DEV Community: Shariq Ahmed

Danfo js — An Alternative to Pandas

Shariq Ahmed — Thu, 02 Jan 2025 12:54:49 +0000

JavaScript has become one of the most versatile programming languages, and with libraries like Danfo.js, it’s even more powerful for data science tasks. If you’re new to data manipulation in JavaScript, this guide will introduce you to Danfo.js and help you get started with handling data efficiently.

What is Danfo.js?

Danfo.js is a powerful library built on top of JavaScript that enables users to perform data manipulation and analysis, similar to what Python’s Pandas library does. It is designed to work with DataFrames and Series, which are the two primary data structures that allow you to manage data in a tabular format. If you’ve worked with spreadsheets or databases before, you’ll find these concepts familiar.

Why Danfo.js?

JavaScript for Data Science: If you’re already familiar with JavaScript but want to dive into data manipulation, Danfo.js is an excellent tool. It combines the power of JavaScript with the flexibility of data analysis.
Easy to Learn: If you’re a beginner, Danfo.js is simple to pick up, especially if you are comfortable with JavaScript. It allows you to carry out tasks like filtering, grouping, and transforming data with ease.
Integration with Web Apps: Danfo.js allows you to seamlessly work with data in web apps. You can fetch data from APIs or handle local datasets directly in your browser.

Installing Danfo.js

To get started with Danfo.js, you’ll need to install it. You can install Danfo.js using npm (Node Package Manager) in your project directory.

npm install danfojs-node

For working in the browser, you can include Danfo.js from a CDN:

<script src="https://cdn.jsdelivr.net/npm/danfojs@0.5.0/dist/index.min.js"></script>

Working with DataFrames

A DataFrame is a two-dimensional, size-mutable, and potentially heterogeneous tabular data structure. It’s similar to a table in a database or an Excel sheet.

Here’s a basic example of creating a DataFrame in Danfo.js:

const dfd = require("danfojs-node"); const data = {
    "Name": ["Alice", "Bob", "Charlie"],
    "Age": [25, 30, 35],
    "Country": ["USA", "UK", "Canada"]
}; const df = new dfd.DataFrame(data);
df.print();

This will output:

Name  Age  Country
0  Alice   25      USA
1    Bob   30       UK
2 Charlie   35   Canada

Common Operations in Danfo.js

Here are some of the most common data manipulation tasks you’ll perform using Danfo.js:

1. Selecting Columns

You can select a specific column from the DataFrame like this:

const ageColumn = df["Age"];
ageColumn.print();

2. Filtering Rows

To filter rows based on a condition:

const adults = df.query(df['Age'].gt(30)); // Filters rows where age > 30
adults.print();

3. Adding New Columns

You can easily add a new column based on existing columns:

df.addColumn("IsAdult", df["Age"].gt(18)); // Adds a column based on age
df.print();

4. Handling Missing Data

Danfo.js provides various functions to handle missing values:

df.fillna(0, {inplace: true}); // Replace NaN values with 0

Working with Series

A Series in Danfo.js is a one-dimensional array-like object. It can be thought of as a single column of a DataFrame.

Here’s how you can create and manipulate a Series:

const ageSeries = new dfd.Series([25, 30, 35]);
ageSeries.print();

You can also perform operations on Series:

const doubledAge = ageSeries.mul(2);
doubledAge.print();

Visualizing Data

While Danfo.js itself does not focus on visualization, you can easily integrate it with libraries like Plotly or Chart.js for visualizing your data. After processing your data in Danfo.js, you can pass it to a visualization library to generate charts and graphs.

The type of visualization depends on the kind of data and the message you want to convey. Below are some common visualizations for different types of data:

Bar Chart

Use case: Comparing different categories or groups.
When to use: When you have categorical data and you want to compare values across different categories.

const plotly = require('plotly.js-dist');
const data = [{
    x: ['A', 'B', 'C', 'D'],
    y: [20, 14, 23, 17],
    type: 'bar'
}];
plotly.newPlot('chart', data);

Line Chart

Use case: Visualizing trends over time or continuous data.
When to use: To show how a value changes over time (time series data) or continuous data.

const data = [{
    x: ['2021', '2022', '2023'],
    y: [100, 150, 130],
    type: 'scatter',
    mode: 'lines'
}];
plotly.newPlot('chart', data);

Pie Chart

Use case: Showing proportions of a whole.

