math.sqrt is the C implementation of square root and is therefore different from using the ** operator which implements Python's built-in pow function. Thus, using math.sqrt actually gives a different answer than using the ** operator and there is indeed a computational reason to prefer numpy or math module implementation over the built-in. Specifically the sqrt functions are probably implemented in the most efficient way possible whereas ** operates over a large number of bases and exponents and is probably unoptimized for the specific case of square root. On the other hand, the built-in pow function handles a few extra cases like "complex numbers, unbounded integer powers, and modular exponentiation".

See this Stack Overflow question for more information on the difference between ** and math.sqrt.

In terms of which is more "Pythonic", I think we need to discuss the very definition of that word. From the official Python glossary, it states that a piece of code or idea is Pythonic if it "closely follows the most common idioms of the Python language, rather than implementing code using concepts common to other languages." In every single other language I can think of, there is some math module with basic square root functions. However there are languages that lack a power operator like ** e.g. C++. So ** is probably more Pythonic, but whether or not it's objectively better depends on the use case.

Answer from Shashank on Stack Overflow
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Exponents in Python: A Comprehensive Guide for Beginners | DataCamp
November 25, 2024 - To get a leg up, solidify your understanding, and become an expert, enroll in our Python Programming Fundamentals skill track. Python offers multiple ways to calculate exponents: **: The double asterisk operator (**) is the simplest and basic option for exponentiation.
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64

math.sqrt is the C implementation of square root and is therefore different from using the ** operator which implements Python's built-in pow function. Thus, using math.sqrt actually gives a different answer than using the ** operator and there is indeed a computational reason to prefer numpy or math module implementation over the built-in. Specifically the sqrt functions are probably implemented in the most efficient way possible whereas ** operates over a large number of bases and exponents and is probably unoptimized for the specific case of square root. On the other hand, the built-in pow function handles a few extra cases like "complex numbers, unbounded integer powers, and modular exponentiation".

See this Stack Overflow question for more information on the difference between ** and math.sqrt.

In terms of which is more "Pythonic", I think we need to discuss the very definition of that word. From the official Python glossary, it states that a piece of code or idea is Pythonic if it "closely follows the most common idioms of the Python language, rather than implementing code using concepts common to other languages." In every single other language I can think of, there is some math module with basic square root functions. However there are languages that lack a power operator like ** e.g. C++. So ** is probably more Pythonic, but whether or not it's objectively better depends on the use case.

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Even in base Python you can do the computation in generic form

result = sum(x**2 for x in some_vector) ** 0.5

x ** 2 is surely not an hack and the computation performed is the same (I checked with cpython source code). I actually find it more readable (and readability counts).

Using instead x ** 0.5 to take the square root doesn't do the exact same computations as math.sqrt as the former (probably) is computed using logarithms and the latter (probably) using the specific numeric instruction of the math processor.

I often use x ** 0.5 simply because I don't want to add math just for that. I'd expect however a specific instruction for the square root to work better (more accurately) than a multi-step operation with logarithms.

Discussions

Why is the power operator much slower than multiplication in Python?
The way most computers compute a power, special cases (NaN, inf, ...) aside, is to do 2^(y*log2(x)) for x^y. This takes 12 x86-64 instructions, although two of them are likely to happen simultaneously. In contrast, x*x is a single x86 instruction. So, for y=2, x*x should be about ten times faster. Python (or any other language) could specialize power to be x*x N times when N <= 10 or so, but the switch statement it takes to specialize the cases isn't free. And the accumulation of floating point errors is not the same between the two. Optimizing pow to a sequence of multiplies is usually done by compilers, which look if you did pow(x,CONSTANT), and decide what to do depending on CONSTANT. That way it is done one time instead of N times at runtime. Python doesn't really have a compiler, so pow is slow for small y, because it would be slower for big y to optimize it for the small ys. Numpy does optimize x**2 to x*x for you, but does not optimize the other powers smaller than ten (actually, I forget if three is also a special optimized case but I know four isn't.) More on reddit.com
๐ŸŒ r/Python
69
217
January 30, 2022
2 ** 1 ** 3 = 2??

