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math.log in Python

stackoverflow.com › questions › 14764339 › math-log-in-python

math.log uses floats and these almost always involve some rounding errors.

>>> math.log(1000, 10)
2.9999999999999996

If you need it to be exact you should change your algorithm to generate the powers of 10 (simply multiply the last power by 10) and continue as long as the new power is smaller than your input number.

>>> limit = 12345
>>> power = 10
>>> while power < limit:
...     print power
...     power = power * 10
... 
10
100
1000
10000

This is guaranteed to be exact as it does not involve any floating point numbers. (And is a lot faster, too)

Answer from bikeshedder on Stack Overflow

w3schools.com › python › ref_math_log.asp

Python math.log() Method

Python Examples Python Compiler Python Exercises Python Quiz Python Challenges Python Server Python Syllabus Python Study Plan Python Interview Q&A Python Bootcamp Python Certificate Python Training ... # Import math Library import math # Return the natural logarithm of different numbers print(math.log(2.7183)) print(math.log(2)) print(math.log(1)) Try it Yourself »

docs.python.org › 3 › library › math.html

math — Mathematical functions

February 23, 2026 - >>> from math import exp, expm1 >>> exp(1e-5) - 1 # gives result accurate to 11 places 1.0000050000069649e-05 >>> expm1(1e-5) # result accurate to full precision 1.0000050000166668e-05 · Added in version 3.2. ... With one argument, return the natural logarithm of x (to base e).

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geeksforgeeks.org › python › log-functions-python

Log functions in Python - GeeksforGeeks

August 14, 2024 - Syntax : math.log2(a) Parameters : a : The numeric value Return Value : Returns logarithm base 2 of a Exceptions : Raises ValueError if a negative no. is passed as argument. ... # Python code to demonstrate the working of # log2(a) import math # Printing the log base 2 of 14 print ("Logarithm base 2 of 14 is : ", end="") print (math.log2(14))

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includehelp.com › python › log-functions-of-math-module.aspx

Log functions of math module in Python

April 20, 2019 - Return value: float – it returns a float value that is the base-10 logarithm of the given number x. # python code to demonstrate example of # different math module log functions # importing math module import math # number x = 10 # natural logarithm (base-e) using log(x) print("natural logarithm of ", x, " is = ", math.log(x)) # logarithm (base-5) using log(x, base) print("base-5 logarithm of ", x, " is = ", math.log(x,5)) # natural logarithm (base-e) of 1+ number using log1p(x) print("natural logarithm of 1+ ", x, " is = ", math.log(1+x)) # base-2 logarithm using log2(x) print("base-2 logarithm of ", x, " is = ", math.log2(x)) # base-10 logarithm using log10(x) print("base-10 logarithm of ", x, " is = ", math.log10(x))

codecademy.com › docs › python › math module › math.log()

Python | Math Module | math.log() | Codecademy

March 17, 2024 - Learn the basics of Python 3.12, one of the most powerful, versatile, and in-demand programming languages today. ... The math.log() function returns a float value, the natural logarithm of n or the logarithm of n to base.

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Python Examples

pythonexamples.org › python-math-log

Python math.log() - Natural Logarithm

Natural logarithm of a negative number. import math x = -2 result = math.log(x) print('log(x) :', result)

stackoverflow.com › questions › 14764339 › math-log-in-python

math.log in Python - Stack Overflow

math.log uses floats and these almost always involve some rounding errors.

>>> math.log(1000, 10)
2.9999999999999996

If you need it to be exact you should change your algorithm to generate the powers of 10 (simply multiply the last power by 10) and continue as long as the new power is smaller than your input number.

>>> limit = 12345
>>> power = 10
>>> while power < limit:
...     print power
...     power = power * 10
... 
10
100
1000
10000

This is guaranteed to be exact as it does not involve any floating point numbers. (And is a lot faster, too)

Floating-point math strikes again! Look at the values returned by math.log:

>>> math.log(10000, 10)
4.0
>>> math.log(1000, 10)
2.9999999999999996
>>> math.log(100, 10)
2.0
>>> math.log(10, 10)
1.0

geeksforgeeks.org › python › python-math-module

Python Math Module - GeeksforGeeks

July 26, 2025 - Helps in performing logarithmic and exponential operations easily. Supports real-world applications like physics, statistics, geometry and finance. To work with mathematical constants and functions in Python, you need to import the math module:

digitalocean.com › community › tutorials › python-log-function-logarithm

Python log() Functions to Calculate Logarithm | DigitalOcean

August 4, 2022 - The math.log(x) function is used to calculate the natural logarithmic value i.e.