When to use: When you want to show how parts make up a whole or to compare relative proportions of categories.

const data = [{    labels: ['A', 'B', 'C', 'D'],
    values: [20, 14, 23, 17],
    type: 'pie'
}];
plotly.newPlot('chart', data);

Scatter Plot

**Use case: **Showing relationships between two continuous variables.
When to use: To visualize correlations or relationships between two numeric variables.

const data = [{
    x: [1, 2, 3, 4, 5],
    y: [10, 11, 12, 13, 14],
    type: 'scatter',
    mode: 'markers'
}];
plotly.newPlot('chart', data);

Heatmap

Use case: Visualizing matrix data or the intensity of values across two dimensions.
**When to use: **To show patterns in data that change in intensity, like correlation matrices, or geographical heatmaps.

const data = [{
    z: [[1, 20, 30], [20, 1, 60], [50, 60, 1]],
    type: 'heatmap'
}];
plotly.newPlot('chart', data);

Box Plot

Use case: Understanding the distribution of a dataset.
When to use: When you want to visualize the distribution of data, including the median, quartiles, and potential outliers.

const data = [{    y: [10, 15, 23, 30, 32, 43],
    type: 'box'
}];
plotly.newPlot('chart', data);

All in all, danfo.js is a powerful library that brings the capabilities of data manipulation and analysis to JavaScript, making it an ideal choice for those who are already familiar with JavaScript and want to dive into data science tasks.

GitHub Copilot is Now Free for Everyone in VS Code!

Shariq Ahmed — Thu, 19 Dec 2024 10:05:41 +0000

Good news: GitHub has announced that GitHub Copilot is now free for everyone. This means that you can use the previously paid subscription service without any cost in Visual Studio Code (VS Code). Why this move? We don’t know. But for the people who don’t know what GitHub Copilot actually is, here’s the basic introduction for you.

What Is GitHub Copilot?

GitHub Copilot, powered by OpenAI’s advanced models, is an AI-driven coding assistant that helps developers write code faster by providing smart suggestions. It can autocomplete lines of code, suggest entire functions, and even help write documentation based on the context of your project.

What Can You Get In This Free Access?

Well, in this free access, you can get 2,000 code completions and 50 chat messages per month.

How to Start Using GitHub Copilot for Free in VS Code

Install VS Code: If you haven’t already, download and install the latest version of Visual Studio Code from here.
Install the GitHub Copilot Extension: Open VS Code, go to the Extensions Marketplace, and search for “GitHub Copilot.” Install the extension to integrate Copilot with your coding environment.
Sign In to GitHub: After installing the extension, you’ll be prompted to sign in with your GitHub account. Once signed in, you’ll have full access to GitHub Copilot’s features for free.
Start Coding: As you write your code, GitHub Copilot will offer real-time suggestions to help you speed up development and improve code quality.

The Benefits of Free GitHub Copilot in VS Code

Access to AI-Powered Assistance: With Copilot now free, developers of all experience levels can use AI to assist with code completion, bug fixes, and even creative solutions to coding challenges.
Boosted Productivity: Copilot helps automate repetitive coding tasks, which means developers can focus on solving unique problems, improving their code, and learning new languages and frameworks.
No More Cost Barriers: Whether you’re a student, a freelancer, or a hobbyist, you no longer have to pay for access to powerful AI assistance. This is because, now it’s free for everyone.
Better Code Quality: Copilot’s suggestions help write clean, optimized, and error-free code. This is pretty helpful for both beginners and experienced developers in adhering to best practices.

Stay tuned! I will be creating some amazing projects using GitHub Copilot, and you don’t want to miss out on the incredible possibilities it offers for speeding up development and improving your coding experience.

Cursor AI — The Good, The Bad, The Ugly!

