Computations on Arrays

Computations on Arrays

• Computation on NumPy arrays can be very fast, or it can be very slow. Using vectorized operations, fast computations is possible and it is implemented by using NumPy's universial functions (ufuncs).

• A universal function (ufuncs) is a function that operates on ndarrays in an element-by- element fashion, supporting array broadcasting, type casting, and several other standard features. The ufunc is a "vectorized" wrapper for a function that takes a fixed number of specific inputs and produces a fixed number of specific outputs.

• Functions that work on both scalars and arrays are known as ufuncs. For arrays, ufuncs apply the function in an element-wise fashion. Use of ufuncs is an esssential aspect of vectorization and typically much more computtionally efficient than using an explicit loop over each element.

NumPy's Ufuncs :

• Ufuncs are of two types: unary ufuncs and binary ufuncs.

• Unary ufuncs operate on a single input and binary ufuncs, which operate on two inputs.

• Arithmetic operators implemented in NumPy is as follows:

• Example of Arithmetic Operators: Python Code

# Taking input

num1 = input('Enter first number:')

num2 = input('Enter second number:')

# Addition

sum = float(num1) + float(num2)

# Subtraction

min =float(num1) - float(num2)

# Multiplication

mul = float(num1)* float(num2)

#Division

div = float(num1) / float(num2)

#Modulus

mod = float(num1) % float(num2)

#Exponentiation

exp =float(num1)**float(num2)

#Floor Division

floordiv = float(num1) // float(num2)

print("The sum of {0} and {1} is {2}'.format(num1, num2, sum))

print("The subtraction of {0} and {1} is {2}'.format(num1, num2, min))

print("The multiplication of {0} and {1} is {2}'.format(num1, num2, mul))

print("The division of {0} and {1} is {2}'.format(num1, num2, div))

print("The modulus of {0} and {1} is {2}'.format(num1, num2, mod))

print("The exponentiation of {0} and {1} is {2}'.format(num1, num2, exp))

print("The floor division between {0} and {1} is {2}'.format(num1, num2,floordiv))

Absolute value :

• NumPy understands Python's built-in arithmetic operators, it also understands Python's built-in absolute value function. The abs() function returns the absolute magnitude or value of input passed to it as an argument. It returns the actual value of input without taking the sign into consideration.

• The abs() function accepts only a single arguement that has to be a number and it returns the absolute magnitude of the number. If the input is of type integer or float, the abs() function returns the absolute magnitude/value. If the input is a complex number, the abs() function returns only the magnitude portion of the number.

Syntax: abs(number)

Where the number can be of integer type, floating point type or a complex number.

• Example:

num -25.79

print("Absolute value:", abs(num))

• Output:                

Absolute value : 25.79

Trigonometric functions:

• The numpy package provides trigonometric functions which can be used to calculate trigonometric ratios for a given angle in radians.

Example:

importnumpy as np

Arr = np.array([0, 30, 60, 90])

#converting the angles in radians

Arr = Arr*np.pi/180

print("\nThe sin value of angles:")

print(np.sin(Arr))

print("\nThe cos value of angles:")

print(np.cos(Arr))

print("\nThe tan value of angles:")

print(np.tan(Arr))