Learn Cypress, Python and React-storybook

Imagine the following.

You join a new project and after getting access to the repo you notice that there's no tests.

You decide to ask the team whether they are in different repo or something and unfortunately the answer is:

>We don't have any tests, we didn't have the time.

😐

In this collection we're going to learn how to add e2e tests with cypress.io to an existing React app in order to do two things:

- 🌟 Ensure that our app currently works as intended
- 🌟 Allow us to add features to it with a technique called "Cypress Driven Development" 

In less than 20 minutes we're going to go through:

- ⚡️Adding cypress.io setup to an existing project
- ⚡️Testing search functionality with `data-cy` properties
- ⚡️Using "Cypress Driven Development" to add a new feature to an app
- ⚡️Use Cypress UI to debug an API response
- ⚡️How to use `cy.only` and `cy.skip` in order for us to build our e2e test suite faster

and much more!

Fast, easy and reliable testing for anything that runs in a browser.

cypress

Add e2e tests with cypress to a React application

One of the most important — but most ignored — practices for web developers is performing end-to-end testing on applications before they go live into production. You know: making sure they work like they’re supposed to. But testing can be tedious, and definitely not fun.

In this course, Brett Cassette will show you how you can test all layers of your application stack, simultaneously, with Cypress. When you use Cypress, it’s like having a robot that uses your app the way a real user would. Cypress reports if things work the way you designed them to — and if they don’t — every step of the way. And it’s fun.

After completing this course, you’ll be ready to apply the same E2E testing principles to your own applications. Stop leaning on your QA department (if it exists) to stress-test your application, and ship your app knowing it’s ready. 


Test Production Ready Apps with Cypress

Neural networks, with Keras, bring powerful machine learning to Python applications. They can answer questions like “How much traffic will hit my website tonight?” or answer classification questions like “Will this customer buy our product?” or “Will the stock price go up or down tomorrow?”

In this course, we’ll build a fully connected neural network with Keras. This is the most basic type of neural network you can create, but it’s powerful in application and can jumpstart your exploration of other frameworks. 

We’ll start the course by creating the primary network. Then we’ll:

- build and configure the network, then evaluate and test the accuracy of each
- save the model and learn how to load it and use it to make predictions in the future
- expose the model as part of a tiny web application that can be used to make predictions

You don’t need to know a lot of Python for this course, but some basic Python knowledge will be helpful. Enjoy!

Python is a widely used dynamically typed programming language. It allows for many different programming paradigms including object-oriented, imperative, functional programming, and procedural styles.

python

Fully Connected Neural Networks with Keras

The tools available for unit testing have come a long way. While testing may never be “easy”, the available tools have certainly moved testing closer to the simple end of the spectrum. Full end to end testing hasn’t quite kept the same pace and as a result many applications are testing manually and often by users in production. Cypress provides a testing environment that makes end-to-end testing fast and reliable with a simple API that makes creating automated unit tests for your web applications a no-brainer.


End to End testing with Cypress

In Introduction to Python, we will start at the beginning: Installing python 3 and using virtual environments. With a working environment, we will learn how to manipulate strings and numbers, and gain working knowledge of data structures and flow control. We will build on that by introducing modules and classes and learn how to interact with our python applications using command line switches, console input and files.

At the end of this course, you’ll have a great working knowledge of Python, fully capable of creating your own Python projects from scratch or jumping into an existing application with the capability to read, understand, and contribute to the application.

Check out these [**community notes for this course on Github**](https://github.com/eggheadio-projects/introduction-to-the-python-3-programming-language-notes).

Introduction to the Python 3 Programming Language

Artificial intelligence. Machine learning. Bots. Computers learning and communicating with us to do our bidding. But, where do you start? How do you get a machine to even begin to understand what you speak or type at it? There are several common machine learning algorithms that will help us begin to answer these questions.

In this course we’ll learn about common machine learning algorithms that don’t require implementing a neural network. We will not be going too much into the math behind them, but instead learn what each algorithm is good for, and how to train them. We'll also learn about a few metrics for evaluating models.

We’ll implement these in Python using `scikit-learn` using `scikit-learn`’s built-in data sets. The focus of this course is on implementation and a high-level understanding of these algorithms. 

We'll look at a few ways to evaluate our models, for both classification and regression models. We'll touch on mean squared error and coefficient of determination (for regression), and accuracy score, logarithmic loss, confusion matrices, and classification reports (for classification). 

Python 2.7 is used in the lesson videos but the code provided has Python 3 available. The only breaking change is the `print` statement API.

For additional information on installation, vocabulary, and common errors visit the `README.md` to the course code attached to each lesson.

Simple and efficient tools for data mining and data analysis

scikit-learn

Introductory Machine Learning Algorithms in Python with scikit-learn

AWS Amplify allows developers to create, configure, and deploy Serverless APIs and functions directly from their front-end environment.In this collection, you'll learn how to deploy various types of APIs and functions using the Amplify CLI. You'll also learn how to share code among various functions using Lambda layers and schedule functions to run at an interval to implement Serverless cron jobs.