Shariq Ahmed — Wed, 27 Nov 2024 12:16:38 +0000

There used to be days when people would work on their programming skills. Ah — the golden era! With the increase of AI softwares, it seems like almost everyone is everything. Don’t get me wrong. But it feels like people are relying too much on AI that they have stopped working on their problem solving skills. Just recently, I got to know about a coding competition that was held by one of the most prestigious universities. The catch? Almost half of the participants (whom I know) used AI. And yes, Cursor did a really good job at solving it. ChatGPT? Well, it fell flat! I wasn’t even able to understand the logic.

However, this reliance on AI raises an important question: Are we losing the essence of what it means to be a programmer? The thrill of tackling a complex problem, the satisfaction of debugging a stubborn piece of code, and the joy of finally seeing your project come to life are experiences that cannot be replicated by any AI tool.

While AI can assist in generating code snippets or providing suggestions, it lacks the creativity and intuition that human programmers bring to the table. The ability to think critically, adapt to new challenges, and innovate is what sets skilled developers apart.

Moreover, the educational aspect of programming is being overshadowed. Students and aspiring developers might miss out on fundamental concepts and problem-solving techniques if they rely solely on AI for solutions. It’s essential to strike a balance between leveraging AI as a tool and honing our own skills.

In the end, AI should be viewed as a complement to our abilities, not a replacement. Embracing both worlds can lead to a new era of programming where human ingenuity and artificial intelligence work hand in hand, pushing the boundaries of what we can achieve in technology.

Basics of Pandas (Python Library)

Shariq Ahmed — Wed, 13 Nov 2024 06:01:46 +0000

Pandas is a Python library that provides data structures and functions needed to work with structured data seamlessly. It is built on top of NumPy and is great for data manipulation and analysis.

Installing Pandas

Before using Pandas, you need to install it. You can do this using pip. Open your command line or terminal and run:

pip install pandas

Importing Pandas

Once installed, you can import Pandas in your Python script or Jupyter Notebook:

import pandas as pd

Key Data Structures in Pandas

Pandas mainly has two data structures:

Series:

A one-dimensional labeled array capable of holding any data type.

DataFrame:

A two-dimensional labeled data structure with columns of potentially different types.

Creating a Series

You can create a Pandas Series from a list, dictionary, or NumPy array.

import pandas as pd data_list = [1, 2, 3, 4] series_from_list = pd.Series(data_list) print(series_from_list)

# From a dictionary data_dict = {'a': 1, 'b': 2, 'c': 3} series_from_dict = pd.Series(data_dict) print(series_from_dict)

Creating a DataFrame

A DataFrame can be created from a variety of data structures.

From a dictionary of lists

data = { 'Name': ['Alice', 'Bob', 'Charlie'], 'Age': [25, 30, 35], 'City': ['New York', 'Los Angeles', 'Chicago'] } df = pd.DataFrame(data) print(df)

Basic DataFrame Operations

Viewing Data
You can view the first few rows of a DataFrame using the head() method:

print(df.head())

Accessing Columns

You can access a column by its name:

print(df['Name'])

Accessing Rows

You can access rows by index using the iloc[] method:

print(df.iloc[0]) # First row print(df.iloc[1:3]) # Rows from index 1 to 2

Adding a New Column

You can add a new column to the DataFrame:

df['Salary'] = [50000, 60000, 70000] print(df)

Filtering Data

You can filter data based on a condition:

# Filter rows where Age is greater than 28 filtered_df = df[df['Age'] > 28] print(filtered_df)

5 Things That You Should Consider Before Learning Programming

Shariq Ahmed — Thu, 01 Aug 2024 10:02:01 +0000

Let’s face it. You can’t resist getting into coding when people are using it to make a fortune. It’s literally irresistible. But before you jump on this bandwagon, it’s important to keep certain things in mind. Let’s see them one by one:

Doing Programming is Easy, But Mastering It is Difficult

If you think you can code, you are absolutely right! Everyone can code. But if you think mastering it is a piece of cake, you are wrong. It takes grit and perseverance to be good at it. You need to constantly improve your coding skills. You can never stop learning if you want to become a master at coding. Technology isn’t developing every day, but there is a sea of information that you need to consume to get better at it.

