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Mobile Price Classification: An Open Source Data Science Project with Dagshub

The platform is all about app containers and containerization. If you’re new to , it is a way of packing a software application and all its dependencies into a single image. Containers allow users to separate an application from its infrastructure, so the app works in a predefined way regardless of the environment. Docker containerization While other types of containerizing software exist, Docker’s products and services are expected to change drastically by the end of 2022. How, you might ask? Not so long ago, was a free open source platform preferred by developers. But starting , the new Docker Desktop subscription policy requires licensing for the . So what’s the confusion? Docker from 31 January 2022 commercial user Should we panic about this licensing update and start choosing an alternative to Docker Desktop? Let’s find out. and the Ecosystem Docker Container According to , a container is a separate unit of software that stores an application’s code, its libraries, and dependencies. IBM There is a widespread misconception that Docker is all about containers only. Well, that’s not true and Docker is only part of the container ecosystem that consists of large, different projects. What unites the two is the requirement to meet the Open Container Initiative (OCI) and Kubernetes Container Runtime Interface (CRI) standards. What are Containers Used For? Containers are mostly used in the following scenarios: In the multi-cloud environments: when using from different vendors multiple cloud services when an application works with different public clouds In the cloud migration process for legacy software modernization. In the . When applications are split into smaller and independent services, containers are essential in deploying and maintaining the provider’s services. microservice architecture When used in the ways mentioned above, containers solve the following problems: Setting up an environment for new software development. Building and compiling software applications. Running and testing an application in different environments. Deploying the application in a cloud platform such as Kubernetes or AWS. Running third-party software. What is ? Docker is a simplified platform for development, shipping, and running containerized applications. The platform provides an open-source engine that automates the deployment of any application in a lightweight, self-contained space for on-premises and cloud use. Docker Docker architecture overview Docker was the first company to ever package the advanced Linux kernel features. This made development and deployment faster. Instead of building an entire server from scratch to run a service application, you could run a Docker container image on the host and start the application, using a few host resources in multiple environments. An is a read-only, organized collection of filesystem changes that form the basis of a container. A is a runnable instance of an image. Each container has its own filesystem, memory, and network interface. Also, containers are much lighter than . image container virtual machine (VM) images Docker Tools Containerization is a relatively new concept in software development, and we’ll show you how it works in the case of Docker. Docker Engine At the heart of the Docker ecosystem is the which is a . A container runtime is a software tool for loading container images, monitoring, isolating system resources, and managing the container lifecycle. It allows you to create and run containers on any development machine and then store or upload container images through a container registry such as or . Docker Engine container runtime Docker Hub Azure Container Registry Docker Hub is a service provided by Docker for finding and sharing container images. Docker Hub As applications grow and start to include containers deployed on different servers, they get harder to work with. Everything from coordinating to planning the use of vast numbers of containers, organizing how these containers interact with each other, as well as scaling multiple container instances become harder than they should be. Kubernetes in Docker Docker provides the tool for defining and running multi-container applications. enables the deployment and configuration of multiple containers simultaneously. A better solution for large systems would be using Docker for containerization and image creation. For container management, use more specialized software tools called . Docker Compose Docker Compose orchestrators , also known as . It was developed by Google to manage, automate, and scale containerized applications. Essentially, in projects. Docker simplifies the process of creating containers, while Kubernetes takes over the management of the containers. That’s considered the main Kubernetes vs Docker difference. Kubernetes K8s Docker and Kubernetes are often used together and Possible Docker Issues Solutions Docker products are on a whole new level. Portability, isolation, scalability, consistency, repeatability, and automation are just a few features you can expect from Docker tools. Their widespread use and the existence of a powerful community speak for the company. However, there is still a downside to using Docker tools. Below are some of the most common reasons why developers  would look for Docker container alternatives: Security Issues Containers have a weak point in the when it comes to routing, security, or detection. There is limited security in the Docker architecture itself. A user with access to the Docker daemon has root advantage over the host system. Additionally, there is no division of containers between different users on the same system. Everyone can see all the containers running on the host system. connection between