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# NumPy vectorization examples

### Python Examples of numpy

The following are 30 code examples for showing how to use numpy.vectorize(). These examples are extracted from open source projects. You can vote up the ones you like or vote down the ones you don't like, and go to the original project or source file by following the links above each example. You may check out the related API usage on the sidebar import numpy as np from timeit import Timer # Create 2 vectors of same length length1 = 1000 length2 = 500 vector1 = np.random.randint(1000, size=length1) vector2 = np.random.randint(1000, size=length2) # Finds outer product of vectors using for loop def outerproduct_forloop(): outer_product = np.zeros((length1, length2), dtype='int') for i in range(length1): for j in range(length2): outer_product[i, j] = vector1[i] * vector2[j] # Finds outer product of vectors using numpy vectorization def. The concept of vectorized operations on NumPy allows the use of more optimal and pre-compiled functions and mathematical operations on NumPy array objects and data sequences. The Output and Operations will speed-up when compared to simple non-vectorized operations. Example 1 : Using vectorized sum method on NumPy array. We will compare the vectorized sum method along with simple non-vectorized operation i.e the iterative method to calculate the sum of numbers from 0 - 14,999 Dump the loops: Vectorization with NumPy Many calculations require to repeatedly do the same operations with all items in one or several sequences, e.g. multiplying two vectors a = [1, 2, 3, 4, 5] and b = [6, 7, 8, 9, 10]

The video breaks down several examples of using a variety of manipulation operations—Python for-loops, NumPy array vectorization, and a variety of Pandas methods—and compares the speed that. Example import time import numpy import array p = array.array('q') for i in range(100000,200000): p.append(i); q = array.array('q') for i in range(200000, 300000): q.append(i) # classic dot product tic = time.process_time() dot_value = 0.0; for i in range(len(a)): dot_value += p[i] * q[i] toc = time.process_time() print(dot_product of vector arrays = + str(dot_value)); print(Computation time taken = + str(1000*(toc - tic )) + ms) n_tic = time.process_time() n_dot_product.

multiply (a, b): Matrix product of two arrays. dot (a, b): Dot product of two arrays. zeros ( (n, m)): Return a matrix of given shape and type, filled with zeros. process_time (): Return the value (in fractional seconds) of the sum of the system and user CPU time of the current process The use of vectorization allows numpy to perform matrix operations more efficiently by avoiding many for loops. I will include the me a ning, background description and code examples for each matrix operation discussing in this article. The Key Takeaways section at the end of this article will provide you with some more specific facts and a brief summary of matrix operations. So, make. Let's think of this easy example (I understand this is a very basic example and I don't have to use numpy.vectorize at all. I am just asking for an example): I am just asking for an example): aa = [[1,2,3,4], [2,3,4,5], [5,6,7,8], [9,10,11,12]] bb = [[100,200,300,400], [100,200,300,400], [100,200,300,400], [100,200,300,400] Example: import numpy as np def g(x,p): return p+x*p+x*x*p print(g(5,[0,0,1])) vg = np.vectorize(g, excluded=['p']) print(vg(x=[0,1,2,3,4,5],p=[0,0,1])) # p will not be iterated Shar

