NumPy Vectorize in Python

Introduction:

In this tutorial we are learning about the NumPy Vectorize in Python. Python offers different functions to its users. For vectorization, the Python library provides the NumPy function. NumPy vectorization accepts a NumPy array or hierarchical-level variables as input to the system and produces a NumPy array or multiple NumPy arrays. NumPy vectorization treats pyfunc like a Python function map after multiple consecutive inputs and helps interpret NumPy code. We use NumPy vectorization instead of loops to increase speed. Arrays play an important role in data science, where speed is important. Basically, NumPy is an open-source project. In Python, NumPy is faster than lists. Therefore, work and manipulating can be done well.

A Numpy array is inherently homogeneous. This means that it is an array containing only one type of data. Python lists and tuples contain unrestricted data. NumPy's concept of vectorized operations allows many optimizations, preprocessing, and arithmetic operations to be used on NumPy array objects and data arrays. The output and processing will be faster compared to simple non-vectorized functions.

Syntax:

The syntax of the NumPy Vectorize in Python is given below -

Parameters:

The parameters of the NumPy Vectorize in Python are given below -

• pyfunc: This parameter defines required Python functions and methods, which is why it is called a parameter.
• otypes: This parameter represents the output data type, which is optional. In otype, a string must be specified as a list or type code symbol of the specified data type. Data must be specified for each output.
• doc: This parameter is an optional parameter for a docstring. If it is not in the document, the docstring will be pyfunc_doc_str.
• exclude: This parameter is optional. It contains a string or number representing the function's positional or key that will not be vectorized. A set of strings or numbers can be passed directly to pyfunc without modification.
• cache: This is an optional parameter. It caches the first function call; if correct and otypes are not provided, this usually determines the output. 
• Signature: This is an optional parameter. It is a generalized function. For example, (x, y), (y) -> (x) is used for vectorized matrix-vector function multiplication. But if provided, pyfunc will be called (and should return) with an array whose shape is given by the approximate size of the view. By default, Pyfunc should accept scalers as input and output.

Program Code 1:

Here we give a program code of the NumPy Vectorize in Python. We used here the vectorized addition methods on NumPy arrays. We will compare the vectorized addition method with a simple non-vectorized method, which is an iterative method used for calculating numbers from 0 to 16,999. The code is given below -

Output:

Now, we run the above code and find the result of vectorized addition methods on NumPy arrays. The above code shows that NumPy's vectorized functions have better features than non-vectorized functions. This means that when efficiency calculations are important in the program, we should not use these simple functions and use NumPy vectorized functions. The output is given below -

 144491500 The time taken by the vectorized sum is: 65.5 µs ± 2.9 µs per loop (mean ± std. dev. of 7 runs, 10000 loops each) 144491500 The time taken by the iterative sum is: 72.3 ns ± 3.28 ns per loop (mean ± std. dev. of 7 runs, 10000000 loops each) 

Program Code 2:

Here we give another program code of the NumPy Vectorize in Python. Here, we will compare the NumPy exponential function with the Python built-in math library exponential function to calculate the exponential value of each entry in a given object. The code is given below -

Output:

Now, we run the above code and find the calculate the exponential value of each entry in a given object. Here, we can see that NumPy vectorization works better in terms of calculation results, and the Python math library has another limitation, which is the limitation of many math libraries. Because, unlike NumPy vectorization, it is only suitable for a small value. Arithmetic functions can be used to calculate exponential values with a very large range. The output is given below -

Program Code 3:

Here we give another program code of the NumPy Vectorize function in Python. The code is given below -

Output:

Now, we run the above code and find the output from it. In this example, we use numpy vectorization. We define a vectorized function where a and b are parameters. The Vectorize function used in the example above reduces the length of the code. In this example, x operates directly on the array. Use the output below to show the end of the above statement. The output is given below -

Program Code 4:

Here we give another program code of the NumPy Vectorize docstring in Python. The code is given below -

Output:

Now, we run the above code and find the output from it. For vectorization, the docstring is retrieved from the input function unless a docstring is specified. Use the output below to show the end of the above code. The output is given below -

 welcome to Javatpoint 

Program Code 5:

Here, we give another program code of the excluded, which is used to stop the vectorization over some arguments in Python. The code is given below -

Output:

Now, we run the above code and find the output from it. The excluded is used to stop the vectorization of parameters. In this example, we use polynomials as in polyval. Finally, the output below is used to show the end of the above code. The output is given below -

 [194 310] 

Conclusion:

So, in this tutorial, we are learning about the NumPy Vectorize in Python. We learned the basics of numpy vectorized functions from the above article. We also learn how to use them in Python through different examples of each parameter. With the help of vectorized functions, we reduce the execution time of the algorithm. From this article, we learn how to solve numpy vectorization in Python. Here, we learn the syntax and parameters of the NumPy Vectorize in Python. We also learn some program code about it.