services Use next-generation alternatives, which are initially aimed at better safety and ease of use. A good example is , a great Docker open-source container alternative with no daemon at all. Solution: Podman Orchestration issues Docker can’t manage the container launch order. Though it has an orchestration tool called , its functionality is limited compared to powerful orchestrators like Kubernetes. Besides, Docker Swarm works only with Docker containers. Docker Swarm Use Docker alternatives like Kubernetes for container orchestration. Kubernetes supports multiple container types: Docker, containers, CRI-O, and any solution that corresponds to the CRI standard. Solution: Isolation issues Docker does not provide 100% isolation of resources between containers. And, there could be a mess in the images repository as all the users have the power to change something. Use next-generation alternatives aiming at better safety and ease of use. Unlike Docker, stores images and containers in different directories. The Podman repository is in the user’s home directory. Each user has his own, separate set of containers so that several users can work on the host simultaneously. Solution: Podman Reliability Issues The Docker daemon service is responsible for all the work with registries, images, containers, and the kernel. But, a single service means an increased risk of failure. When a daemon process fails, all the running containers are left on their own. Use containerization tools that have no daemon – for example, . Solution: Podman Firewall Issues Docker interferes with the system firewall by adding its own firewall rules to the system. There is no reliable way to manage network access to the container through the firewall. Additionally, there is an issue when combining Docker with other services that try to manage the firewall, for example, with a VPN client-server. Deny the Docker daemon service the permission to make changes in the system’s firewall. Instead,  create all the necessary rules manually. Solution: Docker Hub Issues registry contains both official and non-official images. Most of the non-official images are poorly built and have vulnerabilities. The authors of such images usually don’t provide any quality guarantees or support either. Docker Hub Whenever possible, build any image used in the project yourself and put it in your own registry for Docker images. Never run randomly selected images on your system. Solution: Installation Issues There are multiple ways to install Docker. But some features may stop working or may work unexpectedly when doing the custom installations. Always install Docker tools according to the instructions from the . Solution: official Docker site With all these unresolved issues, it’s no wonder companies and development teams are always in search of other alternatives to Docker containers. Besides, these problems were exacerbated by the latest announcement from the Docker team – Docker Desktop – when it announced that part of the Docker ecosystem, is no longer free for everyone. and What Has Docker Desktop Changed has recently caught the attention of the container community after the new changes in its subscription policy. Docker Desktop (DD) is an all-in-one application for Mac or Windows environments that enables building and sharing containerized applications and microservices. Docker Desktop includes Docker Engine, Docker CLI (Command Line Interface) client, Docker Compose, Kubernetes, just to mention a few. Docker Desktop on Mac and Windows is a way to run Linux (create a Linux VM) so that can work. Docker Desktop Docker Engine What has changed in the Docker licensing policy? The Docker company recently announced that it would be making changes in its Docker Desktop subscription terms. Docker Desktop is the only Docker solution without a complete alternative yet. The new were announced on August 31, 2021, with a grace period up to January 31, 2022. During this period, Docker Desktop users need to decide on a subscription plan if they want to continue using the  Docker Desktop license. Otherwise, they will have to switch to other Docker Desktop alternatives. changes The officials expressed that these changes are vital to the improvement of their product and scaling their business. With Docker Desktop now , the company is one big step closer to its goals. reaching have 3.3 million installations The New Subscription Policy Back to our topic, only the subscription plan is free – it is available for . If a company has more than 250 employees or its revenue is greater than $10 million, it will have to use one of the paid subscriptions: , , or . Simply put, large business users must go for a Docker Desktop enterprise license to get additional features – secure software supply chain management, centralized policy visibility, and controls. Smaller businesses will enjoy Docker Desktop for free. Personal small businesses, individual use, and non-commercial open-source projects Pro Team Business If you don’t need , then nothing has changed for your business. Note: Docker Desktop If you don’t need Docker Desktop, then nothing has changed for your business – you can still choose any tool (Docker, Podman, CRI-O, etc.) for container creation and orchestration. Most of the extra features provided by the involve using the images registry. For example, the ability to scan images from the for vulnerabilities. But normally, companies use registries provided by Cloud vendors. Docker Desktop paid plans Docker Hub Docker Hub When do you really need Docker Desktop? is for people who crave a simplified installation process but don’t want to use the command line. Docker Desktop facilitates easy Docker setup and integration with (Windows Subsystem for Linux). Docker Desktop WSL Speaking of Linux, may still not be fully available for Linux users. The thing is DD is supported