Have a Solid Why

As mentioned before, coding is easy, but mastering it is another story. You need to have a solid “why”! Why do you want to code? Why can’t you survive (yes, you read that right) without coding? This is important because coding can be boring at times. But if you know why you are coding, your passion will push you to your limits and help you improve your skills. Without a strong “why,” you will tire soon!

Don’t Learn Every Language

Avoid becoming a jack of all trades, master of none. Pick one language and stick to it. Don’t veer away in different directions, as it can make you lose focus. Your concentration will be all over the place. You won’t excel at any language. Instead, you’ll remain at a beginner or intermediate level in all languages. This is not a good approach.

Be Hungry for Information

This doesn’t mean you should learn every single programming language. You need to become an expert in a few languages. But don’t be oblivious to the technological world. Seek information and always crave it. It will significantly help you.

Learn. Experiment. Repeat.

Don’t just learn. Use and experiment with what you’ve learned. This will help you succeed significantly. When you build something, sometimes it doesn’t go well. You need to continuously learn and do trial and error to see what works and what doesn’t. Seek feedback from others.

Don’t Think That You Can Never Be Good at Coding

Your mind is powerful. Don’t feed it negative thoughts. If everyone else around you is succeeding, then you can succeed as well. No one is born a genius (there are exceptions, though). Success will also be in your bag if you put 110% into coding.

Latest React Tips and Tricks in 2024

Shariq Ahmed — Wed, 31 Jul 2024 03:55:42 +0000

React is one of the fastest-growing frameworks for many reasons. Moreover, it’s popular because of its virtual DOM, which makes it really fast. However, for beginners, React can be quite challenging. I also took my sweet time to get good at React. In this article, I will share tips and tricks that are already available on the internet. This article just summarizes those tricks.

Let’s start without further ado.

1. Master the Basics

Get yourself bogged down in fundamentals. Become an expert in it. This is because when the base is strong you can understand even the complex topics easily. Some of the components that you need to master include components, state, props and JSX. And yes, do understand what componentDIdMount — used when a component is mounted to the screen -and componentDiDUpdate — a component that updates the DOM with state changes — are.

2. Learn Next.js After React

This is because whenever you are using a network request in React, then rather than using fetch or Axios, use react-query. Why? It doesn’t only have built-in state managers and a global cache but also a loading state. It can also re-fetch — no matter the component.

Further, if you are using the setState function, then in order to avoid passing extra dependency inside the dependency, use a state value like setState(prev=> //. Again, why? Well, it avoids race conditions.

3. Console.log

In an arrow function, if you want to log out a component or something else, use console.log(/* something */) || after the arrow. Benefits? You will see the results (logged out) without rewriting the function just to make use of the code.

If you want to edit dialogs/modals then instead of going through any laborious process, just add a ‘key’ prop to a dynamic model. It will mention the fresh state of dynamic items.

4. Simple Components

Never ever overcomplicate the component. Keep it simple. Keep the components for just one task. You will understand them easily. Moreover, in order to better the performance and get rid of unnecessary re-renders, use pure components. They only re-render when the state updates. Further, use prototypes — this assists in picking errors in the early stage.

5. Use Hooks

You ain’t gonna regret it. But why? Well, hooks help developers not only in using state but also other React features in functional components. It also prevents overcomplicating the code.

Now, testing React is another area where beginners face difficulties. So here are two testing frameworks that you can use: Jest and Enzyme.

Go for Jest if you are okay with using APIs to write test cases. If you want to test your React’s components function then use Enzyme. Further, it gives a simple API that also helps in rendering and interacting with components.

How to Self-Learn Coding?

Shariq Ahmed — Mon, 29 Jul 2024 03:24:09 +0000

Virtual meetings. Smartphones. Elsa Speaks. I’m sure our ancestors would have gasped if someone told them that using these technologies would be an everyday thing in the future. They would have thought this was just a snowball’s chance in hell. Maybe they would have fainted if I told them that in the years to come, they wouldn’t lose their jobs because of their indolence, but rather because of AI.

