LikeMagic

likemagic

DRY, or Don't Repeat Yourself, is one of the most well-known software development principles. Many engineers will go to great lengths to achieve this laudable goal, so a successful programming language must be designed with this in mind. Interfaces are just one of the tools that programming languages implement to enable code reuse. Although there are countless differences in the way programming languages implement interfaces, they all define a contract for interaction between software components. Differentiating itself from the typical object-oriented implementation, the Go programming language implements interfaces in a way that aims to reduce complexity, encourage composition, and enable flexible use of the types. Implicit Interfaces The most impactful difference between Go’s interfaces and those from object-oriented languages is that Go’s interfaces work by implicit implementation. Rather than requiring a type to explicitly declare that it implements an interface, determines that a type satisfies an interface as long as it implements all of the required method signatures. The different types and the interface they implement are only coupled by the code using them, rather than always being linked together. Go This difference offers a few key benefits: Reduce complexity and coupling by limiting the hierarchical inheritance of interfaces Simplify types that implement multiple interfaces Enables you to add interfaces later on when they become necessary instead of designing entire applications around them Interfaces can be created for types in different packages This last benefit is the most interesting and unique. The ability to define an interface based on external types leads to a few more specific advantages: Client code can define how it will use types rather than relying on library code to tell it how types must be used Create polymorphic relationships with types that you do not own or cannot change, which allows you to use them more flexibly Solve import cycle issues by changing the location of interface definitions Create mocks of imported types to make your code more testable No Inheritance By excluding inheritance, Go reduces the complexity that can occur from a deep hierarchical structure. When programs are designed around a base set of classes or interfaces, any simple changes to those base structures require a significant refactor. The alternative practice of composition leads to reduced complexity and more readable code. Composition relies on splitting up functionality among different types and using them together, instead of re-defining the functionality of types through inheritance. Now you can re-use these individual components elsewhere, add more functionality with new components, and easily refactor or remove existing ones. Instead of being concerned about what type something , your code just needs to know about what that type , and luckily the interface informs it. is can do Use Case: Polymorphism Polymorphism is perhaps the entire reason behind the existence of interfaces. This common practice is one of the most effective and easy-to-use methods of code reuse. Since an interface defines a strict contract for how types are used, these different types can be used interchangeably; this is polymorphism. A very common and useful scenario for this is having a flexible storage backend for your program: use Postgres in production, SQLite when running locally, and mocks when testing (or skip the database mocks, but that's a topic for another day). type StorageClient interface {
    GetValue(id string) (string, error)
}

func NewStorageClient(clientType string) (StorageClient, error) {
    switch clientType {
        case "sqlite":
            return sqlite.NewClient()
        case "postgres":
            return postgres.NewClient()
        default:
            return nil, fmt.Errorf("invalid client type: %s", clientType)
    }
} This implementation allows you to easily use the interface throughout the program without concern for the data storage layer behind it. StorageClient Use Case: Testing and Mocks You can take advantage of the implicit nature of interfaces by defining an interface for the functions you use from an external library. For example, you are assigned a task to implement a function that fetches recent rain data, in inches, from a weather data API. The made-up weather provider publishes Go package called , which provides a struct with various weather-related methods returning data in metric units: weather Client func GetRainInches(since time.Duration, client weather.Client) (float32, error) {
    rainMM, err := client.GetRain(since)
    if err != nil {
        return 0, fmt.Errorf("error getting data from API: %w", err)
    }

    return rainMM / 2.54, nil
} How will you unit test this code? Since Go has implicit interfaces, you can create your interface that just defines the methods that you need from the library. Now, if your function expects this interface instead, you can create your mocks. Since you currently just need the method, this is simple: GetRain type WeatherClient interface {
    GetRain(time.Duration) (float32, error)
}

func GetRainInches(since time.Duration, client WeatherClient) (float32, error) {
    rainMM, err := client.GetRain(since)
    if err != nil {
        return 0, fmt.Errorf("error getting data from API: %w", err)
    }

    return rainMM / 2.54, nil
} Then, your test file might contain a new struct that also implements the interface: type MockWeatherClient struct {
    expectedErr error
    expectedMM  float32
}

func (c MockWeatherClient) GetRain(time.Duration) (float32, error) {
    return c.expectedMM, c.expectedErr
} After this simple refactor, you do not depend on external libraries to provide interfaces that make your code testable! You have the additional side-effect of being one step closer to allowing your program to use different weather APIs generically. Conclusion and Warnings While interfaces in Go were designed with considerations for simplicity and avoiding some of the common pitfalls of object-oriented patterns, there are still some things to be aware of. It may be tempting to define interfaces for everything with the hopes that you can create a more generic and flexible program. Remember that one of the implicit interfaces is that you can easily create a new interface when you need it without making changes to any existing types, so there is no benefit to creating interfaces early in the process. Additionally, since types do not explicitly declare which interfaces they implement, it may be hard to tell which types are being used by your program when you have superfluous interfaces. While there are always tradeoffs and no perfect solutions, I have found Go's version of interfaces to be incredibly flexible, intuitive, and useful. I have been able to create more flexible programs and improve testability all while minimizing complexity. Also published here.