on Mac and Windows only. But to use Docker in non-Linux operating systems, developers need a Virtual Machine and that’s what does. Everything else (Docker CLI, Docker Engine, and other Docker tools) remains free and open source under the . Docker Desktop Docker Desktop Apache v.2 license Alternatives Docker Desktop When using you’ll probably find the mentioned above. Some of these issues were partially solved in the paid subscriptions. For example, the provides centralized management, enhanced security, and SAML SSO in the near future. But others (together with the new tariffication) of the DD, may force you to search for alternative solutions. Docker Desktop Docker issues Business plan per-user-per-month Docker Desktop and Kubernetes Most of the DD alternatives are focused on building and pushing images and having environments. In similar cases, you would only use Docker to build and push container images in local environments. This can be done with Docker Desktop, through Kube API, and using Minikube as a Linux VM. local Kubernetes In other cases that don’t require Kubernetes (for example, running containers directly, using ), there is no other alternative to Docker Compose Docker Desktop. You can replace Docker Desktop with one of the following alternatives: 1. Run a VM and manage it yourself. and the are still free. Install a Linux VM on the Windows host (for example, via Hyper-V or VirtualBox) and then install the Docker CLI inside WSL and connect over SSH. For development purposes, (building and running images), use . Docker CLI Docker Engine Podman 2. Use to install and manage a VM and also get a Kubernetes cluster up and running from Windows. But note that Minikube doesn’t have a GUI (Graphical User Interface) and doesn’t work with M1 chips, so may not work on new MacBooks. Minikube 3. Use RancherDesktop, a free open-source application for Mac, Windows, and Linux. It simplifies building, pushing, pulling, running container images, and also includes Kubernetes. It’s simple to use and has a built-in GUI. But since this tool is relatively new, you risk missing out on CLI, and multiple features. It may also have bugs. Final Thoughts In the container ecosystem, there are no “Docker containers”, there are just “containers”, and there are no “Docker images”, just  “container images.” You are free to use any alternative containers to Docker, as long as it follows the industry standards around container formats and runtimes. Please remember that Docker is a tool. Sometimes, the best option is to use lightweight virtual machines. In other cases, using more specialized tools may help. It all boils down to the purpose, project goals, and the application features. The only Docker product that has no fully equipped alternative is Docker Desktop for Mac and Windows. But, it’s only a matter of time, before other companies launch products that can substitute DD. For now, try analyzing if provides any additional value for your business or not. If you don’t use any Docker Desktop features beyond what Docker itself is offering, it’s worth considering other options. But if there is significant additional value, it’s worth paying for the out-of-the-box functionality. Docker Desktop

Top Alternatives to Docker Desktop in 2022: How to Choose One 

The Issue Of Machine Ethics and Robot Rights

Machine Learning in Static Code Analysis

This is true for the most part, but Serverless apps also have a certain property that can make their deployment and maintenance time consuming. That is, they depend on services - lots of them. A serverless architecture is typically designed over services for storing data, load balancing, data caching and code execution, just to name a few. With that said, how do you both provision infrastructure and deploy code to test/staging/production environments in an and way? automated risk-minimized If you can’t answer this question easily, or some part of your deploys still require you to look at your cloud provider’s console, then this post is for you. Separate Environments Properly To lessen the risk of integrating new code, most teams setup various environments to test the robustness of submitted code before it is pushed to production. When configuring serverless architectures in these environments, special care needs to be taken in deciding whether a or architecture is best for your project. Otherwise, you might be wasting a lot of time on deployments, or even worse, introducing unnecessary risk to your application. single stack multi-stack approach The approach shares its services and infrastructure environments (like test, staging and prod). To differentiate between environments at runtime, environment variables or similar mechanisms are used. single stack across all In the context of a serverless app deployed on AWS, a single stack setup might configure a single Lambda Function with multiple aliases, an API Gateway with multiple stages, and a DynamoDB service with multiple tables - all with the purpose of supporting multiple test, staging and production environments. In contrast, a approach uses a instance of each service for every environment and does not use API stages or Lambda aliases to differentiate between them. multi-stack separate Using the AWS example again, a multi-stack setup would configure a separate Lambda Function, API Gateway, and DynamoDB instance for each environment. If something goes wrong in a single stack approach, there is a greater chance that your production systems are negatively impacted as well. After all, the environments in this approach are provisioned on top of each other, and rely only on environment variables and lambda aliases for indirection. In the multi-stack approach, this probability is decreased as a result of using separate resources for each environment. This advantage comes at a cost however - multi-stack approaches require