JavaScript® (often shortened to JS) is a lightweight, interpreted, object-oriented language with first-class functions, most known as the scripting language for Web pages, but used in many non-browser environments as well such as node.js or Apache CouchDB. It is a prototype-based, multi-paradigm scripting language that is dynamic, and supports object-oriented, imperative, and functional programming styles.

javascript

Up and Running with AWS Amplify Functions

Learn how to create tuples in Python. The videos in this collection will also cover more advanced features of tuples

The Ins and Outs of Python Tuples

React is one of the web’s most popular frameworks for building JavaScript applications. 

If you know what you’re doing, React can drastically simplify how you build, use, and maintain code.

Whether you’re a React newbie or you’re ready for advanced techniques, you can level-up with egghead. Explore our in-depth courses, lessons, and community resources to build more powerful applications and crack open your career.

react

Storybook is a UI component development environment for React, Vue, and Angular. With that, you can develop UI components without running your app.

Here I show how best to configure a React Storybook to use [Typescript](/q/typescript) (with TSX). This lets you write your reusable components with maximal type safety. Please see the provided code for the full source.

Configure React Storybook for use with Typescript

So you have a production application, and it doesn't use Cypress yet.

In this course, you'll learn how to add Cypress to your existing npm application, and run it for the first time.

Recommended Homework: 
- Look through the tests Cypress spins up for you and learn how typical Cypress tests are setup

Install Cypress in a Production Application

Using cy.server, cy.route, and cy.wait, we can intercept requests to our real backend, and return known mock responses.

The pros of using this approach:
- It's much faster than interacting with the real backend
- Doesn't need to seed data to the backend
- Easy control of requests and responses
- Can mock network delay and failure

Cons:
- No guarantee the stubs match the real backend responses
- Not full end-to-end testing

Recommended Homework:
- Mock the route to create a new todo, and reply with a 200
- Mock the route to edit an existing todo, and reply with a 200

Mock Your Backend with Cypress

Cypress can do more than end-to-end testing. It can fully drive remote applications, including ones you don't own, much more reliably than Selenium.

In this example, we'll use Cypress to build a web scraper to mine data from applications we don't own. This data could be used to power other applications, filtered for data relevant to your use cases, sent to your calendar, or any number of uses. 

Since Cypress is easy to automate, it's easy to integrate with cron or a job framework to run a scraper in an automated fashion.

Create a Web Scraper with Cypress

We can reuse our stubbed responses between tests by using Cypress fixtures. In this lesson, learn how to share data between tests.

Reuse Data with Cypress Fixtures

Learn how to setup your [Cypress development environment](https://docs.cypress.io/guides/tooling/intelligent-code-completion.html#Reference-type-declarations-via-tsconfig) with Intellisense, custom Cypress configuration in cypress.json, and [get settings autocompletion in VSCode](https://docs.cypress.io/guides/tooling/intelligent-code-completion.html#Set-up-in-your-Dev-Environment-1).  

Recommended Homework:
- Setup your Cypress dev environment

Setup Your Cypress Dev Environment

If we want to do full end-to-end testing, then we need to seed our database into a reliable state at the start of every test. In this lesson, we'll learn how to build a Cypress plugin that updates the data in our database.

Seed Your Database in Cypress

Learn how to target the elements on your UI, interact with them, and make assertions on their state.

Recommended Homework:
- Write a test that creates a new todo and asserts that the todo is present on the DOM
- Write a test that finishes a todo, and asserts that the todo shows as completed

Write Your First Cypress Integration Test

Which selectors your choose for your tests matter, a lot. In this lesson, we'll see the recommended Cypress best practices for selectors, and why we should prefer the data-cy attribute.

Recommended Homework:
- Finish the example using cy.contains, and remove the false positives to get a richer sense of why data-cy is a better choice for our selector

Use the Most Robust Selector for Cypress Tests

The Cypress programming model is similar to async and await. When you write Cypress commands, they will be enqueued to be run at a later time, but they will run in the same order as you write them. 

Recommended Homework:
- Verify that Cypress commands run in the order you write them, and not based on when they asynchronously finish. Recommended solutions include using timeouts, and/or synchronously available values as with cy.wrap


Debug and Log with Cypress

Cypress doesn't provide out-of-the-box ability to make assertions on our frontend stores, so let's expose the store to the tests, and assert on it. We'll use our knowledge of Cypress's asynchronous programming style to access properties and functions on the store using cy.its and cy.invoke.

Recommended Homework:
- Consider why it's valuable to test against your frontend store when you can test against the UI

Assert on Your Redux Store with Cypress

Do you need to reuse complex Cypress calls often (like when accessing the store)? 

You can turn them into custom Cypress commands, and even customize their output in the time-traveling debugger, so it's easy to see a snapshot at the point your command ran!

 Recommended Homework:
- Review the Best Practices for Custom Commands - https://docs.cypress.io/api/cypress-api/custom-commands.html#Best-Practices

Create Custom Cypress Commands

External libraries tend to be synchronous, so how do we integrate other powerful tools into the Cypress framework? This lesson walks us through merging in the Lodash library to Cypress to allow us to slice and dice data to more accurately assert on just the pieces of data we care about.

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