### Vectorization in Python - A Complete Guide - AskPytho

• Vectorization is a powerful ability within NumPy to express operations as occurring on entire arrays rather than their individual elements. Here's a concise definition from Wes McKinney: This practice of replacing explicit loops with array expressions is commonly referred to as vectorization. In general, vectorized array operations will often be one or two (or more) orders of magnitude faster than their pure Python equivalents, with the biggest impact [seen] in any kind of numerical.
• NumPy.vectorize() method Example: >>> import numpy as np >>> def my_func(x, y): Return x-y if x>y, otherwise return x+y if x > y: return x - y else: return x + y >>> vec_func = np.vectorize(my_func) >>> vec_func([2, 4, 6, 8], 4) Output: array([6, 8, 2, 4]
• def performance_example(): # Simple function, already supports vectorization f_vec = sampling_function( lambda x: x ** 2, domain=odl.IntervalProd(0, 1) ) # Vectorized with NumPy's poor man's vectorization function f_novec = np.vectorize(lambda x: x ** 2) # We test both versions with 10000 evaluation points
• For example, you can use it for a vectorized calculation of Pearson correlation coefficient and its p-value: >>> import scipy.stats >>> pearsonr = np . vectorize ( scipy . stats . pearsonr , signature = '(n),(n)->(),()' ) >>> pearsonr ([[ 0 , 1 , 2 , 3 ]], [[ 1 , 2 , 3 , 4 ], [ 4 , 3 , 2 , 1 ]]) (array([ 1., -1.]), array([ 0., 0.])
• This accepts any sequence-like object (including other arrays) and produces a new NumPy array containing the passed data. For example, a list is a good candidate for conversion: In : data1 = [6, 7.5, 8, 0, 1] In : arr1 = np.array(data1) In : arr1 Out: array([ 6. , 7.5, 8. , 0. , 1 The rank is the total number of dimensions a NumPy array has. For example, an array of shape (3, 4) has a rank of 2 and array of shape (3, 4, 3) has a rank of 3. Now onto the rules Diving into NumPy vectorization tricks for multivariate time series data. Syafiq Kamarul Azman. Jun 19, 2020 · 15 min read. Photo by bin foch on Unsplash. Once in a while, you get to work with the underdog of the data world: time series (images and natura l language have been in the limelight a lot, recently!). I've been lucky (or unlucky) enough to have had to work on time series data for. Complete Course Deep Learning playlist: https://www.youtube.com/playlist?list=PL1w8k37X_6L95W33vEXSE9jXJOfvNB3l8=====Best Books on Machine Learning.. Example 5: a = 3 x 4 x 1 x 5. b = 3 x 2 x 3. Result: ValueError. Here as well, the second dimension doesn't match and is neither 1 for either of them. Before You Go. Vectorization and Broadcasting, both, are methods how Numpy makes its processing optimized and more efficient. These concepts should be kept in mind especially when dealing with matrices and n-dimensional arrays, which are very common in image data and Neural Networks By the end, you will be familiar with the significant technological trends driving the rise of deep learning; build, train, and apply fully connected deep neural networks; implement efficient (vectorized) neural networks; identify key parameters in a neural network's architecture; and apply deep learning to your own applications

this is very trivial example though... thus, c is our masking vector which we use to perform binary operation based on its value. This avoid branching of execution flow and enables vectorization. Vectorization is as important as Parallelization. Thus, we should make use of it as much possible. All modern days processors have SIMD instructions for heavy compute workloads. We can optimize our code to use these SIMD instructions using vectorization, this is similar to parrallelizing. Take the dot product as an example: Numpy vectorization has a reputation in the industry for being fast by capitalizing on parallelization. Indeed, Numpy is fast. Its iterators compile back to C, but their implementation is fast for reasons other than C itself. The base iterator does an excellent job of encapsulating the kernel of commonality between collection operations. As technologists. import numpy as np import time # Number of features n = 1000 # Number of training examples m = 10000 # Initialize X and W X = np.random.rand(n,m) W = np.random.rand(n,1) # Vectorized code t1=time.time() Z = np.dot(W.T,X) print(Time taken for vectorized code is : ,(time.time()-t1)*1000,ms) # Non Vectorized code Z1 = np.zeros((1,m)) t2 = time.time() for i in range(X.shape): for j in range(X.shape): Z[i] += W[j]*X[j][i] print(Time taken for non vectorized code is. NumPy vectorization with integration. The function quad executes an adaptive algorithm, which means the computations it performs depend on the specific thing being integrated. This cannot be vectorized in principle. In your case, a for loop of length 10 is a non-issue. If the program takes long, it's because integration takes long, not because.

Message #1: If you can use numpy's native functions, do that. If the function you're trying to vectorize already is vectorized (like the x**2 example in the original post), using that is much faster than anything else (note the log scale): If you actually need vectorization, it doesn't really matter much which variant you use Vectorization and the data type of python numpy.ndarray: After the import statements, three functions f, df and F are defined, which the Evaluate function f, its derivative f 'and an antiderivative F. To work with For numpy and scipy it is important that the functions f, df and F are not just for individual Numbers, but work for whole arrays of numbers. An array is one ordered sequence of.