more work to configure and maintain in comparison to single stack architectures. And in most scenarios, they are more expensive to run as well. If you are in the early stages of your project, it might be a better idea for you to run with a single stack approach. Otherwise, a multi-stack approach is the way to go. Continuous Delivery The main idea behind Continuous delivery is to produce artifacts from your code base frequently in an . production ready automated fashion It ensures that code can be rapidly and safely deployed to production by delivering every change to a production-like environment and that any business applications and services function as expected through rigorous automated testing. The most important point however, is that all of this must be . automated Using their front end web application, Cloud providers such as Amazon Web Services to spin off a new lambda function for testing purposes or to update a function’s application code. make it easy However, all aspects of your continuous delivery strategy should be - the only manual step should be the push of the deploy to production button. This is vital for many reasons: automated - Even with proper resource access controls in place which is seldom the case, the risk of accidentally causing bad things to happen are higher than you think. You definitely don’t want be the reason for a disruption to your service. Minimizing Risk - As the number of developers in the project grows, and by extension, the volume of code deliveries, any process that is not completely automated will likely slow down your team’s agility. Scalability - When your continuous delivery strategy is an automated, often using an automated pipeline of some sort, the use of deployments, builds and other components are better documented, making it easier to resolve and recover from failures. Traceability Using Bitbucket pipelines, I usually use the following skeleton pipeline in multi-environment, continuous delivery environments. First, it runs your static analysis tools and automated tests for every Pull Request. Once the changes are deployed to master, it automatically updates the environment. Finally, the environment is updated once the changes in are known to be safe using a manual trigger. If you are using AWS, checkout for more information on the actual automated deployment of Lambda functioApp Config Is Stored in the Environment STAGING PROD STAGING this repo pipelines: default: - step: name: Run Static Analysis Tools script: ... - step: name: Run Automated Tests script: ... branches: master: - step: name: Run Static Analysis Tools script: ... - step: name: Run Automated Tests script: ... - step: name: Update STAGING deployment: staging script: (deploy to staging)... - step: name: Update PROD deployment: production trigger: manual script: (deploy to production)... App Config Is Stored in the Environment An app’s config consists of everything that is likely to vary between deploys (staging, production, developer environments, etc). This typically includes: Resource handles to the database, cache or any other attached resources Credentials to external services such as Amazon S3 or Twitter Per-deploy values such as the canonical hostname for the deploy Apps sometimes store config variables as constants in source code which is problematic for several reasons. First, any security sensitive related config data is visible to everyone who has access to the source code, which poses a serious security concern. Additionally, config varies substantially across deploys, whereas code does not. Developing code that dynamically detects the environment and sets the appropriate config data as required results in code that is unnecessarily complicated and difficult to maintain, especially as the number of environments/deploys increases. On top of this, any required changes to an environment config requires pushing a change through the code base and waiting for it to propagate through the software delivery pipeline which is tedious and slow. Rather than keeping your config data in source code, consider using consider using a service dedicated for this purpose like . Or as a slightly less safe but commonly used alternative, store this data in environment variables. Now your application can simply refer to abstract variables for config info, which are dynamically set by a build pipeline during deployment. AWS Secrets Manager In this way, environment variables can be leveraged to generate flexible and infinite variations (test, staging, prod, etc..) of your environment template. Lastly, environment variables are OS and language agnostic, which means they can easily be integrated across all types of projects. Infrastructure is Provisioned and Deployed as Code Infrastructure as Code (IaC) is all about provisioning and updating entire workloads (applications, infrastructure) using code. The idea here is that the same engineering discipline that is typically used for application code can be applied to deploying and managing workloads as well. You can implement your operations procedures as code and automate their execution by triggering them in response to events. By using code to automate the process of setting up and configuring a virtual machine or container for example, you have a and method for replicating the process. So if you build a virtual environment for the development of an application, you can repeat the process of creating that VM simply by running the same code once you are ready to deploy. fast repeatable Additionally, IaC processes increase agility. Developers don’t have to wait for however long the IT department needs to provision a new VM for them to do work. When it comes to practicing IaC in the cloud, the is a great tool for configuring serverless architectures. It’s a command line interface for building and deploying entire serverless applications