Go Interfaces Are Just Like Magic

Suppose Peggy needs to prove to Victor that she is in possession of a secret without revealing the secret. Can she do so in a way that convinces Victor that she really does know the secret? This is the question at the heart of one of the most powerful cryptographic processes we can employ in identity systems: . (ZKPs) zero-knowledge proofs Suppose for example that Peggy has a digital driver's license and wants to prove to Victor, the bartender, that she's over 21 without just handing over her driver's license or even showing him her birthdate. ZKPs allow Peggy to prove her driver's license says she's at least 21 in a way that convinces Victor without Peggy having to reveal anything else (i.e., there's zero knowledge). excess This problem was first explored by MIT researchers Shafi Goldwasser, Silvio Micali, and Charles Rackoff in the 1980s as a way of combatting information leakage. The goal is to reduce the amount of information the verifier, Victor, can learn about the prover, Peggy. extra One way to understand how ZKPs work is the story of the Cave of Alibaba, first published by cryptographers Quisquater, Guillou, and Berson1. The following diagram provides an illustration. The Cave of Alibaba has two passages, labeled A and B, that split off a single passageway connected to the entrance. Peggy possesses a secret code that allows her to unlock a door connecting A and B. Victor wants to buy the code but won't pay until he's sure Peggy knows it. Peggy won't share it with Victor until he pays. The algorithm for Peggy to prove she knows the code proceeds as follows: Victor stands outside the cave while Peggy enters and selects one of the passages. Victor is not allowed to see which path Peggy takes. Victor enters the cave and calls out "A" or "B" at random. Peggy emerges from the correct passageway because she can easily unlock the door regardless of which choice she made when entering. Of course, Peggy could have just gotten lucky and guessed right, so Peggy and Victor repeat the experiment many times. If Peggy can always come back by whichever passageway Victor selects, then there is an increasing probability that Peggy really knows the code. After 20 tries, there's less than one chance in a million that Peggy is simply guessing which letter Victor will call. This constitutes a that Peggy knows the secret. probabilistic proof This algorithm not only allows Peggy to convince Victor she knows the code, but it does it in a way that ensures Victor can't convince anyone else Peggy knows the code. Suppose Victor records the entire transaction. The only thing an observer sees is Victor calling out letters and Peggy emerging from the right tunnel. The observer can't be sure Victor and Peggy didn't agree on a sequence of letters in advance to fool observers. Note that this property relies on the algorithm using a good pseudo-random number generator with a high-entropy seed so that Peggy and third-party observers can't predict Victor's choices. Thus, while Peggy cannot deny to Victor that she knows the secret, she can deny that she knows the secret to other third parties. This ensures that anything she proves to Victor stays between them and Victor cannot leak it—at least in a cryptographic way that proves it came from Peggy. Peggy retains control of both her secret and the fact that she knows it. When we say "zero knowledge" and talk about Victor learning nothing beyond the proposition in question, that's not perfectly true. In the cave of Alibaba, Peggy proves in zero-knowledge that she knows the secret. But there are many other things that Victor learns about Peggy that ZKPs can do nothing about. For example, Victor knows that Peggy can hear him, speaks his language, walks, and is cooperative. He also might learn things about the cave, like approximately how long it takes to unlock the door. Peggy learns similar things about Victor. So, the reality is that the proof is zero knowledge not zero knowledge. approximately perfectly ZKP Systems The example of Alibaba's Cave is a very specific use of ZKPs, what's called a . Peggy is proving she knows (or possesses something). More generally, Peggy might want to prove many facts to Victor. These could include propositional phrases or even values. ZKPs can do that as well. zero-knowledge proof of knowledge To understand how we can prove propositions in zero knowledge, consider a different example, sometimes called the . Suppose Peggy and Victor want to know if they're being paid a fair wage. Specifically, they want to know whether they are paid the same amount but don't want to disclose their specific hourly rate to each other or even a trusted third party. In this instance, Peggy isn't proving she knows a secret, rather, she's proving an equality (or inequality) proposition. Socialist Millionaire Problem For simplicity, assume that Peggy and Victor are being paid one of $10, $20, $30, or $40 per hour. The algorithm works like this: Peggy buys four lock boxes and labels them $10, $20, $30, and $40. She throws away the keys to every box except the one labeled with her wage. Peggy gives all the locked boxes to Victor who privately puts a slip of paper with a "+" into the slot at the top of the box labeled with his salary. He puts a slip with a "-" in all the other boxes. Victor gives the boxes back to Peggy who uses her key in private to open the box with her salary on it. If she finds a "+" then they make the same amount. Otherwise, they make a different amount. She can use this to prove the fact to Victor. This is called an and proves the proposition true or false in zero knowledge (i.e., without revealing any other information). oblivious transfer VictorSalary = PeggySalary For this to work, Peggy and Victor must trust that the other will be forthcoming and state their real salary. Victor needs to trust that Peggy will throw away the three other keys. Peggy must trust that Victor will put only one slip with a "+" on it in the boxes. Just like digital certificates need a PKI to establish confidence beyond what would be possible with self-issued certificates alone, ZKPs are more powerful in a system that allows Peggy and Victor to prove facts from things others say about them, not just what they say about themselves. For example, rather than Peggy and Victor self-asserting their salary, suppose they could rely on a signed document from the HR department in making their assertion so that both know that the other is stating their true salary. provide a system for using ZKPs to prove many different facts alone or in concert, in ways that give confidence in the method and trust in the data. Verifiable Credentials Non-Interactive ZKPs In the previous examples, Peggy was able to prove things to Victor through a series of interactions. For ZKPs to be practical, interactions between the prover and the verifier should be minimal. Fortunately, a technique called SNARK allows for non-interactive zero-knowledge proofs. have the following properties (from whence they derive their name): SNARKs the sizes of the messages are small compared to the length of the actual proof. Succinct: : other than some setup, the prover sends only one message to the verifier. Non-interactive this is really an argument that something is correct, not a proof as we understand it mathematically. Specifically, the prover theoretically could prove false statements given enough computational power. So, SNARKs are "computationally sound" rather than "perfectly sound". ARguments: the prover knows the fact in question. of Knowledge: You'll typically see "zk" (for zero-knowledge) tacked on the front to indicate that during this process, the verifier learns nothing other than the facts being proved. The mathematics underlying zkSNARKs involves homomorphic computation over high-degree polynomials. But we can understand how zkSNARKs work without knowing the underlying mathematics that ensures that they're sound. If you'd like more details of the mathematics, I recommend . Christian Reitwiessner's "zkSNARKs in a Nutshell" As a simple example, suppose Victor is given a hash, , of some value. Peggy wants to prove that she knows a value such that without revealing to Victor. We can define a function that captures the relationship: sha256 H s sha265(s) == H s C C(x, w) = ( sha256(w) == x ) So, , while other values for will return . C(H, s) == true w false Computing a zkSNARK requires three functions , , and . is the key generator that takes a secret parameter called and the function and generates two public keys, the proving key and the verification key . They need only be generated once for a given function . The parameter must be destroyed after this step since it is not needed again and anyone who has it can generate proofs. G P V G lambda C pk vk C lambda fake The prover function takes as input the proving key , a public input , and a private (secret) witness . The result of executing is a proof, , that the prover knows a value for that satisfies . P pk x w P(pk,x,w) prf w C The verifier function computes which is true if the proof is correct and false otherwise. V V(vk, x, prf) prf Returning to Peggy and Victor, Victor chooses a function representing what he wants Peggy to prove, creates a random number , and runs to generate the proving and verification keys: C lambda G (pk, vk) = G(C, lambda) Peggy must not learn the value of . Victor shares , , and with Peggy. lambda C pk vk Peggy wants to prove she knows the value that satisfies for . She runs the proving function using these values as inputs: s C x = H P prf = P(pk, H, s) Peggy presents the proof to Victor who runs the verification function: prf V(vk, H, prf) If the result is true, then the Victor can be assured that Peggy knows the value . s The function does not need to be limited to a hash as we did in this example. Within limits of the underlying mathematics, can be quite complicated and involve any number of values that Victor would like Peggy to prove, all at one time. C C This article is an excerpt from my book , published by O’Reilly Media. Learning Digital Identity Notes Quisquater, Jean-Jacques; Guillou, Louis C.; Berson, Thomas A. (1990). How to Explain Zero-Knowledge Protocols to Your Children (PDF). Advances in Cryptology – CRYPTO '89: Proceedings. Lecture Notes in Computer Science. 435. pp. 628–631. doi:10.1007/0-387-34805-0_60. ISBN 978-0-387-97317-3. Also published here.