### Vectorized Operations in NumPy - GeeksforGeek

• When we run this simple example ten times, then on average the NumPy version is 961 times faster than the pure python implementation. The speedup is big because we are handling a large data set, but it will be noticeable for smaller tasks too. Maybe you think this task was too far fetched, and its rare that we want to calculate the average of that many images, then consider the next task.
• Example 1. Project: odl Author: odlgroup File: vectorization.py License: Mozilla Public License 2.0. 6 votes. def performance_example(): # Simple function, already supports vectorization f_vec = sampling_function( lambda x: x ** 2, domain=odl.IntervalProd(0, 1) ) # Vectorized with NumPy's poor man's vectorization function f_novec = np.vectorize.
• Get code examples like numpy vectorization functions instantly right from your google search results with the Grepper Chrome Extension
• numpy, linear algebra, vectorization 1 NumPy and Linear Algebra arrays and matrices linear algebra 2 Vectorizations using numpy.vectorize using numpy.where 3 Particle Movements basic version of the simulation vectorized implementation the game of life of John Conway MCS 507 Lecture 4 Mathematical, Statistical and Scientiﬁc Software Jan Verschelde, 4 September 2019 Scientiﬁc Software (MCS.
• Using NumPy arrays enables you to express many kinds of data processing tasks as concise array expressions that might otherwise require writing loops. This practice of replacing explicit loops with array expressions is commonly referred to as vectorization. In general, vectorized array operations will often be one or two (or more) orders of.

Vectorization is the process of performing the same operation in the same way for each element in an array. As an example, NumPy represents the Unicode character ������ with the bytes 0xF4 0x01 0x00 with a dtype of '<U1' and 0x00 0x01 0xF4 with a dtype of '>U1'. Try it out by creating an array full of emoji, setting the dtype to one or the other, and then calling .tobytes() on your. This allows you to achieve parallelized computation, for example fully use the processors of GPU. In this post, the implementation of vectorization of machine learing is introduced. All the code used in this post can be found in my github. Prerequisite: Numpy Array. The m ost important tool we will use in this vectorization process in numpy array. Note that, we don't use numpy matrix since. Numba is a just-in-time compiler for Python that works amazingly with NumPy. Does that mean we should alway use Numba? Well, let's try some examples out and learn. If you know about NumPy, you know you should use vectorization to get speed. Does Numba beat that? Step 1: Let's learn how Numba work

### Vectorization and parallelization in Python with NumPy and

• Vectorization is jargon for a classic approach of converting input data from its raw format (i.e. text ) into vectors of real numbers which is the format that ML models support. This approach has been there ever since computers were first built, it has worked wonderfully across various domains, and it's now used in NLP. In Machine Learning, vectorization is a step in feature extraction. The.
• In this example, we have used the np.arcsin() method to find the inverse sin value of the elements of the array. Numpy sin vs Math sin: math.sin works on a single number, the numpy version works on numpy arrays and is tremendously faster due to the benefits of vectorization
• Message #1: If you can use numpy's native functions, do that. If the function you're trying to vectorize already is vectorized (like the x**2 example in the original post), using that is much faster than anything else (note the log scale): If you actually need vectorization, it doesn't really matter much which variant you use
• Step 1: Example of Vectorization slower than Numba. In the previous tutorial we only investigated an example of vectorization, which was faster than Numba. Here we will see, that this is not always the case. import numpy as np from numba import jit import time size = 100 x = np.random.rand(size, size) y = np.random.rand(size, size) iterations = 100000 @jit(nopython=True) def add_numba(a, b): c.
• The vectorized version of this example is much faster than the looped one. Using broadcasting of Numpy not only speed up writing code, it's also faster the execution of it. This means that we actually double the calculations. @vincenzo.lavorini. Vincenzo Lavorini. Data Scientist. From this series: The example of the mean shift clustering in Poincaré ball space; Vectorizing the loops with.
• Arithmetic Operations with NumPy Arrays. For the examples in this section, we will use the nums array that we created in the last section. Let's first add two arrays together: nums3 = nums + nums You can add two arrays together with the same dimensions. For instance, the nums array contained 15 elements, therefore we can add it to itself. The elements at the corresponding indexes will be added.