through the use of configuration template files. It, along with many other services including AWS’s Serverless Application Model (SAM) for example, provide a scalable and systematic solution to many of the operational complexities of multi-environment serverless architectures. Serverless Framework The is probably the most popular cloud provider agnostic framework for configuring serverless applications and supports AWS, Microsoft Azure, Google Cloud Platform, IBM OpenWhisk, and more. Serverless Framework The typical serverless application uses a combination of cloud services, which often need to be configured and setup separately for each environment. makes this process much more straight forward and scalable by utilizing a component based configuration where all the infrastructure and code for your entire application can be provisioned easily. Not only do these components conveniently refer to commonly used serverless services (AWS: S3 buckets, lambda functions, API Gateways, etc..), they can be re-used and composed into higher level components to build entire serverless applications. Components are open source, and there is lots of pre-built functionality developers can tap into right away. Serverless As mentioned in the previous section, an automated continuous delivery solution is key for your serverless application. Serverless frameworks can be easily integrated into CI/CD pipeline to deploy sophisticated serverless applications in one step, making it much easier to manage multi-environment configurations. Closing Serverless applications often consist of plucking and combining various services and packages together which can make keeping track of everything a complex process, especially when running multi-environment setups. In such scenarios, I feel it’s important that processes surrounding Serverless architectures should be scalable, automated, and require little manual intervention. This list represents my thoughts on how to deal with the complexity. If you think this list could benefit from other ideas/concepts I’ve missed, let me know in the comments below!

Application Deployment: Recipe for Great Serverless Strategy

NumPy is the fundamental package for scientific computing with Python. In of the Data science With Python series, we looked at the basic in-built functions for numerical computing in Python. In this part, we will be taking a look at the Numpy library. Part 1 NumPy is the fundamental package for scientific computing with Python. It contains among other things: a powerful N-dimensional array object sophisticated (broadcasting) functions tools for integrating C/C++ and Fortran code useful linear algebra, Fourier transform, and random number capabilities Besides its obvious scientific uses, NumPy can also be used as an efficient multi-dimensional container of generic data. Arbitrary data-types can be defined. This allows NumPy to seamlessly and speedily integrate with a wide variety of databases. Great, let’s see how to use the Numpy library for basic array manipulation. The Numpy library First, we need to import numpy in Python. import numpy as np Let’s create a numpy array. np.array([4,5,6]) Output : array([4,5,6]) Now, let’s create a multi-dimensional array. mul=np.array([[5,4,6],[7,8,9],[10,11,12]])mul Output : array([[4, 5, 6], [7, 8, 9],[10,11,12]]) Check the shape (rows and columns of the array). mul.shape Output : (3, 3) Create an evenly spaced array between 1 and 60 with a difference of 2. dif=np.arange(1,60,2)dif Output : array([ 1, 3, 5, 7, 9, 11, 13, 15, 17, 19, 21, 23, 25, 27, 29, 31, 33, 35, 37, 39, 41, 43, 45, 47, 49, 51, 53, 55, 57, 59]) Reshape the above array into a desired shape. dif.reshape(10,3) Output : array([[ 1, 3, 5], [ 7, 9, 11], [13, 15, 17], [19, 21, 23], [25, 27, 29], [31, 33, 35], [37, 39, 41], [43, 45, 47], [49, 51, 53], [55, 57, 59]]) Generate an evenly spaced list between the interval 1 and 8. (Take a minute here to understand the difference between ‘linspace’ and ‘arange’) gen = np.linspace(1,8,40)gen Output: array([1. , 1.17948718, 1.35897436, 1.53846154, 1.71794872, 1.8974359 , 2.07692308, 2.25641026, 2.43589744, 2.61538462, 2.79487179, 2.97435897, 3.15384615, 3.33333333, 3.51282051, 3.69230769, 3.87179487, 4.05128205, 4.23076923, 4.41025641, 4.58974359, 4.76923077, 4.94871795, 5.12820513, 5.30769231, 5.48717949, 5.66666667, 5.84615385, 6.02564103, 6.20512821, 6.38461538, 6.56410256, 6.74358974, 6.92307692, 7.1025641 , 7.28205128, 7.46153846, 7.64102564, 7.82051282, 8. ]) Now, change the shape of the array in place (‘resize’ function changes the shape of the array in place, unlike ‘reshape’) gen.resize(10,4)gen Output: array([[1. , 1.17948718, 1.35897436, 1.53846154], [1.71794872, 1.8974359 , 2.07692308, 2.25641026], [2.43589744, 2.61538462, 2.79487179, 2.97435897], [3.15384615, 3.33333333, 3.51282051, 3.69230769], [3.87179487, 4.05128205, 4.23076923, 4.41025641], [4.58974359, 4.76923077, 4.94871795, 5.12820513], [5.30769231, 5.48717949, 5.66666667, 5.84615385], [6.02564103, 6.20512821, 6.38461538, 6.56410256], [6.74358974, 6.92307692, 7.1025641 , 7.28205128], [7.46153846, 7.64102564, 7.82051282, 8. ]]) Create an array with all elements as ones. onarr = np.ones((4,4))onarr Output: array([[1., 1., 1., 1.], [1., 1., 1., 1.], [1., 1., 1., 1.], [1., 1., 1., 1.]]) Create an array filled with zeros. zearr = np.zeros((4,4))zearr Output: array([[0., 0., 0., 0.], [0., 0., 0., 0.], [0., 0., 0., 0.], [0., 0., 0., 0.]]) Create a diagonal matrix with diagonal values = 1 dm = np.eye(3)dm Output: array([[1., 0., 0.], [0., 1., 0.], [0., 0., 1.]]) Extract only diagonal values from an array. np.diag(dm) Output: array([1., 1., 1.]) Create an array consisting of repeating list relist = np.array([1,2,3]*7)relist Output: array([1, 2, 3, 1, 2, 3, 1, 2, 3, 1, 2, 3, 1, 2, 3, 1, 2, 3, 1, 2, 3]) Now, repeat each element of array n number of times using