Zero-Knowledge Proofs: It's Like Magic, but I'll Explain It

জিরো-নলেজ প্রুফ: এটা ম্যাজিকের মত, কিন্তু আমি এটা ব্যাখ্যা করব

Zero-Knowledge-Beweise: Es ist wie Magie, aber ich werde es erklären

Pruebas de conocimiento cero: es como magia, pero lo explicaré

Preuves sans connaissance : c'est comme par magie, mais je vais l'expliquer

शून्य-ज्ञान प्रमाण: यह जादू की तरह है, लेकिन मैं इसे समझाऊंगा

知識ゼロの証明: 魔法のようなものですが、説明します

영지식 증명: 마술과 같지만 설명하겠습니다.

Provas de conhecimento zero: é como mágica, mas vou explicar

Доказательства с нулевым разглашением: это похоже на волшебство, но я объясню это

Sıfır Bilgi Kanıtları: Sihir Gibi, Ama Açıklayacağım

Bằng chứng không có kiến thức: Nó giống như phép thuật, nhưng tôi sẽ giải thích nó

零知识证明：就像魔法，但我会解释它

Before you accuse me of pushing hallucinogens, I should clarify that this is a Super Mario metaphor. The only trip you should take is a trip to your mentor’s office. Mushrooms are Mario’s secret weapon in the same way that mentors are mine. They make him grow bigger and mean he can take an extra hit without dying. Ultimately, they help him to advance through the levels towards his end goal. The journey from the first-time founder to an established entrepreneur takes time. It can also feel like climbing a mountain. Getting to the peak is a heck of an achievement, but the cliff edges are steep, and the weather is treacherous. And if you’re an Italian plumber, you also have to watch out for Goombas and Koopas. A good mentor is like one of Mario’s magic mushrooms. They’re a that can support you when you first establish your business through consolidation and growth until you’re ready to sell, merge or retire. power-up With the right mentor, you’ll be able to find quick solutions to complex problems, saving yourself time, stress, and money. They’ll help you live your dreams and achieve your goals, enhancing your life, leadership, and business skills. Six Ways Mentors Can Help Keeping this list down to six points was a challenge, but it also helped me focus on only the main ways that mentors can help you. Here are the most significant benefits that I’ve experienced from having a mentor and being one myself. 1. 10x Your Learnings Mentors know things that their mentees don’t, thanks to their years of hard work and experience. They can offer a shortcut to learning and knowledge, especially when people are starting their first business or branching out into a different industry. They can teach you: How to grow a business How to retain customers How to fundraise How to hire and manage employees How to develop a strategy How to stay compliant in regulated industries You can learn a lot from your mentors, and the lessons are quicker and easier than learning through trial and error. Instead of learning from your own (costly) mistakes, you can learn from people who’ve been there, done that, and bought the t-shirt. “Live as if you were to die tomorrow. Learn as if you were to live forever.” – Mahatma Gandhi 2. Support and Motivation Entrepreneurship can be lonely, and I know from experience that it often feels as though the weight of the world is on your shoulders. Fortunately, mentors are uniquely placed to understand entrepreneurship's struggles and provide moral support and motivation. They also know better than anyone that the rewards are waiting on the other side if only you persevere and keep going. When you’re an entrepreneur, self-confidence is key. That’s why people say you should “fake it ‘til you make it,” although that’s not always good advice and so take it with a pinch of salt. It’s not an excuse to skip your homework or pretend you know something you’re clueless about. We all need some assurance from time to time, especially when making tough decisions or second-guessing ourselves. Mentors have weathered enough storms and made enough decisions in their time to be able to offer convincing reassurance. This reassurance can help you to keep your spirits up, even during the darkest of times. We all experience failure and setbacks along the way, but mentors have experienced these setbacks and powered through them. They know what to say to offer comfort and boost morale, and they can also offer ideas that can help you mitigate those setbacks and avoid repeating the same mistakes that they made. 3. Networking Mentors are well-connected, and their connections can open doors and lead to collaboration and career growth opportunities. They can hook you up with strategic partnerships, customer trials, potential investors, and much, much more. In fact, you’ll find that most investors are more likely to invest in your startup if you’ve been referred to them by a friend. They say it’s not what you know but who you know, but that’s only half of the story. It’s not what you know or who you know; it’s what and who your mentor knows. “Experience is a master teacher, even when it’s not our own.” – Gina Greenlee, bestselling author, speaker and coach 4. Level Up Instantly Mentors have a unique kind of experience that you won’t be able to find elsewhere. This applies to subject matters and industries, but also specific situations. When you have a super-specific question, you usually can’t just Google it. The good news is that with a mentor, you don’t need to. There's rarely a one-size-fits-all answer when it comes to topics like SEO, user acquisition, product-market fit, fundraising, and business models. There’s rarely even a one-size-fits-all question! This is where mentors’ unique experience comes into play because you can talk the issues through with them and benefit from their insights and years of experience. In the rare instances where they can’t help you out directly, the chances are that they’ll point you in the right direction. You’ll level up instantly, just like Mario and his magic mushrooms. 5. Increased EQ We’ve all heard of intelligence quotients (or IQ). EQ stands for emotional quotient, and it’s essentially the emotional equivalent. By working with a successful and mature mentor, young entrepreneurs can access a shortcut to improving their emotional quotient and become more rounded individuals. They’ll gain greater control over their emotions and be more at peace with themselves, but they’ll also enjoy better business relations. 6. The harsh truth I’m going to let you in on a little secret: you already have enough cheerleaders. Even the loneliest of entrepreneurs have a support network of friends and family who believe in them. The people who love and care for you will always be there to sing your praises and to tell you that you’re awesome, and that’s fantastic. The problem is that they’ll rarely ask you the tough questions or tell you when your great new idea actually sucks. You don’t need more yes men telling you that if you put enough time in, you’re sure to succeed. You need mentors who’ll cut through the crap and tell you the truth, even when you don’t want to hear it. The Truth You can’t do everything alone, and even if you could, you shouldn’t. Let’s take the best-case scenario, which is that you already have the answer somewhere inside you. Even when that’s the case, the most reliable way to uncover it is to talk it over with someone neutral and knowledgeable. It’s easy to get distracted and to chase after every shiny new idea. Luckily, surrounding yourself with the right people can keep you grounded and focused. You can also avoid costly mistakes by learning from the people who’ve gone before you. One-on-one consultation is the way to go, providing you with a safe space to discuss your challenges and doubts confidentially. But don’t make the mistake of thinking of it as a one-way street. Mentoring is mutually beneficial. The person being mentored has access to experience and know-how, and the mentor can develop trusted partnerships within their business community. Mentors can learn a lot about themselves through mentoring. They can also draw from that experience to approach their problems and challenges in new (and better) ways. I’m not just saying this, either. I’ve seen it happen through my own mentoring. I’ve learned countless lessons that I’ve used to improve myself, and I’ve even integrated some of them into the processes I use when building startups and businesses. No matter where you are along the journey, you can give yourself the best chance of success by finding a mentor to walk alongside you and share their hard-earned advice and experience. So if anything that you’ve read today has resonated with you, start looking for a mentor today! Originally published on https://www.growthreflection.com/why-every-founder-needs-a-mentor-2/.