But hey, I’m sure that you know how to prevent all this from happening. Well, by learning to code. But let’s face it: not everyone has the money to get a computer science degree under their belt. Some people have no money in their pockets. So, they take a different path — they self-learn coding.

According to HackerRank’s survey, 27.4% of the programmers are self-taught, and well I’m also a self-taught programmer. I have taught myself many programming languages and frameworks. Currently, I’m learning Flutter. However, I think I will take a long time to learn Flutter. My yearly goal is to learn it completely.

Some tips that helped me learn programming include:

Tip #1: Set a Goal

Before doing or learning anything, make sure you set a goal.

Setting goals is the first step in turning the invisible into the visible

— Tony Robbins

So, make sure you set a goal. Because when you want to learn something, then even if you have run out of motivation, you’ll learn to code. But set small goals. Don’t set lofty goals in the start like you’ll learn both Flutter and React in 3 months. That’s impossible. Sure, you should make ambitious goals, but don’t set unrealistic goals. Make goals that are achievable with some hard work.

Tip # 2: Start with Basics

Don’t try to learn C, when Python can do the job. Because the basics are almost the same in every language.

Remember, just like the first brick of the building, it is important. The basics of programming language are important. You must ensure that your basics are strong and at the back of your hand.

Personally, I found these two websites helpful.

w3schools
geeksforgeeks
freecodecamp

Tip # 3: Learn Important but Easy Language

I hinted at this before. Don’t learn C#. Heck, don’t even think of learning JavaScript. Learn Python first. It’s not only versatile but also one of the easiest programming languages. Experienced developers consider Python easiest after Ruby. Google, Spotify, Instagram, and Stripe all use Python.

Tip # 4: Get a Mentor

YouTube isn’t the only platform on which you should rely. You should ask for help from other experienced developers. The best thing is that you can find a mentor from many websites. Coding Coach is one of the websites that I came across a few days back. They have trained and experienced coders who are always willing to help you.

But if you don’t have money, make the internet your mentor. Ask ChatGPT. Learn from YouTube. Post your questions on Stack Overflow. You can also ask your questions on Quora.

Tip # 5: Start Doing LeetCode Questions

After 8 months of coding, start doing LeetCode questions. Now, you will find many people saying that you should first go straight to the solution. But hey, that’s not what you should do. In fact, I’d recommend that you select one problem and try to give your best shot. If it’s been an hour and you have no idea how to move forward, go to the solution.

Understand it completely, then revisit the question. Try to find some pattern in the answer. Now, go to LeetCode to find questions with that pattern, and boom.

That’s how you should solve questions. This technique may be time-consuming, but it’s rewarding.

Tip # 6: Build Something

There’s power in building projects. In the first month, try to build something easy like a tic-tac-toe game. In the second month, make a scientific calculator. In the third month, try to build a calculator. In short, every month build something that can challenge your coding knowledge. You can also ask ChatGPT to give you project ideas.

But, don’t fret about making that perfect project. Even if your project is like that pebble stack building pictured above, it's totally fine.

Pat on your back after every project. If you find yourself stuck somewhere, get help. But don’t stop. I repeat: Don’t ever stop. You’re gonna regret it later! In the next 30 years, coding will make and break your career.

Everything About GOLang — Go Language

Shariq Ahmed — Thu, 25 Jul 2024 08:34:54 +0000

Out of all programming languages, JavaScript is widely used. However, Python has been gaining popularity lately. Contrary to what’s assumed, Go is still in the top 10 most-loved languages according to the survey conducted by Stack Overflow. Some famous companies that use Go include Google, Kubernetes, Uber, Docker, PayPal, and Dropbox.

But who created it? Well, it was developed by three developers: Robert Griesemer, Rob Pike, and Ken Thompson. In 2007, they released it, but it was in 2009 that they launched it as an open-source programming language.

The main reason behind its creation was to address issues related to networked layers, scalability, performance, productivity, and concurrency. They were done with the complexity of C++. They wanted something that could address all the issues. That’s also why it was released to speed up the coding process and support multicore computing.