repeat function. np.repeat([1,2,3],3) Output : array([1, 1, 1, 2, 2, 2, 3, 3, 3]) Generate two arrays of desired shape filled with random values between 0 and 1. relist = np.random.rand(2,3)print(relist)de = np.random.rand(2,3)print(de) Output : [[0.55523672 0.46815197 0.67590369] [0.5331193 0.62780236 0.45044916]] [[0.26215572 0.07380256 0.06592746] [0.89782279 0.95603968 0.82052478]] Stack the above two arrays created vertically st = np.vstack([de,relist])st Output : array([[0.26215572, 0.07380256, 0.06592746], [0.89782279, 0.95603968, 0.82052478], [0.55523672, 0.46815197, 0.67590369], [0.5331193 , 0.62780236, 0.45044916]]) Now, let’s stack them horizontally. sh = np.hstack([de,relist])sh Output : array([[0.26215572, 0.07380256, 0.06592746, 0.55523672, 0.46815197, 0.67590369], [0.89782279, 0.95603968, 0.82052478, 0.5331193 , 0.62780236, 0.45044916]]) Great, now let’s perform some array operations. First let’s create two random arrays r1 = np.random.rand(2,2)r2 = np.random.rand(2,2)print(r1)print(r2) Output : [[ 0.02430146 0.14448542] [ 0.54428337 0.40332494]] [[ 0.77574886 0.08747577] [ 0.51484157 0.92319888]] Let’s do element wise addition. r3 = r1+ r2r3 Output : array([[-0.75144739, 0.05700965], [ 0.02944179, -0.51987394]]) Element wise subtraction. r4 = r1 - r2r4 Output : array([[-0.75144739, 0.05700965], [ 0.02944179, -0.51987394]]) Let’s power each element to 3. r5 = r1**3r5 Output : array([[0.65228631, 0.24993365], [0.97976155, 0.71554632]]) Now, instead of element wise operation, let’s perform a dot product of the two arrays r1 and r2. r6 = r1.dot(r2)r6 Output : array([[ 0.09323893, 0.13551456], [ 0.62987564, 0.41996073]]) Let’s create a new array and transpose it. sh = np.array([[1,2],[3,4]])sh Output : array([[1, 2], [3, 4]]) sh.T Output : array([[1, 3], [2, 4]]) Now, check the datatype of elements in the array. sh.dtype Output : dtype(‘int32’) Change the datatype of the array. rs = a.astype('f')rs.dtype Output : dtype(‘float32’) Now, let’s look at some mathematical functions in an array, starting with sum of an array. c = np.array([1,2,3,4,5])c.sum() Output : 15 Maximum of the elements of an array. c.max() Output : 5 Mean of the elements of the array c.mean() Output : 3 Now, let’s retrieve the index of the maximum value of the array. c.argmax() Output : 4 c.argmin() Output : 0 Create an array consisting of square of first ten whole numbers. dim = np.arange(10)**2dim Output : array([ 0, 1, 4, 9, 16, 25, 36, 49, 64, 81], dtype=int32) Access values in the above array using index dim[2] Output : 4 dim[1:5] Output : array([ 1, 4, 9, 16], dtype=int32) Use negative sign to access variables in reverse. dim[-1:] Output : array([81], dtype=int32) Now, access certain elements of the array based on a step size. dim[1:10:2] #dim[start:stop:stepsize] Output : array([ 1, 9, 25, 49, 81], dtype=int32) Create a multidimensional array en = np.arange(36)en.resize(6,6)en Output : array([[ 0, 1, 2, 3, 4, 5], [ 6, 7, 8, 9, 10, 11], [12, 13, 14, 15, 16, 17], [18, 19, 20, 21, 22, 23], [24, 25, 26, 27, 28, 29], [30, 31, 32, 33, 34, 35]]) Access the second row and third column en[1,2] Output : 8 Access 2nd row and columns 3 to 7. Note that the numbering of the rows and columns start with 0. en[1, 2:6] Output : array([ 8, 9, 10, 11]) Select all rows till the 2nd row and all columns except last column en[:2,:-1] Output : array([[ 0, 1, 2, 3, 4], [ 6, 7, 8, 9, 10]]) Select values from array greater than 20. en[en>20] Output : array([21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35]) Assign value of the array elements as 20 if the element value is greater than 20. en[en>20] = 20en Output : array([[ 0, 1, 2, 3, 4, 5], [ 6, 7, 8, 9, 10, 11], [12, 13, 14, 15, 16, 17], [18, 19, 20, 20, 20, 20], [20, 20, 20, 20, 20, 20], [20, 20, 20, 20, 20, 20]]) To copy an array onto another variable, always use the copy function. fun = en.copy()fun Output : array([[ 0, 1, 2, 3, 4, 5], [ 6, 7, 8, 9, 10, 11], [12, 13, 14, 15, 16, 17], [18, 19, 20, 20, 20, 20], [20, 20, 20, 20, 20, 20], [20, 20, 20, 20, 20, 20]]) Create an array with a set of random integers between 1 and 10. Specify the array to be of shape 4*4 gom = np.random.randin(1,10,(4,4))gom Output : array([[9, 7, 1, 4], [1, 4, 3, 6], [2, 5, 5, 1], [2, 2, 9, 9]]) Great, we have looked at creating, accessing and manipulating arrays in Numpy. In the next part of the series, we will be looking at a library which is built on the Numpy library — Pandas. Pandas is a library which makes data manipulation and analysis much easier in Python. It offers data structures and operations for numerical tables and time series. Resources : Numpy documentation Applied Data Science with Python Specialization. Connect on and, check out Github (below) for the complete notebook. LinkedIn _Python-The-Dangerous-Tool-For-ML-Data-Science - Learn data science and Machine learning with Python._github.com harunshimanto/Python-The-Dangerous-Tool-For-ML-Data-Science You can what you think about this, if you enjoy writing, click on the clap 👏 button. tell me Thanks to everyone.

Numpy With Python For Data Science