Mentors Are Like Magic Mushrooms

The ReactJS structure based on Javascript has gained increased popularity over the last couple of years. And rightfully so — it is a very flexible and, without modesty, perfect solution for the creation of Single Page Applications, also very popular at the moment. React also helps develop interactive web apps with impeccable performance due to the server-side rendering. Add on top of that, with support by the Facebook team as well as an extensive online community, you can see for yourself how utterly efficient it can be for the frontend development of web apps. Nevertheless, React alone isn’t enough to build fully-featured web software. An API must also be integrated with the created frontend to provide server interaction and data transfer. We, similarly to a multitude of developers, prefer a Python-based framework, Django Rest, for this task, invented especially for backend development. Despite Django and React basically being two independent structures, a frontend and backend written with the help of each work together pretty well. We prepared a brief tutorial where we tell you how to use React with Django. Why is the React + Python (Django) combination so efficient? Up until frontend frameworks first appeared in the field of IT development, all of the business logic of applications worked on the server side exclusively. The UI only had to display certain reactions to user requests. When solutions like React and Angular appeared, the web app construction approach experienced significant changes. The process of user-server interaction became more advanced. A synchronization of user input with user interface elements started being implemented in the frontend software code. This allowed significantly accelerating web soft performance and provided a brand-new level of UX. It also made the developers’ job easier — apps written with the help of this method are much more scalable in nature. As to the React and Django pair, it’s practically a canonical combination for building web software globally. The thing is, both Python and Django feature a steep learning curve, vast online community, and a good set of built-in tools that allow users to focus on the construction of software logic instead of other, more trivial aspects. React is quite accessibly integrated with Django. Moreover, this framework is praised for its (as opposed to that provided by Angular, for instance), accessible learning curve and advanced flexibility (which, however, can spawn certain difficulties for inexperienced developers like in-depth code errors). very rapid rendering Well, have we convinced you that Django with React is worth your attention? Then let’s get to some tips for the creation of a backend (via Django) and frontend (via React). Writing backend First things first — install the Django framework and connect the database in the following manner: $ virtualenv env $ source env/bin/activate $ pip3 install django djangorestframework django-filter $ pip3 freeze > requirements.txt Now we can open our project and launch the application: $ django-admin startproject backend $ cd backend $ django-admin startapp api The next step would be the configuration of the selected database in accordance with plus the . Django working requirements configuration of DRF Now, to one of the most important aspects of software creation — the authentication procedure. In our example, we are taking a look at authentication with the use of tokens. This isn’t the most reliable option because tokens are stored on the server end for an unlimited time — if some hacker finds this weak point in your solution, they will successfully hack it. On the other hand, this method of authentication allows us to demonstrate a way to provide the connection between the UI and server side through an API. Read about how to configure tokens on the . Django official website See how the authentication procedure with the help of tokens can be implemented below — particularly, the importance of a name and password of a certain user in your redux Store: # file: api/urls.py l from django.conf.urls import ur from rest_framework.authtoken import views as drf_views urlpatterns = [ url(r'^auth$', drf_views.obtain_auth_token, name='auth'), ] The next step would the definition of urlpatterns: # file: backend/urls.py from django.conf.urls import url, include urlpatterns = [ url(r'^', include('api.urls', namespace='api', app_name='api')), ] Adding this code to the project, you teach your software to track resource location indicators. The following lines are for testing the app performance — they launch the performance of the server end, however, the testing itself can only be launched once the frontend has been worked on. $ python3 manage.py migrate $ python3 manage.py createsuperuser $ python3 manage.py runserver 0.0.0.0:8000 Once you’ve written the code for the server end, you can easily check where your resource is located via the following command: $ curl -X POST -d "username=username&password=password" http://localhost:8000/auth Writing frontend Let’s start with the definition of how the commonly known feature create-react-app will be used in our code. Notice also that you’ll have to delete default content in the configuration files (we believe this will give you much more flexibility in the further development of frontend performance). $ npm install -g create-react-app $ create-react-app frontend $ cd frontend $ npm run eject Setting up dependencies you’ll find useful further on: $ npm install --save-dev babel-preset-es2015 babel-preset-stage-3 $ npm install --save redux redux-logger redux-persist response-redux $ npm install --save axios response-router-dom lodash Consider the part of code responsible for the connection of backend and frontend. The first thing to do would be to create a redux Store where you’ll carry out the procedure of storing tokens with user data (which an app receives upon authentication): // file: src/store.js import { compose, createStore, applyMiddleware } from 'redux'; import { createLogger } from 'redux-logger'; import { persistStore, autoRehydrate } from 'redux-persist'; import rootReducer from './reducers'; const store = createStore( rootReducer, compose( ( applyMiddleware , createLogger() ), autoRehydrate() ) ); persistStore(store); export default store; // file: src/reducers/index.js import { combineReducers } from 'redux'; import * as actionType from '../actions/types'; const tokenInitialState = null; const token = (state = tokenInitialState, action) => { switch(action.type) { case actionType.SET_TOKEN: return action.data; default: return state; } } const appReducer = combineReducers({ token, }) const rootReducer = (state, action) => { return appReducer(state, action); } export default rootReducer; Next, define actions that must be entered in the form of code into files index.js and types.js: // file: src/actions/index.js import * as actionType from './types'; export const setToken = (data) => { return { type: actionType.SET_TOKEN, data } } // file: src/actions/types.js export const SET_TOKEN = "SET_TOKEN"; Above is the definition of how the solution would act to define the processing of tokens with user data during authentication. Particularly, you can see the way they are being saved on the server end. Now for the lines you’ll need to describe user authorization processes (they include the exceptions processor in which the app behavior upon input of invalid data is defined). // file: src/util/Auth.js import axios from 'axios'; import _ from 'lodash'; import store from '../store'; import { setToken } from '../actions' import { URL, LOGIN } from '../config/Api'; export function InvalidCredentialsException(message) { this.message = message; this.name = 'InvalidCredentialsException'; } export function login(username, password) { return axios .post(URL + LOGIN, { username, password }) .then(function (response) { store.dispatch(setToken(response.data.token)); }) .catch(function (error) { // raise different exception if due to invalid credentials if (_.get(error, 'response.status') === 400) { throw new InvalidCredentialsException(error); } throw error; }); } export function loggedIn() { return store.getState().token == null; } These lines of code allow redux Store tokens to be sent, and, in such a manner, give us the ability to set up an interconnection with authorized users from any place in your solution’s user part. // file: src/util/ApiClient.js import axios from 'axios'; import store from '../store'; import { URL } from '../config/Api'; export const apiClient = function() { const token = store.getState().token; const params = { baseURL: URL, headers: {'Authorization': 'Token ' + token} }; return axios.create(params); } These two chunks of code employ the file including definitions of constants. This file makes it simpler to read the code and reduce the chance of errors due to improper redefinition of variables (in the case of constants, they are redefined once in the ../config/Api file). export const URL = process.env.API_URL; export const LOGIN = "/ auth"; That’s it. You have successfully established the interconnection between backend and frontend using React with Django. Now, you may test the created solution. Just enter the system and enter your authentication data into the respective lines. If everything works correctly, you can open a browser upon the repeated launch of the app in developer mode and see that a token has been transferred to the redux Store servers. Conclusion As you can see, people more or less acquainted with React, Redux, Django, and DRF won’t experience much difficulty in implementing the above-described example. On the other hand, if you’re an inexperienced web developer starting out and want to build a commercial solution — turn to professionals. Originally published at sloboda-studio.com on August 7, 2018.