This is the standard order of operations for power towers in mathematics:

https://math.hmc.edu/funfacts/tower-of-powers/

I did not know that Python actually handles this correctly. That is awesome.

More on reddit.com
๐ŸŒ r/learnpython
58
118
March 19, 2021
Time complexity of power function
It looks like pow and ** are O(log(n)) where n is the exponent. Here is a good post on Stack overflow that includes links to the CPython implementation source (other implementations could differ): https://stackoverflow.com/a/48848512 Basically math.pow works with a floating point algorithm and the other two methods (same method behind the scenes) may use integers. The integer algorithms use 14.79 or 14.82 out of the Handbook of Applied Cryptography ( https://cacr.uwaterloo.ca/hac/about/chap14.pdf page 26). It's a binary exponentiation algorithm that loops once for each bit of the exponent. It allows for an exact result as opposed to a floating point approximation. More on reddit.com
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1
September 19, 2021
How come e means 10 in Python numbers like 7.3e^-10? Isn't e suppose to be 2.71828..?
Hi u/Key-Hunt-6107 , We noticed you are a pretty new Reddit account, so we just wanted to let you know to check out the subreddit rules here and maybe have a read through our Frequently Asked Questions - they make for fascinating reading! We're called No Stupid Questions because we believe nobody needs to be attacked for asking a question, but that doesn't mean there are no rules! This sub is meant for users like you to ask genuine questions. Please don't ask jokes or rants disguised as questions - that's not in the spirit of this sub. While you can ask almost anything here, please keep illegal and offensive questions elsewhere to give people a good experience here - and if you have a medical question, please ask your doctor, not us. Otherwise, welcome! I am a bot, and this action was performed automatically. Please contact the moderators of this subreddit if you have any questions or concerns. More on reddit.com
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November 26, 2024 - The ** operator is the most intuitive and versatile option for exponentiation. Here are some scenarios where itโ€™s the best fit: When you need to quickly calculate powers in scripts or projects, the ** operator is fast, clean, and easy to read. ... python # Formula: A = P * (1 + r/n)^(nt) principal = 1000 # Initial investment rate = 0.05 # Annual interest rate times_compounded = 4 # Compounded quarterly years = 5 amount = principal * (1 + rate / times_compounded) ** (times_compounded * years) print(f"Future value: ${amount:.2f}") # Output: Future value: $1283.36
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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 #find the exponential of the specified value print(math.exp(65)) print(math.exp(-6.89)) Try it Yourself ยป
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W3Schools offers free online tutorials, references and exercises in all the major languages of the web. Covering popular subjects like HTML, CSS, JavaScript, Python, SQL, Java, and many, many more.
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February 14, 2023 - The last argument is optional, but according to the python documentation on pow, this argument computes more efficiently than pow(base, exponent) % number. ... result1 = pow(100, 3) print(result1) # 1000000 result2 = pow(5, 4) print(result2) # 625 result3 = pow(3, 2, 5) print(result3) # 4 ยท In the last example, we have pow(3, 2, 5).
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1 week ago - The current implementation will raise ValueError for invalid operations like sqrt(-1.0) or log(0.0) (where C99 Annex F recommends signaling invalid operation or divide-by-zero), and OverflowError for results that overflow (for example, exp(1000.0)). A NaN will not be returned from any of the functions above unless one or more of the input arguments was a NaN; in that case, most functions will return a NaN, but (again following C99 Annex F) there are some exceptions to this rule, for example pow(float('nan'), 0.0) or hypot(float('nan'), float('inf')). Note that Python makes no effort to disting
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However, keyboards and character sets donโ€™t know about superscripts,1 and so the designers of programming languages had to come up with different ways of writing exponentiation. ** was first used in Fortran, which first appeared in 1957. This is the operator which Python uses for exponentiation.
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June 15, 2025 - Medium Stories covering applications of Exponentiation and Logarithms: ... specialised math.exp(x) generates e^x where e = 2.718281โ€ฆ is the base of natural logarithms. This is usually more accurate than math.e ** x or pow(math.e, x). ... ...
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Binary Exponentiation โ€” Faster way to calculate Pow(x,n) [Python] | by Pritul Dave :) | Geek Culture | Medium
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