Editing Entropy and Information (section)

== <span style="color: #FFFFFF;">Applying</span> ==
'''Modeling 'The Entropy Calculation' (Measuring the information in a choice):'''
<syntaxhighlight lang="python">
import math

def calculate_entropy(probabilities):
    """
    H = -sum(p * log2(p))
    Measures the 'Uncertainty' in bits.
    """
    entropy = 0
    for p in probabilities:
        if p > 0:
            entropy -= p * math.log2(p)
    return round(entropy, 2)

# Fair Coin (50/50)
print(f"Fair Coin Entropy: {calculate_entropy([0.5, 0.5])} bits")

# Weighted Coin (90/10)
print(f"Weighted Coin Entropy: {calculate_entropy([0.9, 0.1])} bits")
# Note: The weighted coin is more predictable, so it has LESS information.
</syntaxhighlight>

; Information Landmarks
: '''A Mathematical Theory of Communication (1948)''' → Shannon's paper that is considered the "Magna Carta" of the information age.
: '''The Enigma Machine''' → While Shannon worked on theory, Alan Turing worked on "Information" in the context of codebreaking during WWII.
: '''Deep Space Communication''' → NASA uses information theory to send photos from Pluto using a tiny, weak signal that is almost entirely covered by "Noise."
: '''DNA as Code''' → Biologists realized that DNA is an information system with an entropy that can be measured just like a computer file.
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