NumPy Vectorization. Readability vs. Speed. Anatomy of an Array. Introduction. Memory layout. Views and Copies. Coding Example: How to find if one vector is view of the other? Solution Review . Code Vectorization. Introduction. Uniform Vectorization. Coding Example: Game of life (Python approach) Coding Example: Game of life (NumPy approach) Coding Example: Reaction-Diffusion. Temporal. Python Debugging & Numpy Basics CS 5670 Qianqian Wang, Kai Zhang and the CS5670 Staf python-3.x numpy (2) . Les boucles Python for sont intrinsèquement plus lentes que leurs homologues C. C'est pourquoi numpy propose des actions vectorisées sur des tableaux numpy.Cela pousse la boucle for vous feriez habituellement en Python jusqu'au niveau C, ce qui est beaucoup plus rapide This lesson gives a brief introduction to code vectorization and explains an example using pure Python and NumPy Numpy Vectorization - AskPython › On roundup of the best images on www.askpython.com Images. Posted: (1 day ago) The vectorized version of the function takes a sequence of objects or NumPy arrays as input and evaluates the Python function over each element of the input sequence. Numpy Vectorization essentially functions like the python map() but with additional functionality - the NumPy.

Fast and versatile, the NumPy vectorization, indexing, and broadcasting concepts are the de-facto standards of array computing today. NumPy offers comprehensive mathematical functions, random number generators, linear algebra routines, Fourier transforms, and more. NumPy supports a wide range of hardware and computing platforms, and plays well. Code vectorization means that the problem you're trying to solve is inherently vectorizable and only requires a few NumPy tricks to make it faster. Of course it does not mean it is easy or straightforward, but at least it does not necessitate totally rethinking your problem (as it will be the case in the `Problem vectorization`_ chapter) Vectorization and parallelization in Python with NumPy and Pandas , Array indexing refers to any use of the square brackets ([]) to index array values. It work exactly like that for other standard Python sequences. For all cases of index arrays, what is returned is a copy of the original data, Here the 4th and 5th rows are selected from the indexed array and combined to make a 2-D array. How. Python Examples of numpy.vectorize › Most Popular Law Newest at www.programcreek.com Courses. Posted: (3 days ago) The following are 30 code examples for showing how to use numpy.vectorize(). These examples are extracted from open source projects. You can vote up the ones you like or vote down the ones you don't like, and go to the original project or source file by following the links above. Hello! Welcome to the 3rd tutorial of NumPy: Arithmetic Operations on NumPy Arrays. In this tutorial, I discuss the following things with examples. NumPy ufuncs which stand for universal function

### Understanding Vectorization in NumPy and Pandas by Mike

1. But as it turns out, NumPy is also capable of handling operations between arrays of different sizes. The only criteria being that, NumPy should be able to extend all the arrays involved in an operation to a common shape. This is what we call Broadcasting. Let me give couple of examples to further elaborate on this idea
2. numpy.vectorize¶ class numpy.vectorize (pyfunc, otypes=None, doc=None, excluded=None, cache=False, signature=None) [source] ¶. Generalized function class. Define a vectorized function which takes a nested sequence of objects or numpy arrays as inputs and returns an single or tuple of numpy array as output
3. Chapter 4. NumPy Foundations. As you may recall from Chapter 1, NumPy is the core package for scientific computing in Python, providing support for array-based calculations and linear algebra.As NumPy is the backbone of pandas, I am going to introduce its basics in this chapter: after explaining what a NumPy array is, we will look into vectorization and broadcasting, two important concepts.
4. >>> vfunc = np.vectorize(myfunc) >>> vfunc([1, 2, 3, 4], 2) array([3, 4, 1, 2]

1 Python multiprocessing VS Numpy vectorization - one example 2 Parallelized vectorization with Dask - a Monte-Carlo example... 4 more parts... 3 Fibonacci without recursiveness in Python - a better way 4 Python Singletons 5 Time needed to search in a list or set in Python 6 Make readable string formatting 7 Express your intentions in your coding - short lived variables 8 Express your. In the above example, dot product operation has been performed and it is clearly visible that for the same operation 'for' loop takes 0.6ms while vectorization takes merely 0.003ms. Example 2 : Exponent operation. n = 100000000. arr1 = np.random.rand (n) output = np.zeros ( (n,1)) ## Approach 1 Examples of vectorization. NumPy. Python is known as one of the most development-friendly languages yet struggles with runtime for simple arithmetic. The NumPy tool gives Python developers the vectorization abilities of C or Fortran. NumPy makes vectorization easy and runtime smooth for developers by enabling parallel operations, list creation for NumPy arrays, and memory locality. High. NumPy Optimization: Vectorization and Broadcasting | Paperspace Blog. In Part 1 of our series on writing efficient code with NumPy we cover why loops are slow in Python, and how to replace them with vectorized code. We also dig deep into how broadcasting works, along with a few practical examples. Paperspace Blog Ayoosh Kathuria. Using NumPy to Speed Up K-Means Clustering by 70x | Paperspace. Text vectorization is an important step in preprocessing and preparing textual data for advanced analyses of text mining and natural language processing (NLP). With text vectorization, raw text can be transformed into a numerical representation. In this three-part series, we will demonstrate different text vectorization techniques using Python. The first part focuses on the term-document.