How to Make React + Django Combination Work Like Magic

User-centered is an approach which helps you shape your product or service in a form that users need, want and desire. Every consumer group has a certain level of social inertia which is basically a resistance to change their behavior. So don't try to persuade and talk them into using what think is a state of the art product. Rather, make it familiar to them, present it as a " " kind of product — from point of view. And here's how you can do it… design you this is exactly what I need their Note: I'm writing about the design in general. It could be website design, website copy, product package, product brochure, facebook ad cover, instagram story or whatever includes visual communication between the seller and the buyer. In further text, I'll refer to this assembly as a "design". It's not about who you are, it's about the value you provide I tend to start almost every project on the wrong note — with the introduction: . Then I remember that whoever found this piece of design was looking for something else. Something of their own interest. So restrain yourself from writing about your awesomeness, and focus on the target customers' needs (except if you're writing a recruiting copy, then bring out the bean bags and free snack). Hello, We are ____ and we love ____ The gap Creating a product is hard. It takes time, money and effort. Going through the process of creating it, you get emotionally attached. So it is natural to market it as the best product ever. Because it is yours and you believe so. But unfortunately, you'll have to do better than that to persuade the customer to buy it. Any business is a relationship between the seller and the buyer. It has two sides involved, each with their own point of view. It is a relationship. Not give & receive, because you don't want to receive just anything, you have a specific prize on your mind. In order for a relationship to work properly, each side should clearly communicate what they give and what they want to take in return. This way, the expectations are met for both sides, and the relationship (business) will grow. give & take Communication via design The design is a medium between the seller and the buyer. And it should be used to visually communicate between two parties, close the gap and seal this give & take relationship. Persona The first step to closing the gap is getting to know the other side. You can't meet all the people, but you can do a market segmentation and create a persona within the segments you want to tackle. Use a pen and paper and draw that person. Pay attention to the age, education, clothes, haircut, glasses, interests and as many personal details you can think of. Now, take a look at the person on the paper and figure out which one of your friends reminds you the most of it. It is important to visualize somebody you know personally because this will help you understand how does the customer think and what does he want. Better yet, you could do an A/B test with that person, before you roll-out your design. Get to know your customers Buyers perspective When you introduce your design, almost every person goes through this steps: Notices the package Asks "what is it?" Wonders "Why should I care?" Wants to be persuaded Needs proof [1] Make sure your design answers these questions. Especially number 3. Communicating their benefit should be your top priority. Your offer and x-factor Communication via design can be very effective. No wonder there's a whole industry behind the design business. It gives you a way to win people's hearts only by using words and colors (if used with care and attention). Buying decisions often come from our subconscious, emotions and gut feeling. And brand is all about the gut feeling. If the buyer feels positive about your product, you've done the job right. And positive feelings will appear if you solve their problems and improve their lives. This is what your design should say loud and clear. Here is one way to do it… Word game I like to play a word game where I lay down five categories, and assign feelings and states in which I want my clients to be in after interaction with the design. Culture / What is the company culture? e.g. Responsible, professional, informal, friendly, competent… 2. Voice / What tone and voice do we want to have? e.g. Personal, charming, trustworthy, caring… 3. Benefit / How will the client feel about the benefits we provide? e.g. At ease, relaxed, confident… 4. Value / What value do we provide to the client? e.g. Effortless, worry-free service, reliability… 5. X-Factor / What makes you an unmatched service compared to the competition? e.g. Available 24/7, organic, eco-friendly, traditional… It takes some time to think of the words you want to describe your design. But as you go, pile them up and identify similar groups. Then extract three or four keywords that you think are the most important. Those words best describe your product and emotions you want your customers to feel. In the end, it the design doesn’t speak those keywords, go back to the drawing board. Having done all these steps, designing will be much easier, since you know who are you designing it for, and how do you want your customers to feel. This makes it so much easier. And this is the power of user-centered design — chances to hit the sweet spot with your clients is much higher. Spend your 80 cents of a dollar for the preparation, and it will certainly pay off in the long run. Extra points Use emotional triggers of colors in your design to gain some extra points. Reading material Here are some books I would highly recommend, regarding the product design and the psychology of design. [1] — , Marty Neumeier The Brand Gap: How to Bridge the Distance Between Business Strategy and Design [2] — , David Ogilvy Confessions of an Advertising Man [3] — , Donald Norman The Design of Everyday Things

User-centered design principle works like magic— i.e.