February 3, 2018 Javascript https://blogs.msdn.microsoft.com/typescript/2018/01/31/announcing-typescript-2-7/ https://m.signalvnoise.com/stimulus-1-0-a-modest-javascript-framework-for-the-html-you-already-have-f04307009130 https://dev-blog.apollodata.com/improved-apollo-client-devtools-eb43258f103f https://medium.com/javascript-scene/graphql-at-the-rest-aurant-f4091054e82a https://medium.com/styled-components/v3-1-0-such-perf-wow-many-streams-c45c434dbd03 https://medium.com/javascript-scene/top-javascript-libraries-tech-to-learn-in-2018-c38028e028e6 https://medium.com/@dalaidunc/10-things-i-love-about-vue-505886ddaff2 https://blog.logrocket.com/the-increasing-nature-of-frontend-complexity-b73c784c09ae https://www.digitalauthority.me/insights/step-step-guide-driving-android-app-downloads-29-01-2018 https://www.cypress.io/ https://medium.com/seek-blog/sketching-in-the-browser-33a7b7aa0526 https://codeburst.io/writing-javascript-with-map-reduce-980602ff2f2f https://medium.freecodecamp.org/how-i-reverse-engineered-a-chrome-extension-to-write-my-own-flask-app-d77f36421106 https://medium.freecodecamp.org/rock-solid-react-js-foundations-a-beginners-guide-c45c93f5a923 https://medium.freecodecamp.org/why-would-you-not-use-typescript-67d0baa3eaca https://hackernoon.com/progressive-web-apps-the-next-step-in-web-app-development-372235bf9a99 https://hackernoon.com/js-wtf-with-math-79da9a941ec1 https://medium.freecodecamp.org/conditional-rendering-in-react-using-ternaries-and-logical-and-7807f53b6935 DevOps https://medium.com/meus-pedidos/spot-instances-in-production-is-it-worth-fcb3e5951973 https://medium.com/google-cloud/kubernetes-101-pods-nodes-containers-and-clusters-c1509e409e16 Cloud & Serverless https://siliconangle.com/blog/2018/01/30/microsoft-launches-azure-event-grid-managing-serverless-computing-functions/ https://medium.com/@PaulDJohnston/container-people-lets-talk-about-serverless-e6ecb5c437cf http://www.rapyder.com/amazon-s3-top-10-things-careful/ https://techcrunch.com/2018/01/31/microsofts-azure-revenue-nearly-doubled-year-over-year-in-its-second-quarter/ https://hackernoon.com/serverless-observability-part-1-new-challenges-to-old-practices-95de1b94d379 https://hackernoon.com/informative-alerts-with-serverless-slack-unfurling-acb6457e3a59 AI https://medium.com/@andrewng/announcing-the-ai-fund-building-transformative-ai-companies-55c008663072 https://www.ibm.com/blogs/research/2018/02/deep-learning-models/ https://towardsdatascience.com/what-used-to-cost-millions-in-2017-can-now-be-solved-with-machine-learning-for-499-month-10b5b4bb1173 https://medium.com/@aldamiz/how-we-grew-from-0-to-4-million-women-on-our-fashion-app-with-a-vertical-machine-learning-approach-f8b7fc0a89d7 https://blog.gyana.co.uk/retail-evolution-gyana-to-define-the-future-of-brick-and-mortar-b877fbe6bbe8 https://machineleraning.blogspot.com/2018/02/top-artificial-intelligence-startups-of.html https://www.sporttechie.com/nvidia-deep-learning-video-analytics-automate-future-player-scouting/ https://www.linkedin.com/pulse/how-become-machine-learning-engineer-path-andrew-nikishaev/ https://towardsdatascience.com/dealing-with-imbalanced-classes-in-machine-learning-d43d6fa19d2 https://medium.com/@dpatil/a-code-of-ethics-for-data-science-cda27d1fac1 https://towardsdatascience.com/only-numpy-implementing-gan-general-adversarial-networks-and-adam-optimizer-using-numpy-with-2a7e4e032021 https://medium.com/modeldepot/transfer-learning-will-radically-change-machine-learning-for-engineers-78732b2bb415 Big Data https://engineering.salesforce.com/how-apache-kafka-inspired-our-platform-events-architecture-2f351fe4cf63 https://wearecitizensadvice.org.uk/markets-dont-work-like-they-used-to-and-people-are-starting-to-notice-af00ed38014d https://towardsdatascience.com/python-basics-for-data-science-6a6c987f2755 Blockchain https://blog.bigchaindb.com/2018-the-year-for-data-driven-blockchains-1b8ce0b5f9 https://medium.com/@iotasuppoter/iota-real-world-use-cases-are-coming-ab1d8240cf09 https://hackernoon.com/build-a-cryptocurrency-price-tracker-in-5-minutes-d66c3d37ad71 https://medium.com/friendz/how-to-register-to-friendz-ico-8d02fe1975bc https://media.consensys.net/state-of-decentralized-exchanges-2018-276dad340c79 https://medium.com/@ryandonz/cryptocurrency-the-secret-to-profiting-from-the-most-volatile-market-in-the-world-via-asymmetric-3fd50e56405 https://hackernoon.com/a-crash-course-in-blockchain-and-crypto-6b6794f41814 https://medium.com/cryptomuse/the-unmasking-of-bitcoin-creator-satoshi-nakamoto-the-fallout-86102a3c60eb Personal Growth https://medium.com/@alltopstartups/how-to-teach-yourself-to-read-an-entire-book-in-a-single-day-485e35e28126 https://medium.com/swlh/i-sucked-at-writing-so-i-spent-a-month-writing-for-4-hours-a-day-dba37aa85585 https://medium.com/the-mission/how-to-discipline-your-mind-for-greater-success-e44d15a2a0ee https://medium.com/the-mission/today-is-the-first-day-of-your-life-a0c9e0b91d05 https://medium.com/swlh/how-to-disrupt-the-daily-grind-slipstreaming-your-dream-be0fae6fc04d https://medium.com/@dariusforoux/15-limiting-beliefs-that-are-holding-you-back-from-reaching-your-full-potential-adc795a4049 https://medium.com/swlh/this-expert-approach-will-prepare-you-for-any-interview-d8b62e3be03d https://medium.com/the-mission/when-you-love-yourself-first-life-will-take-care-of-the-rest-7e8c7e9b3097 https://medium.com/@RyanHoliday/the-moments-no-one-wants-to-experience-but-everyone-needs-to-succeed-c284273b3d59 Startup https://medium.com/@jc_70515/growth-hack-your-way-to-the-top-of-product-hunt-2f09e925cc06 https://medium.com/@zebulgar/how-to-raise-money-before-launch-a3544ef4dba6 https://medium.com/swlh/steps-to-validate-your-startup-idea-before-you-spend-any-even-1-43723781afd3 Misc https://medium.com/@jamesflight/critical-thinking-in-software-development-the-word-should-and-why-you-shouldn-t-listen-to-563090144331 https://www.simform.com/ecosystem-serverless-technologies/ https://medium.com/berkman-klein-center/the-american-museum-of-exploding-cars-and-toys-that-kill-you-5123f35cb271 https://towardsdatascience.com/16-useful-advices-for-aspiring-data-scientists-6da9afa8c72c

Week 5: Reading list & news to keep up-to-date with Javascript, Python, DevOps, Cloud, Serverless…

