The Wayback Machine - https://web.archive.org/web/20250121185110/https://www.geeksforgeeks.org/numpy-log2-python/

numpy.log2() in Python

Last Updated : 29 Nov, 2018

numpy.log2(arr, out = None, *, where = True, casting = ‘same_kind’, order = ‘K’, dtype = None, ufunc ‘log1p’) :
This mathematical function helps user to calculate Base-2 logarithm of x where x belongs to all the input array elements.

Parameters :

array    : [array_like]Input array or object.
out      : [ndarray, optional]Output array with same dimensions as Input array, 
           placed with result.
**kwargs : Allows you to pass keyword variable length of argument to a function. 
           It is used when we want to handle named argument in a function.
where    : [array_like, optional]True value means to calculate the universal 
           functions(ufunc) at that position, False value means to leave the 
           value in the output alone.

Return :

An array with Base-2 logarithmic value of x; 
where x belongs to all elements of input array.

Code 1 : Working

# Python program explaining 
# log2() function 
import numpy as np 
  
in_array = [1, 3, 5, 2**8] 
print ("Input array : ", in_array) 
  
out_array = np.log2(in_array) 
print ("Output array : ", out_array) 
  
  
print("\nnp.log2(4**4) : ", np.log2(4**4)) 
print("np.log2(2**8) : ", np.log2(2**8)) 

Output :

Input array :  [1, 3, 5, 256]
Output array :  [ 0.          1.5849625   2.32192809  8.        ]

np.log2(4**4) :  8.0
np.log2(2**8) :  8.0

Code 2 : Graphical representation

# Python program showing 
# Graphical representation of  
# log2() function 
import numpy as np 
import matplotlib.pyplot as plt 
  
in_array = [1, 1.2, 1.4, 1.6, 1.8, 2] 
out_array = np.log2(in_array) 
  
print ("out_array : ", out_array) 
  
plt.plot(in_array, in_array, color = 'blue', marker = "*") 
  
# red for numpy.log2() 
plt.plot(out_array, in_array, color = 'red', marker = "o") 
plt.title("numpy.log2()") 
plt.xlabel("out_array") 
plt.ylabel("in_array") 
plt.show()   

Output :

out_array :  [ 0.          0.26303441  0.48542683  0.67807191  0.84799691  1.        ]

References :
https://docs.scipy.org/doc/numpy-1.13.0/reference/generated/numpy.exp.html
.

numpy.log() in Python

M

Mohit Gupta_OMG :)

News

Improve

Article Tags :

Practice Tags :

python

Similar Reads

numpy.log2() in Python

numpy.log2(arr, out = None, *, where = True, casting = 'same_kind', order = 'K', dtype = None, ufunc 'log1p') : This mathematical function helps user to calculate Base-2 logarithm of x where x belongs to all the input array elements. Parameters : array : [array_like]Input array or object. out : [nda

numpy.log() in Python

The numpy.log() is a mathematical function that helps user to calculate Natural logarithm of x where x belongs to all the input array elements. Natural logarithm log is the inverse of the exp(), so that log(exp(x)) = x. The natural logarithm is log in base e. Syntax :numpy.log(x[, out] = ufunc 'log1

numpy.log10() in Python

About : numpy.log10(arr, out = None, *, where = True, casting = 'same_kind', order = 'K', dtype = None, ufunc 'log10') : This mathematical function helps user to calculate Base-10 logarithm of x where x belongs to all the input array elements. Parameters : array : [array_like]Input array or object.