The numpy.vectorize () function maps functions on data structures that contain a sequence of objects like arrays in Python. It successively applies the input function on each element of the sequence or array. The return-type of the numpy.vectorize () function is determined by the input function. See the following code example Vectorized Operations in NumPy - GeeksforGeeks › See more all of the best images on www.geeksforgeeks.org Images. Posted: (1 day ago) Oct 06, 2021 · Jul 27, 2020 · The Output and Operations will speed-up when compared to simple non-vectorized operations. Example 1 : Using vectorized sum method on NumPy array. We will compare the vectorized sum method along with simple non-vectorized.

### Vectorization in Python - Tutorialspoin

Python Basics with Numpy (optional assignment) To make sure that your code is computationally efficient, you will use vectorization. For example, try to tell the difference between the following implementations of the dot/outer/elementwise product. In : import time x1 = [9, 2, 5, 0, 0, 7, 5, 0, 0, 0, 9, 2, 5, 0, 0] x2 = [9, 2, 2, 9, 0, 9, 2, 5, 0, 0, 9, 2, 5, 0, 0] ### CLASSIC DOT. Numpy arrays provide vectorization abilities. Vectorization in numpy is when operations are applied to entire arrays instead of individual items within a for loop 2.Instead of writing a for loop for numpy arrays in Python code, the underlying numpy API uses a for loop in its C implementation, which is much faster than native Python. As a simple vectorization example, let's take a numpy array. So Vectorization is the process of converting iterative value into vector-based operations. It is a really quick process because of the latest CPUs. As a result, the conversion rate is pretty high. And it takes very little time in completing the process. Examples. Now let we will try to add two lists of arrays, so one way of doing that is by iterating over both lists and then add them up. let.

Vectorization Programming style - Python using NumPy Extension... 4 NumPy Basics NumPy's main object is the homogeneous multidimensional array - Table of elements (usually numbers) In NumPy nomenclature: - Dimensions are called axes - Number of axes is called rank import numpy as np oneDimArray = np.array([1,2,3,4]) twoDimArray = np.array([[1,2,3,4],[5,6,7,8]]) 5 NumPy Basics It. Je voulais utiliser la numpy vectorization car je dois maintenant appliquer une autre fonction à cette liste. J'essaie comme ça, mais la performance est très lente. Des résultats similaires avec appl Text Vectorization Pipeline. This example illustrates how Dask-ML can be used to classify large textual datasets in parallel. It is adapted from this scikit-learn example. The primary differences are that. We fit the entire model, including text vectorization, as a pipeline. We use dask collections like Dask Bag, Dask Dataframe, and Dask Array.

### Vectorization in Python - GeeksforGeek

Vectorization - more examples. 1 question. Why use NumPy? 05:49. Speed of NumPy Arrays vs Python lists. 09:59. Performance Test: Try it out yourself. 00:14 . Your First NumPy Project 17 lectures • 1hr 7min. Introduction to your first NumPy project. 06:54. Load the data into the correct data type. 07:34. Full dataset used in the project. 00:25. Load the data and correct the data type. 1. Applying unvectorized functions with apply_ufunc ¶. This example will illustrate how to conveniently apply an unvectorized function func to xarray objects using apply_ufunc. func expects 1D numpy arrays and returns a 1D numpy array. Our goal is to coveniently apply this function along a dimension of xarray objects that may or may not wrap dask arrays with a signature ### python - how to use numpy

For vectorization of calculations involving booleans and if conditions, the solution can be problem dependent, but one common easy way of addressing simple boolean problems could be where method in numpy package. For example, suppose you have a list of numbers and you would like to perform a task on all negative numbers in the array, say set them all to zero, and leave the positive numbers. An open-source book about numpy vectorization techniques, based on experience, practice and descriptive examples. www.labri.fr . Numpy tutorial. The Game of Life, also known simply as Life, is a cellular automaton devised by the British mathematician John Horton www.labri.fr. Python Numpy Tutorial - Learn Numpy Arrays With Examples. This blog on Python NumPy will help you learn all the.

