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]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 2**x(power of 2) for all x i.e. array elements
Code 1 : Working
# Python program explaining # exp2() function import numpy as np in_array = [1, 3, 5, 4] print ("Input array : \n", in_array) exp2_values = np.exp2(in_array) print ("\n2**x values : \n", exp2_values) |
Output :
Input array : [1, 3, 5, 4] 2**x values : [ 2. 8. 32. 16.]
Code 2 : Graphical representation
# Python program showing # Graphical representation of # exp2() function import numpy as np import matplotlib.pyplot as plt in_array = [1, 2, 3, 4, 5 ,6] out_array = np.exp2(in_array) print("out_array : ", out_array) y = [1, 2, 3, 4, 5 ,6] plt.plot(in_array, y, color = 'blue', marker = "*") # red for numpy.exp2() plt.plot(out_array, y, color = 'red', marker = "o") plt.title("numpy.exp2()") plt.xlabel("X") plt.ylabel("Y") plt.show() |
Output :
out_array : [ 2. 4. 8. 16. 32. 64.]
References :
https://docs.scipy.org/doc/numpy-1.13.0/reference/generated/numpy.exp2.html
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