By , Founder and CEO at Washlava. on . Todd Belveal Originally published Quora We all want our startups to be the next Uber, the next Airbnb. Everyone loves a good underdog story. Whether it’s a 15-seed running deep into your March Madness bracket, or an NFL quarterback who’s working at a grocery store to keep his dream alive, we’re all eager to see the little guy win every now and then. And some of us get a similar thrill when a startup muscles its way into an industry, going up against established players and coming out on top. Everyone is looking at industries with established companies and wondering how they can topple them. The question is, is it possible? Is this industry ripe for disruption? I’ve founded two startups in two very different industries — rental cars and laundry — and I can tell you, there are some signs. There are things that will tip you off that an industry could use some new blood. 1. Power Is Consolidated One of the important signs that an industry could be disrupted is imbalance, or dominance by one side of the economic equation. Oligopolies, where a few companies have consolidated vast amounts of the market share either on the supply or demand side, are often good candidates. Rental car companies are a great example. Nine brands control 80% of the market. Not too bad, right? But there are actually just three companies that own those nine brands. Two of those company headquarters are actually in the same little town in New Jersey. It’s incredibly consolidated. And that makes it seem difficult to disrupt because the barriers to entry look high. But that sort of entrenched, oligopolistic behavior (despite competition) generally leads to a greater incentive to preserve the past rather than innovate for the future. So, when things do change, those huge companies can stumble. They’re big, slow, and often too complex to adapt to new technologies. That’s where startups shine. Startups have the ability to work fast, to develop quickly. Speed is their greatest asset, other than their technology. You may be a small fry, but you’ll likely find opportunities when you enter an industry with these dynamics. A lot of the large companies in these industries have become more self-aware, and you may even find an exit among these important players. 2. Consumers Are Using Outdated Technology Massive companies often have incredibly complex systems, and that makes it difficult for them to implement new technology quickly. Creating a smartphone app seems pretty simple, until you realize you have to deal with your complicated backend system and business model. A mobile platform requires simplicity, not complexity. Industries where the major players are stuck using old technology are usually ripe for disruption. A good example of this is the laundry industry. I know, disrupting laundromats doesn’t sound flashy, but that’s exactly the point. Laundry isn’t flashy. It’s old school. Whether you’re doing it in your apartment building, or you’re heading to the laundromat, there’s a good chance you’re still using coins. At best, you might have a rechargeable card you can swipe. My current startup, Washlava, brings together the machines, the technology, and the users. Consumers can use the app to check which washers and dryers are open, reserve them, and then pay with their phone at the laundromat. No more hoarding quarters. No more trying to force your wrinkled dollar into a change machine. It isn’t the craziest idea anyone’s ever had, but it feels like it because of how antiquated the current system is. Old technology is often used. Not because it’s the best way to do things for consumers, but because it’s the best way to do things for a few entrenched business interests. That’s an opportunity for someone to give consumers what they really want. 3. Business Practices Aren’t Changing — Despite Negative Consumer Sentiment When developing the original concept of Silvercar, what struck me was just how universally panned the car rental industry was. I was obviously inspired by my frustrations, but I was not alone. Car Rental has a net promoter score of 23. (Just to give you a benchmark if you’re unfamiliar with the NPS, Apple has a net promoter above 80). Basically less than 1 in 4 rental car customers in the US would recommend these brands to a friend. At Silvercar, we were working to create a simplified experience for renting a car. Something that might not be for everyone. But for the right person — say, a business traveler — it would feel like a godsend. Despite the sentiments reflected above, car rental companies did very little to improve the overall experience — either because they didn’t want to, or simply couldn’t due to the complexity of their systems and operating models. After Silvercar was launched, National rolled out their “choose any car on the lot” campaign. It was a smart move because choosing a car was part of our value proposition, but it was also an easy move. It wasn’t exactly a game changer, because you’re still choosing from the unexciting selection of cars on the lot. But that type of fealty to the status quo is exactly why companies are sluggish when they have to respond to changes in the market. It’s exactly why startups can flourish in those markets. 4. The Research Backs You Up I always recommend that people spend a lot of time, and even a lot of money, doing research on the industry and the idea. Why? Because research breeds confidence. Confidence in your idea and confidence in your business plan. Spend what you need to make sure there’s really an opportunity. Figure out who the competitors are and what they have. You need to know what you’re getting into from a competitive standpoint, and you need to know that your company could truly contribute to the industry. The research is what will confirm all the other points I’ve talked about. Or it may tell you that breaking in will be tougher than you thought. Whatever the case, put in the time to research what you’re getting into. It’ll give you the confidence you need to take your shot at disrupting an entire industry. By , Founder and CEO at Washlava. on . Todd Belveal Originally published Quora For more trending tech answers from , visit . Quora HackerNoon.com/quora

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