### vectorization - Numpy vectorize excluded argument - Stack

NumPy arrays. An array can be used to contain values of a data object in an experiment or simulation step, pixels of an image, or a signal recorded by a measurement device. For example, the latitude of the Eiffel Tower, Paris is 48.858598 and the longitude is 2.294495. It can be presented in a NumPy array object as p Vectorization. Sometimes batching is not implemented for arguments of a function (that is, some of the arguments are missing the batch shape). In this case, tf.vectorized_map might be extremely useful as it can parallelize calculations along the 0-th dimension of the input Tensors. Below we demonstrate how to use vectorized map for the option. Vectorization. NumPy takes advantage of vectorization processing multiple calculations at a single time using Single Instruction Multiple Data (SIMD). While the loop may have taken 800 cycles, the vectorized computation may only take 200 cycles or even less. NumPy also makes our life as a developer easier as it simplifies many of the everyday tasks we need to perform on a dataset. Take the. Python Debugging & Numpy Basics. CS 5670. Qianqian Wang, Kai Zhangand the CS5670 Staff. PyCharm Debugging Techniques. See . here. for basic tutorials. Virtualenv Environment Configurations. In . Settings/Preferences . dialog (⌘,), select . Project: <project name> | Project Interpreter. In the Project Interpreter page, click and select . Add. In the left-hand pane of the Add Python.

### Look Ma, No For-Loops: Array Programming With NumPy - Real

To iterate over a NumPy Array, you can use numpy.nditer iterator object. numpy.nditer provides Python's standard Iterator interface to visit each of the element in the numpy array. Any dimensional array can be iterated. Example. In the following example, we have a 2D array, and we use numpy.nditer to print all the elements of the array. import numpy as np #2D array a = (np.arange(8)*2. The following example illustrates the vectorization difference between standard Python and numpy. For two arrays A and B of the same size, if we wanted to do a vector multiplication in Python: c = [] for i in range (len (a)): c.append(a[i]*b[i]) In numpy, this can simply be done with the following line of code: c = a* NumPy, also known as Python's vectorization solution, is the fundamental package for performing scientific computations with Python. It gives you the ability to create multidimensional array objects and perform faster mathematical operations than you can with base Python. It is the basis of most of Python's Data Science ecosystem. Most of the other libraries that you use in data analytics. An example, using append is very costly (dynamic memory allocation = a new matrix is created for each append call, to add a new row) and you can easily avoid it either by creating a matrix, or by adding a column a matrix; numpy is implicitly vertorized and it's fast if it's used correctly. Share a bit more and the community will help yo

### NumPy Functional programming: vectorize() function

Using vectorization you can do this in just one step, instead of 10. So the code is now 10 times faster, almost. You do need to take in account the time it would take to merge the results, and the extra time due to the overhead. But that is negligible compared to how much time you are actually saving. Lets look at an example ����� Best Book for Numpy and Pandas. 1. Python for Data Analysis: Data Wrangling with Pandas, NumPy, and Ipython. This book has been written by Wes McKinney, the creator of the Python pandas project. You will learn all the things required for making good datasets. You will know the practical approach to manipulate, process and learning the datasets Implementing the k-means algorithm with numpy. Fri, 17 Jul 2015. Mathematics Machine Learning. In this post, we'll produce an animation of the k-means algorithm. The k-means algorithm is a very useful clustering tool. It allows you to cluster your data into a given number of categories. The algorithm, as described in Andrew Ng's Machine. what is vectorization in python; what is vectorization in python. June 12, 2021. ### Python Examples of numba

Multiplication of two matrices using NumPy is also known as vectorization. Using this module to reduce explicit use of for loops in the program makes program execution faster. NumPy is a built-in package of Python which is used for array processing and manipulation. We need to import NumPy in the program and use dot operator for matrix multiplication to use this package. Let's have a look at. This last example illustrates two of NumPy's features which are the basis of much of its power: vectorization and broadcasting. # Why is NumPy Fast? Vectorization describes the absence of any explicit looping, indexing, etc., in the code - these things are taking place, of course, just behind the scenes in optimized, pre-compiled C code. Vectorized code has many advantages, among which.  