Clustering is one among the most useful unsupervised ML methods. It is used to find the relationship patterns and similarity among the input data samples. After finding these patterns, unsupervised algorithm clusters the data samples having similarities into groups as illustrated in the diagram below −Anomaly detection, image segmentation, medical imaging, social network analysis, and market segmentation are some common applications for clustering. K-means and Hierarchical are the two most common forms of clustering.To implement clustering, SciPy provides us a clustering package (scipy.cluster) which further has two modules as given below −scipy.cluster.vq module − This SciPy module provides functions for k-means ... Read More

When SciPy is imported, you do not need to explicitly import the NumPy functions because by default all the NumPy functions are available through SciPy namespace. But as SciPy is built upon the NumPy arrays, we must need to know the basics of NumPy.As most parts of linear algebra deals with vectors and matrices only, let us understand the basic functionalities of NumPy vectors and matrices.Creating NumPy vectors by converting Python array-like objectsLet us understand this with the help of following example−Exampleimport numpy as np list_objects = [10, 20, 30, 40, 50, 60, 70, 80, 90] array_new = np.array(list_objects) print ... Read More

NumPy, stands for Numerical Python, is used for the manipulation of elements of numerical array data. SciPy, stands for Scientific Python, is used for numerical computations in Python. Both these packages provide extended functionality to work with Python. Let’s understand some basic differences between NumPy and SciPy −Functional differences − NumPy has a faster processing speed than SciPy. The functions defined in NumPy library are not in depth whereas SciPy library consists of detailed versions of the functions. SciPy is built on NumPy and it is recommended to use both libraries altogether for fast and efficient scientific and mathematical computations.Array concept − ... Read More

SciPy is built upon the following core packages −Python − Python, a general-purpose programming language, is dynamically typed and interpreted. It is well suited for interactive work and quick prototyping. It is also powerful to write AI and ML applications.NumPy − NumPy is a base N-dimensional array package for SciPy that allows us to efficiently work with data in numerical arrays. It is the fundamental package for numerical computation.Matplotlib − Matplotlib is used to create comprehensive 2-dimensional charts and plots from data. It also provides us basic 3-dimensional plotting.The SciPy library − It is one of the core packages providing us many user-friendly and ... Read More

We can install Python SciPy with the help of following methods −Scientific Python Distributions − There are various scientific Python distributions that provide the language itself along with the most used packages. The advantage of using these distributions is that they require little configuration and work on almost all the setups. Here we will be discussing three most useful distributions −Anaconda − Anaconda, a free Python distribution, works well on MS Windows, Mac OS, and Linux. It provides us over 1500 Python and R packages along with a large collection of libraries. This Python distribution is best suited for beginners.WinPython − It ... Read More

To cover different scientific computing domains, SciPy library is organized into various sub-packages. These sub-packages are explained below −Clustering package (scipy.cluster) − This package contains clustering algorithms which are useful in information theory, target detection, compression, communications, and some other areas also. It has two modules namely scipy.cluster.vq and scipy.cluster.hierarchy. As the name entails, the first module i.e., vq module supports only vector quantization and k-meansalgorithms. Whereas the second module i.e., hierarchy module provides functions for agglomerative and hierarchical clustering.Constants(scipy.constants) − It contains mathematical and physical constants. Mathematical constants include pi, golden and golden_ratio. Physical constants include c, speed_of_light, planck, gravitational_constant, etc.Legacy ... Read More

SciPy, pronounced as “Sigh Pie”, is an ecosystem of Python open-source libraries for performing Mathematical, Scientific, and Engineering computations. SciPy stands for Scientific Python and is comprised of the following core packages, called SciPy ecosystem −NumPy − NumPy is a base N-dimensional array package for SciPy that allows us to efficiently work with data in arrays.Matplotlib − Matplotlib is used to create comprehensive 2-D charts and plots from data.Pandas − Pandas is an open-source Python package used to organize and analyze our data.Apart from SciPy ecosystem, there are other related but distinct entities SciPy refers to −Community − It refers to the community of ... Read More

When you create a Dockerfile, you can use two different commands to build your context. Building a context means including the files and directories that you want from your local machine, to be in your container when it’s created. These files may be directories in your local machine, a URL from which you want to download files, or a compressed tarball file that you want to include as it is or after extracting the tarball file.We can use two different instructions to add the files from the build context in the local machine to the Docker container. These are the ... Read More

We can build Docker images by specifying instructions inside a Dockerfile. Dockerfile allows us to specify step-by-step instructions which define the rules for making a container environment. The Docker containers are created for specific tasks and processes to be run inside them. There are three important instructions that are used inside a Dockerfile which lets us define which processes need to be run inside the containers and in what sequence and order.These 3 instructions are -RUNCMDENTRYPOINTThe RUN instruction is quite simple. We can define simple subcommands along with the RUN instruction that we want to execute inside the container. For ... Read More

Vagrant is a software that allows you to create a virtual machine that replicates the user's experience exactly as they want to set it up. Specifically, Vagrant allows you to test your application in a specific environment by mirroring the OS and all appropriate configurations.Whereas Docker is a framework that lets you containerize your app and build so-called microenvironments for deploying it without having to run a whole VM. Each container is a separate isolated environment that contains a unique application environment.As a result, programmers, testers, and DevOps engineers are able to spend less time debugging and identifying important bugs ... Read More