numpy.log1p() in Python

numpy.log1p(arr, out = None, *, where = True, casting = 'same_kind', order = 'K', dtype = None, ufunc 'log1p') : This mathematical function helps user to calculate natural logarithmic value of x+1 where x belongs to all the input array elements. log1p is reverse of exp(x) - 1. Parameters : array : [

numpy.logaddexp2() in Python

numpy.logaddexp2() function is used to calculate Logarithm of the sum of exponentiations of the inputs in base-2. This function is useful in machine learning when the calculated probabilities of events may be so small as to exceed the range of normal floating point numbers. In such cases, the base-2

numpy.logspace() in Python

The numpy.logspace() function returns number spaces evenly w.r.t interval on a log scale. Syntax : numpy.logspace(start, stop, num = 50, endpoint = True, base = 10.0, dtype = None) Parameters : -> start : [float] start(base ** start) of interval range. -> stop : [float] end(base ** stop) of in

numpy.logaddexp() in Python

numpy.logaddexp() function is used to calculate Logarithm of the sum of exponentiations of the inputs. This function is useful in statistics where the calculated probabilities of events may be so small as to exceed the range of normal floating point numbers. In such cases, the logarithm of the calcu

numpy.exp() in Python

numpy.exp(array, out = None, where = True, casting = 'same_kind', order = 'K', dtype = None) : This mathematical function helps user to calculate exponential of all the elements in the input array. Parameters : array : [array_like]Input array or object whose elements, we need to test. out : [ndarray

Logging in Python

Logging is a means of tracking events that happen when some software runs. Logging is important for software developing, debugging, and running. If you don't have any logging record and your program crashes, there are very few chances that you detect the cause of the problem. And if you detect the c

numpy.exp2() in Python

numpy.exp2(array, out = None, where = True, casting = 'same_kind', order = 'K', dtype = None) : This mathematical function helps user to calculate 2**x for all x being the array elements. Parameters : array : [array_like]Input array or object whose elements, we need to test. out : [ndarray, optional

numpy.i0() function | Python

numpy.i0() function is the modified Bessel function of the first kind, order 0. it's usually denoted by I0. Syntax : numpy.i0(x) Parameters : x : [array_like, dtype float or complex] Argument of the Bessel function. Return : [ndarray, shape = x.shape, dtype = x.dtype] The modified Bessel function ev

numpy.info() function in Python

In Numpy we can get all the information about the function, class, or module like what will the parameter and what will be the type of the return value with the help of numpy.info() function. This function returns the help information for a function, class, or module. Syntax: numpy.info(numpy.info(o

numpy.binary_repr() in Python

numpy.binary_repr(number, width=None) function is used to represent binary form of the input number as a string. For negative numbers, if width is not given, a minus sign is added to the front. If width is given, the two’s complement of the number is returned, with respect to that width. In a two’s-

bin() in Python

Python bin() function returns the binary string of a given integer. bin() function is used to convert integer to binary string. In this article, we will learn more about Python bin() function. Example In this example, we are using the bin() function to convert integer to binary string. C/C++ Code x

sympy.log() method in Python

With the help of sympy.log() function, we can simplify the principal branch of the natural logarithm. Logarithms are taken with the natural base, e. To get a logarithm of a different base b, use log(x, y), which is essentially short-hand for log(x) / log(y). Syntax : sympy.log() Return : Return the

numpy.fromstring() function – Python

numpy.fromstring() function create a new one-dimensional array initialized from text data in a string. Syntax : numpy.fromstring(string, dtype = float, count = -1, sep = ' ') Parameters : string : [str] A string that contained the data. dtype : [data-type, optional] Data-type of the array. Default d

float() in Python

Python float() function is used to return a floating-point number from a number or a string representation of a numeric value. Example: Here is a simple example of the Python float() function which takes an integer as the parameter and returns its float value. C/C++ Code # convert integer value to f

Python | Numpy np.logseries() method

With the help of np.logseries() method, we can get the log series in the form of an array by using np.logseries() method. Syntax : np.logseries(p, size) Return : Return an array of log series. Example #1 : In this example we can see that by using np.logseries() method, we are able to get an array of

Python | Numpy np.lognormal() method

With the help of np.lognormal() method, we can get the log normal distribution values using np.lognormal() method. Syntax : np.lognormal(mean, sigma, size) Return : Return the array of log normal distribution. Example #1 : In this example we can see that by using np.lognormal() method, we are able t