**Operator to Do Exponent in Python
math.power()to Do Exponent in Python
numpy.np()to Do Exponent in Python
- Compare Runtimes for Each Solution
This tutorial will demonstrate how to do exponentiations in Python.
In mathematics, exponentiation is an operation where a number is multiplied several times with itself.
Python provides built-in operations and functions to help perform exponentiation.
** Operator to Do Exponent in Python
Most developers seem to think the caret symbol
^ is the operator for getting the power of a number because the caret is used as a symbol for exponents in mathematics. However, in most programming languages, the caret symbol is reserved for the bit-wise
In Python, the exponent operator is symbolized by two consecutive asterisks
** between the base and exponent number.
The exponent operator’s functionality supplements the behavior of the multiplication operator
*; the difference is the second operand in the exponent operator is for setting the number of times the first operand is multiplied by itself.
To multiply the number
5 by itself
6 times, use the operator
** in between the base operand
5 and the exponent operand
Let’s test this operator on different types of values.
We will initialize a whole number, a whole negative number, zero, and two
float values lesser than
1 and greater than
1. Then we’ll assign random integers as their exponents.
num1 = 2 num2 = -5 num3 = 0 num4 = 1.025 num5 = 0.5 print(num1,'^12=', num1**12) print(num2,'^4=', num2**4) print(num3,'^9999=', num3**9999) print(num4,'^-3=', num4**-3) print(num5,'^8=', num5**8)
2^12= 4096 -5^4= 625 0^9999= 0 1.025^-3= 0.928599410919749 0.5^8= 0.00390625
math.power() to Do Exponent in Python
Another way to do exponent in Python is to use the function
pow() designed to exponentiate values given the base and the exponent. The
math module also has its own implementation of
pow() for the same purpose.
Both these functions have 2 arguments, the first argument is for the base number, and the second is for the exponent.
Let’s try calling both functions multiple times with the same arguments so we can compare their outputs.
import math print(pow(-8, 7)) print(math.pow(-8, 7)) print(pow(2, 1.5)) print(math.pow(2, 1.5)) print(pow(4, 3)) print(math.pow(4,3)) print(pow(2.0, 5)) print(math.pow(2.0, 5))
-2097152 -2097152.0 2.8284271247461903 2.8284271247461903 64 64.0 32.0 32.0
The only difference in the results is
math.pow() always returns a
float value even if whole number arguments are passed, while
pow() will only return
float if there is at least one
numpy.np() to Do Exponent in Python
NumPy also has its own function
power() for exponentiation.
power() accepts the same arguments as the
pow() functions, where the first argument is the base value and the 2nd argument is the exponent value.
NumPy, we should install it via
- Python 2:
pip install numpy
- Python 3:
pip3 install numpy
Let’s print out the same set of examples in
print(np.power(-8, 7)) print(np.power(2, 1.5)) print(np.power(4, 3)) print(np.power(2.0, 5))
-2097152 2.8284271247461903 64 32.0
power() produces the same output as the built-in Python function
pow() where it will return a whole number if there are no
Compare Runtimes for Each Solution
Let’s compare the time it takes for these 3 functions and the
** operator to run with a large exponent value. For timing functions, we’ll import the
timeit module to print out each of the solutions’ runtime.
The base’s value will be
2, and the value for the exponent will be
import numpy as np import math import time start = time.process_time() val = 2**99999 print('** took',time.process_time() - start,'ms') start = time.process_time() val = pow(2,99999) print('pow() took',time.process_time() - start,'ms') start = time.process_time() val = np.power(2,99999) print('np.power() took',time.process_time() - start,'ms') start = time.process_time() val = math.pow(2,99999) print('math.pow() took',time.process_time() - start,'ms')
** took 0.0006959999999999744 ms pow() took 0.00039000000000000146 ms np.power() took 1.6999999999989246e-05 ms Traceback (most recent call last): File "/Users/rayven/python/timeit.py", line 15, in <module> val = math.pow(2,99999) OverflowError: math range error
The most obvious thing to note is
math.pow() resulted in an
OverflowError. This means that
math.pow() can’t support large-valued exponents, most likely because of the way that this module has implemented exponentiation.
The difference between the 3 other methods are trivial, but from this example,
np.power() is the fastest function to perform exponentiation.
What if we try reducing the exponent to
9999? Let’s see what
** took 1.0000000000010001e-05 ms pow() took 4.000000000004e-06 ms np.power() took 2.0000000000020002e-05 ms math.pow() took 2.9999999999752447e-06 ms