Logarithm Calculator

Calculate any logarithm

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About Logarithm Calculator

A logarithm calculator that computes log base 10 (common), ln (natural, base e), log base 2 (binary), and custom base logarithms. Also calculates antilogs (inverse) and demonstrates the change of base formula. Shows step-by-step results and log properties. All calculations are client-side. Essential for math students, scientists, engineers, and anyone working with exponential scales.

Logarithm Calculator Features

  • Common/natural/binary
  • Custom base
  • Antilog
  • Change of base
  • Properties
A logarithm answers: 'To what power must the base be raised to get this number?' log₁₀(1000) = 3 because 10³ = 1000. Logarithms turn multiplication into addition, making them essential for scientific scales (decibels, Richter, pH), compound interest, and algorithm analysis.

How to Use

Enter a number and base:

  • Common: log₁₀(x)
  • Natural: ln(x) = logₑ(x)
  • Binary: log₂(x)
  • Custom: Any base

Log Properties

  • log(a×b) = log(a) + log(b)
  • log(a/b) = log(a) − log(b)
  • log(aⁿ) = n × log(a)
  • log(1) = 0 for any base
  • log_b(b) = 1

Change of Base

log_b(x) = log(x) / log(b) = ln(x) / ln(b). This formula lets you calculate any base logarithm using your calculator's log or ln button.

Step-by-Step Instructions

  1. 1Enter the number.
  2. 2Select the logarithm base.
  3. 3View the result.
  4. 4Check the antilog (inverse).
  5. 5Review the change of base calculation.

Logarithm Calculator — Frequently Asked Questions

What's the difference between log and ln?+

log (common logarithm) uses base 10: log₁₀(100)=2. ln (natural logarithm) uses base e≈2.718: ln(e)=1. In many countries, 'log' means natural log in math contexts.

Can you take the log of 0 or negative numbers?+

log(0) is undefined (approaches −∞). log of negative numbers is undefined in real numbers. In complex math, log(−1) = iπ.

Where are logarithms used in real life?+

Decibels (sound), Richter scale (earthquakes), pH (acidity), compound interest, radioactive decay, algorithm complexity (O(log n)), information theory (bits).

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