How I Created a Social Recommendation Network in Brazil

Artificial intelligence (AI), once a sci-fi dream, is now an everyday reality as an indispensable tool with a growing influence on our creativity, problem-solving, and productivity. Yet, depending on who you ask, AI is either a revolutionary force set to redefine everything or a passing fad, soon to be dismissed. The truth, as with most transformative technologies, lies somewhere in between. Much like the dawn of the internet or the rise of cryptocurrency, AI is shrouded in an aura of mystery. This sense of the unknown is often amplified by those who profit from the confusion, and, as with any new frontier, it attracts its share of opportunists looking to exploit the hype. Advanced technology, especially one as powerful as AI, can feel like magic. But it’s not. Behind its impressive facade are structured algorithms, massive data processing, and immense computational power, all working together to achieve feats that can seem beyond our comprehension. If you were to step back in time a century ago with a smartphone in your pocket, the device we now take for granted would be nothing short of a marvel to those in the past. It could capture photos, communicate across vast distances instantly, and access the world’s knowledge at the touch of a button. To them, you’re not just a traveler from the future, they might see you as a wizard, wielding a power that borders on the divine. But in reality, there’s no sorcery at play. The smartphone is simply the result of years of innovation, combining countless technologies into a single, powerful tool. This is how we should view AI—not as something mystical but as an incredibly potent tool that we can learn, master, and use to create extraordinary outcomes. Like every emerging technology with transformative impact and wide-ranging consequences, AI demands more than learning how to work on new software. Mastering AI requires changing the way we think, developing new skills, and exploring which AI tools will help you achieve your objectives. That’s why I structured these articles into three parts, each focused on a critical area: Mindset, Skillset, and Toolset. Each part is designed to equip you with the insights and strategies you need to unlock AI’s full creative potential. Mindset: Clarity and Purpose as the Foundation Purpose as Your Guiding Star (The Why) Every journey begins with a clear destination in mind. When it comes to mastering AI, your purpose is that destination. It’s essential to start by identifying why you’re engaging with AI in the first place. Is your purpose to become more productive, generate creative ideas, or tackle complex problems? Understanding this foundational “why” will keep your efforts aligned and focused. If your purpose is to generate more creative ideas, your engagement with AI will be driven by exploration and innovation. On the other hand, if your purpose is to improve efficiency, your focus will be on using AI tools to streamline workflows and automate routine tasks. To maintain a steady stream of high-quality content without burning out, a content marketer can leverage AI to brainstorm topics, create outlines, and refine drafts, ensuring she stays productive and creative. Similarly, a project manager might focus on enhancing team productivity. His purpose could lead him to use AI to automate task assignments and track progress more efficiently. Key Takeaway: Your purpose is the lens through which all AI-driven efforts should be viewed. A clear purpose ensures every action taken with AI is aligned with your broader goals. Your Objectives Shape the Path Forward (The What) Once your purpose is clear, it’s time to translate it into specific, actionable objectives. Objectives are the tangible “what” that guides your AI interactions and ensures you’re moving in the right direction. Think of objectives as milestones on a map. Without them, you may wander off course, but with clear objectives, your AI journey becomes a focused, purposeful expedition. A software development team leader’s quest to reduce bug resolution times can translate into a clear and measurable objective: “to deploy an AI-powered code review tool that identifies potential bugs before code is merged.” Setting a clear and measurable objective ensures her team’s use of AI is directly tied to improving efficiency and product quality. Key Takeaway: Clear and specific objectives make it easier to measure success and adjust your approach as needed. They provide a roadmap for using AI to achieve meaningful results. Breaking Free from Limiting Beliefs (The How) Defining your purpose and clear objectives will set you on the right path, but your approach, or the “how,” determines how effectively you can overcome challenges and fully utilize AI’s capabilities. Depending on your mindset, the way you approach AI can either propel you forward or hold you back. Using the “Growth Mindset” framework, where challenges are opportunities, not obstacles, a graphic designer who initially viewed AI as a threat to his creativity might shift his mindset to start seeing AI as a tool to enhance his creative process. By automating repetitive tasks and generating initial design concepts, he can free up more time to focus on truly innovative work and have a chance to learn and grow, rather than something intimidating or restrictive. Applying this mindset to AI allows you to view each interaction with the technology. Key Takeaway: Whether you’re aiming to be more creative, efficient, or effective, the mindset you adopt will directly impact how effectively you can leverage AI. An open, exploratory approach is key to making the most of AI’s capabilities. Now that you’ve laid the foundation with the right mindset, it’s time to move forward to the next critical step: building the skillset necessary for AI mastery. In the next part of this series, we’ll dive into the essential skills you need to structure your approach, simplify complex tasks, communicate with precision, and embrace iterative refinement. Stay tuned for Part 2: Developing the Skillset for AI Mastery. Artificial intelligence (AI), once a sci-fi dream, is now an everyday reality as an indispensable tool with a growing influence on our creativity, problem-solving, and productivity. Yet, depending on who you ask, AI is either a revolutionary force set to redefine everything or a passing fad, soon to be dismissed. The truth, as with most transformative technologies, lies somewhere in between. Much like the dawn of the internet or the rise of cryptocurrency, AI is shrouded in an aura of mystery. This sense of the unknown is often amplified by those who profit from the confusion, and, as with any new frontier, it attracts its share of opportunists looking to exploit the hype. Advanced technology, especially one as powerful as AI, can feel like magic. But it’s not. Behind its impressive facade are structured algorithms, massive data processing, and immense computational power, all working together to achieve feats that can seem beyond our comprehension. If you were to step back in time a century ago with a smartphone in your pocket, the device we now take for granted would be nothing short of a marvel to those in the past. It could capture photos, communicate across vast distances instantly, and access the world’s knowledge at the touch of a button. To them, you’re not just a traveler from the future, they might see you as a wizard, wielding a power that borders on the divine. But in reality, there’s no sorcery at play. The smartphone is simply the result of years of innovation, combining countless technologies into a single, powerful tool. This is how we should view AI—not as something mystical but as an incredibly potent tool that we can learn, master, and use to create extraordinary outcomes. Like every emerging technology with transformative impact and wide-ranging consequences, AI demands more than learning how to work on new software. Mastering AI requires changing the way we think, developing new skills, and exploring which AI tools will help you achieve your objectives. That’s why I structured these articles into three parts, each focused on a critical area: Mindset, Skillset, and Toolset. Each part is designed to equip you with the insights and strategies you need to unlock AI’s full creative potential. Mindset: Clarity and Purpose as the Foundation Mindset: Clarity and Purpose as the Foundation Purpose as Your Guiding Star (The Why) Purpose as Your Guiding Star (The Why) Every journey begins with a clear destination in mind. When it comes to mastering AI, your purpose is that destination. It’s essential to start by identifying why you’re engaging with AI in the first place. Is your purpose to become more productive, generate creative ideas, or tackle complex problems? Understanding this foundational “why” will keep your efforts aligned and focused. If your purpose is to generate more creative ideas, your engagement with AI will be driven by exploration and innovation. On the other hand, if your purpose is to improve efficiency, your focus will be on using AI tools to streamline workflows and automate routine tasks. To maintain a steady stream of high-quality content without burning out, a content marketer can leverage AI to brainstorm topics, create outlines, and refine drafts, ensuring she stays productive and creative. Similarly, a project manager might focus on enhancing team productivity. His purpose could lead him to use AI to automate task assignments and track progress more efficiently. Key Takeaway: Your purpose is the lens through which all AI-driven efforts should be viewed. A clear purpose ensures every action taken with AI is aligned with your broader goals. Your Objectives Shape the Path Forward (The What) Your Objectives Shape the Path Forward (The What) Once your purpose is clear, it’s time to translate it into specific, actionable objectives. Objectives are the tangible “what” that guides your AI interactions and ensures you’re moving in the right direction. Think of objectives as milestones on a map. Without them, you may wander off course, but with clear objectives, your AI journey becomes a focused, purposeful expedition. A software development team leader’s quest to reduce bug resolution times can translate into a clear and measurable objective: “to deploy an AI-powered code review tool that identifies potential bugs before code is merged.” Setting a clear and measurable objective ensures her team’s use of AI is directly tied to improving efficiency and product quality. “to deploy an AI-powered code review tool that identifies potential bugs before code is merged.” Key Takeaway: Clear and specific objectives make it easier to measure success and adjust your approach as needed. They provide a roadmap for using AI to achieve meaningful results. Breaking Free from Limiting Beliefs (The How) Breaking Free from Limiting Beliefs (The How) Defining your purpose and clear objectives will set you on the right path, but your approach, or the “how,” determines how effectively you can overcome challenges and fully utilize AI’s capabilities. Depending on your mindset, the way you approach AI can either propel you forward or hold you back. Using the “Growth Mindset” framework, where challenges are opportunities, not obstacles, a graphic designer who initially viewed AI as a threat to his creativity might shift his mindset to start seeing AI as a tool to enhance his creative process. By automating repetitive tasks and generating initial design concepts, he can free up more time to focus on truly innovative work and have a chance to learn and grow, rather than something intimidating or restrictive. Applying this mindset to AI allows you to view each interaction with the technology. Key Takeaway: Whether you’re aiming to be more creative, efficient, or effective, the mindset you adopt will directly impact how effectively you can leverage AI. An open, exploratory approach is key to making the most of AI’s capabilities. Now that you’ve laid the foundation with the right mindset, it’s time to move forward to the next critical step: building the skillset necessary for AI mastery. In the next part of this series, we’ll dive into the essential skills you need to structure your approach, simplify complex tasks, communicate with precision, and embrace iterative refinement. Stay tuned for Part 2: Developing the Skillset for AI Mastery.

