredictability or 'information content' of a message. Higher entropy means the text is more random and harder to predict.
What does bits per character mean?
It indicates the minimum number of bits required to encode each character based on its frequency. For example, standard English has an entropy of about 4-5 bits/char, while random noise might be close to 8 bits/char (for ASCII).
How is redundancy and compression calculated?
Redundancy is the difference between the standard 8-bit encoding and the text's actual entropy. If your text has an entropy of 4 bits/char, it has ~50% redundancy, meaning it could theoretically be compressed to half its size.
Can I use this to check passwords?
Yes! Entropy is a great metric for password strength. High entropy passwords (e.g., '>4 bits/char' with sufficient length) are mathematically harder for attackers to guess than repetitive or dictionary-based passwords.
What is Metric Entropy?
Metric entropy normalizes the total Shannon entropy by the length of the text. This tool displays entropy primarily as 'bits per symbol', which is effectively the metric entropy for the character stream.
Why do you have a probability chart?
The visualization helps you see which characters contribute most to the text structure. A flat chart (equal probabilities) yields maximum entropy, while a chart with high spikes means lower entropy (more predictable).
Does case sensitivity matter?
Yes. 'A' and 'a' are distinct characters with their own frequencies. If you disable case sensitivity, they are merged, which typically lowers the total entropy since the set of unique symbols is smaller.
Can I analyze source code files?
Absolutely. You can upload .js, .py, or any text-based code file. Code files often have specific entropy signatures different from natural language prose.
Is my data sent to a server?
No. All calculations, including large file processing, happen locally in your web browser ensuring your data remains private and secure.
What is the max possible entropy?
The maximum entropy depends on the number of unique characters (alphabet size). For standard ASCII (256 chars), the max is 8 bits. For just lowercase English (26 chars), it's approx 4.7 bits.