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Python math.log() Method - Sarthaks eConnect | Largest Online Education Community

April 10, 2023 - LIVE Course for free · The math.log() method in Python returns the natural logarithm (base e) of a given number. The natural logarithm of a number x, denoted by ln(x), is the power to which e must be raised to equal x

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Power and Logarithmic Functions in Python: exp, log, log10, log2 | note.nkmk.me

August 10, 2023 - math.log() — Mathematical functions — Python 3.11.4 documentation

howtogeek.com › home › programming › the math module in python: 6 common calculations you can make

The math Module in Python: 6 Common Calculations You Can Make

October 5, 2025 - Another common constant you can use with Python is e, or 2.718281828459045. This is the base of the natural logarithm and is commonly used for exponential growth, such as compound interest.

Math Solver - Trusted Online AI Math Calculator | Symbolab

It breaks problems into step-by-step solutions, showing how each part contributes to the answer. Whether it’s solving an equation, simplifying an expression, or working through a calculus limit, Symbolab’s AI Math Solver is designed to help learners follow the logic and build confidence along the way.

levelup.gitconnected.com › recapping-the-math-module-in-python-403ffcac4ce4

Recapping the Math Module in Python | by Ebo Jackson | Level Up Coding

June 15, 2023 - import math x = 1.5 print(math.sinh(x)) # Output: 2.1292794550948173 print(math.cosh(x)) # Output: 2.3524096152432476 print(math.tanh(x)) # Output: 0.905148253644866 · Exponential and logarithmic functions: import math x = 2.0 print(math.exp(x)) # Output: 7.3890560989306495 print(math.log(x)) # Output: 0.6931471805599453 print(math.log10(x)) # Output: 0.3010299956639812 print(math.sqrt(x)) # Output: 1.4142135623730951 ·

tutorialspoint.com › python › number_log.htm

Python math.log() Method

This method returns natural logarithm of x, for x > 0. The following example shows the usage of the Python math.log() method.

reddit.com › r/learnpython › math.log() method complexity

r/learnpython on Reddit: Math.log() method complexity

April 22, 2021 -

I add math.log() method in a function but it took more step to compute than iterative version of the same function unless input is power of 2 because log is base 2. What I want to learn is does math.log() method have more than constant time complexity ?

because log is base 2. No, math.log2() is base 2, math.log() returns the natural logarithm (so base e, 2.71etc). Regardless of that detail how did you establish that 'it took more step to compute'?

...what. Constant time complexity in relation to what?

centron.de › startseite › python log() function to calculate logarithm

Python log() Function to Calculate Logarithm

February 7, 2025 - Python provides several variants of the log() function to calculate logarithms to specific bases or in unique scenarios: ... The math.log2(x) function computes the logarithm of x to the base 2, commonly used in computer science.

matplotlib.org › stable › tutorials › pyplot.html

Pyplot tutorial — Matplotlib 3.10.8 documentation

# Fixing random state for reproducibility np.random.seed(19680801) # make up some data in the open interval (0, 1) y = np.random.normal(loc=0.5, scale=0.4, size=1000) y = y[(y > 0) & (y < 1)] y.sort() x = np.arange(len(y)) # plot with various axes scales plt.figure() # linear plt.subplot(221) plt.plot(x, y) plt.yscale('linear') plt.title('linear') plt.grid(True) # log plt.subplot(222) plt.plot(x, y) plt.yscale('log') plt.title('log') plt.grid(True) # symmetric log plt.subplot(223) plt.plot(x, y - y.mean()) plt.yscale('symlog', linthresh=0.01) plt.title('symlog') plt.grid(True) # logit plt.subp