The Dark Art of Unlocking AI’s Full Creative Potential - Part 1/3 

The Top AI Tools for Web Development

How To Study Source Code: Object Prototypes and Mixins in Express.js

সোর্স কোড কীভাবে অধ্যয়ন করবেন: Express.js-এ অবজেক্ট প্রোটোটাইপ এবং মিক্সিন

So studieren Sie Quellcode: Objektprototypen und Mixins in Express.js

Cómo estudiar el código fuente: prototipos de objetos y mixins en Express.js

Comment étudier le code source : prototypes d'objets et mixins dans Express.js

स्रोत कोड का अध्ययन कैसे करें: Express.js में ऑब्जेक्ट प्रोटोटाइप और मिक्सिन

ソースコードの学習方法: Express.js のオブジェクト プロトタイプとミックスイン

소스 코드 공부 방법: Express.js의 객체 프로토타입과 믹스인

Como estudar código-fonte: protótipos de objetos e mixins em Express.js

Как изучать исходный код: прототипы объектов и миксины в Express.js

Kaynak Kod Nasıl Çalışılır: Express.js'de Nesne Prototipleri ve Mixinler

Cách nghiên cứu mã nguồn: Nguyên mẫu đối tượng và Mixin trong Express.js

如何研究源代码：Express.js 中的对象原型和 Mixins

I needed some data for my side project. Google helped me find only outdated CSVs, expensive closed sources, and APIs hidden behind the “contact sales” button. Wikipedia was ranking high in my searches, but I quickly found that the tables are as unstructured as the ones you typically create in Word or Google Docs. There was no consistency in columns and naming conventions. This at first discouraged me but after failing to find another data source, I gave it a go. And I ended up using it for my project. Let me show you how to quickly load Wikipedia data for your data analysis. I was astonished at how easy it was. Big thanks to all Wikipedia contributors doing the hard work. Method 1: Loading Tables in Google Sheets This one feels like magic. Use this formula in one cell, and it will expand the entire table scraped from Wikipedia. =importHTML(WIKIPEDIA_URL, "table", NUMBER_OF_TABLE_ON_THE_PAGE); Let’s say you want to fetch a list of all EU countries with their population count, GDP, language, and other essential data. There’s a Wikipedia page containing all of this information and citing sources of the information(!): https://en.wikipedia.org/wiki/Member_state_of_the_European_Union. The table containing this info is the second table on the page (the first one is the little summary table). The formula will look like: =importHTML("https://en.wikipedia.org/wiki/Member_state_of_the_European_Union", "table", 2); After running it, you’ll get all of this data in your Google Sheets, ready to analyze or export. Google Sheets can also read lists! Full docs here. Method 2: Pandas and Python I was surprised to find out that the most popular library for data analysis for Python has a feature equivalent to Google Docs. import pandas as pd

url = "https://en.wikipedia.org/wiki/Member_state_of_the_European_Union"

dfs = pd.read_html(url)

df = dfs[1] read_html function from pandas takes the site URL as an input and fetches all tables to a list of data frames. Again, the second one is what we are looking for. And it’s ready for further analysis! Since Google Docs was not something I intended to stick with, I used this method in addition to method 3 to gather data from several pages. Full documentation here. Method 3: Python and Beautiful Soup Well, Beautiful Soup is a go-to library for parsing HTML in the Python ecosystem. I looked for easy solutions, but I needed to fall back on them in the end. The reason was I needed to get not only data from tables but also from headings that preceded them. Instead of writing the code from scratch, I recommend using ChatGPT and refining its output. I basically pasted a URL, said what I needed to scrape, and got the script without giving any guidance. If you wanted to do something similar to me - getting all tables from a page and data from multiple levels of headings preceding them - you would do something like this. import requests
from bs4 import BeautifulSoup

response = requests.get(url)
soup = BeautifulSoup(response.content, 'html.parser')
tables = soup.find_all('table', {'class': 'wikitable'})

all_airports = []

for table in tables:
  continent = table.find_previous('h2').find('span', class_='mw-headline').text
  region = table.find_previous('h3').find('span', class_='mw-headline').text
  country = table.find_previous('h4').find('span', class_='mw-headline').text
  
  rows = table.find_all('tr')
        
  for row in rows[1:]:
      cols = row.find_all('td')
      
      # here push the data wherever you need It looks pretty neat, doesn’t it? I was worried that scraping “by hand” would be a pain but actually, Wikipedia usually has a pretty clear, predictable HTML structure. It varies from page to page but adheres to the same patterns. This method in addition to using pandas helped me get all the data I needed. Summary As you can see, getting data for your analyses or side projects can be nice and easy. There is a catch though - there are no data consistency mechanisms in tables inside Wikipedia pages. This means that after using any of the above methods, you’ll likely need to do some cleaning and check for mismatches in naming conventions. Some regular expressions will come in handy as well. At times, I am cynical about technological progress but discovering that information access can be so straightforward makes me feel that I can really stand on the shoulders of giants when building on my own. Subscribe to my profile by filling in your email address on the left, and be up-to-date with my articles! I will soon be releasing a crazy interesting project that I needed data from Wikipedia! Don't forget to follow me on Twitter @ horosin, and subscribe to my blog’s newsletterfor more tips and insights! If you don't have Twitter, you can also follow me on LinkedIn. I needed some data for my side project. Google helped me find only outdated CSVs, expensive closed sources, and APIs hidden behind the “contact sales” button. Wikipedia was ranking high in my searches, but I quickly found that the tables are as unstructured as the ones you typically create in Word or Google Docs. There was no consistency in columns and naming conventions. This at first discouraged me but after failing to find another data source, I gave it a go. And I ended up using it for my project. Let me show you how to quickly load Wikipedia data for your data analysis. I was astonished at how easy it was. Big thanks to all Wikipedia contributors doing the hard work. Method 1: Loading Tables in Google Sheets This one feels like magic. Use this formula in one cell, and it will expand the entire table scraped from Wikipedia. =importHTML(WIKIPEDIA_URL, "table", NUMBER_OF_TABLE_ON_THE_PAGE); =importHTML(WIKIPEDIA_URL, "table", NUMBER_OF_TABLE_ON_THE_PAGE); Let’s say you want to fetch a list of all EU countries with their population count, GDP, language, and other essential data. There’s a Wikipedia page containing all of this information and citing sources of the information(!): https://en.wikipedia.org/wiki/Member_state_of_the_European_Union . https://en.wikipedia.org/wiki/Member_state_of_the_European_Union The table containing this info is the second table on the page (the first one is the little summary table). The formula will look like: second table on the page =importHTML("https://en.wikipedia.org/wiki/Member_state_of_the_European_Union", "table", 2); =importHTML("https://en.wikipedia.org/wiki/Member_state_of_the_European_Union", "table", 2); After running it, you’ll get all of this data in your Google Sheets, ready to analyze or export. Google Sheets can also read lists! Full docs here . here Method 2: Pandas and Python I was surprised to find out that the most popular library for data analysis for Python has a feature equivalent to Google Docs. import pandas as pd

url = "https://en.wikipedia.org/wiki/Member_state_of_the_European_Union"

dfs = pd.read_html(url)

df = dfs[1] import pandas as pd

url = "https://en.wikipedia.org/wiki/Member_state_of_the_European_Union"

dfs = pd.read_html(url)

df = dfs[1] read_html function from pandas takes the site URL as an input and fetches all tables to a list of data frames. Again, the second one is what we are looking for. And it’s ready for further analysis! read_html Since Google Docs was not something I intended to stick with, I used this method in addition to method 3 to gather data from several pages. Full documentation here . here Method 3: Python and Beautiful Soup Well, Beautiful Soup is a go-to library for parsing HTML in the Python ecosystem. I looked for easy solutions, but I needed to fall back on them in the end. The reason was I needed to get not only data from tables but also from headings that preceded them. Instead of writing the code from scratch, I recommend using ChatGPT and refining its output. I basically pasted a URL, said what I needed to scrape, and got the script without giving any guidance. If you wanted to do something similar to me - getting all tables from a page and data from multiple levels of headings preceding them - you would do something like this. import requests
from bs4 import BeautifulSoup

response = requests.get(url)
soup = BeautifulSoup(response.content, 'html.parser')
tables = soup.find_all('table', {'class': 'wikitable'})

all_airports = []

for table in tables:
  continent = table.find_previous('h2').find('span', class_='mw-headline').text
  region = table.find_previous('h3').find('span', class_='mw-headline').text
  country = table.find_previous('h4').find('span', class_='mw-headline').text
  
  rows = table.find_all('tr')
        
  for row in rows[1:]:
      cols = row.find_all('td')
      
      # here push the data wherever you need import requests
from bs4 import BeautifulSoup

response = requests.get(url)
soup = BeautifulSoup(response.content, 'html.parser')
tables = soup.find_all('table', {'class': 'wikitable'})

all_airports = []

for table in tables:
  continent = table.find_previous('h2').find('span', class_='mw-headline').text
  region = table.find_previous('h3').find('span', class_='mw-headline').text
  country = table.find_previous('h4').find('span', class_='mw-headline').text
  
  rows = table.find_all('tr')
        
  for row in rows[1:]:
      cols = row.find_all('td')
      
      # here push the data wherever you need It looks pretty neat, doesn’t it? I was worried that scraping “by hand” would be a pain but actually, Wikipedia usually has a pretty clear, predictable HTML structure. It varies from page to page but adheres to the same patterns. This method in addition to using pandas helped me get all the data I needed. Summary As you can see, getting data for your analyses or side projects can be nice and easy. There is a catch though - there are no data consistency mechanisms in tables inside Wikipedia pages. This means that after using any of the above methods, you’ll likely need to do some cleaning and check for mismatches in naming conventions. Some regular expressions will come in handy as well. At times, I am cynical about technological progress but discovering that information access can be so straightforward makes me feel that I can really stand on the shoulders of giants when building on my own. Subscribe to my profile by filling in your email address on the left, and be up-to-date with my articles! I will soon be releasing a crazy interesting project that I needed data from Wikipedia! Don't forget to follow me on Twitter @ horosin , and subscribe to my blog’s newsletter for more tips and insights! @ horosin newsletter If you don't have Twitter, you can also follow me on LinkedIn . LinkedIn

How to Scrape Data Off Wikipedia: Three Ways (No Code and Code)

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