Moreover, Go is inspired by various programming languages. For instance, concurrency was inspired by Limbo and Newsqueak. Similarly, it has taken readability and usability from Python. But all these features aim at just one thing — stability in Go.

A little more information about Go:

It is a statically typed and compiled high-level language.
It is somewhat similar to C, but contrary to it, it has memory safety, garbage collection, structural typing, and CSP-style concurrency.
It was created because developers were frustrated with existing languages.
Go and Golang mean the same thing. Previously, the domain go.org wasn’t available, so it was launched as golang.org (a mix of Go and language).
It is a general-purpose language specifically for creating backends.

Moreover, since Go is an open-source project, it also provides access to various development tools. The IDEs that work well with it include GoLand, LiteIDE, and Zeus IDE. For editors, you can use VSCode.

So, you see it isn’t challenging to understand why Go is popular these days. But that’s not the only advantage of using Go. The fact that Go is really simple and has a small learning curve makes it one of the best languages to master. But, out of all, Go is best for backend engineers. Developers can address requests on the server side without using a lot of memory.

npm vs npx — What are the Basic Difference?

Shariq Ahmed — Mon, 15 Jul 2024 07:18:34 +0000

In Node.js, different packages are installed. But, of course, to manage all of them, you need something. That’s where npm comes in. Npm (node package manager) manages all the packages. In other words, it is a tool that is used to install packages. Further, it puts modules in place that Node.js can locate. It also manages dependency conflicts.

This means you only need to specify your project dependencies in the package.json file. If you need to install the dependencies, you just need to run npm install. It is also the dependency manager you get whenever you install Node.js. With the help of npm, developers can install packages.

But how do you install packages using npm?

Write the following:

npm install {your-package}

Check whether your package is installed by looking under ‘dependencies’ in the package.json file.

Just one piece of advice: Choose which npm packages you use wisely. They are developing quickly. In fact, always prefer to use npm packages that are not only the most stable and popular but also not retiring in the near future.

Npx or Node Package eXecute, on the other hand, is an NPM package runner that helps developers execute any package available on the NPM registry. It also deletes and updates JavaScript packages without the need to install them. It assists in making npm simpler as well as more powerful.

Moreover, for developers, in case they are using something that is heavily reliant on node.js when deploying to the cloud, it is often recommended to master npx.

What’s even better?

Thanks to npx, you can execute packages from the npm registry without needing to install them. Particularly, npx is useful for one-time use packages. Moreover, by default, npx first checks if the package that needs to be executed is present in the path or not. If the package isn’t installed, then npx will install it automatically. Otherwise, it will be executed directly. In other words, npx is a tool used to execute packages.

But here’s the catch: if you have installed npm version under 5.2.0, you will need to install npx on your system because npx isn’t included in those versions.

Don’t know whether npx is installed on your system? Write the following:

npx — v

Install it in your system by writing:

npm install -g npx

So what actually is npx? Well, it is a CLI tool that makes it hassle-free for you to install and manage dependencies. By downloading it, you can also run it in the local directory. What other benefits does npx offer? Well, you can create a React app without ensuring that all the packages are installed. Just write:

npx create-react-app my-app

Here, all the necessary files essential to run the package create-react-app will be downloaded. The files will be executed in the parameter my-app. This means our app will be created and initialized in the my-app folder.

What is MUI? (Including Pros and Cons)

Shariq Ahmed — Thu, 11 Jul 2024 10:02:29 +0000

If you are a developer but you want to add designs and animations to your app, then you should use MUI or Material UI.

Why? It’s one of the powerful React UI frameworks that has a design language. What’s more,is that it was created by Google in 2014. And it’s not just a basic design app. It has a lot of designs, animations, grid systems, as well as lighting effects.

Apart from React.js, you can also use it with Angular.js and Vue.js. It follows the principles of Material Design. The MUI widget library has lots of buttons to help you make everything from buttons to data tables.

So, is there any prerequisite for MUI? Well, you need to have a code editor. Not only this, you should also have some knowledge of developing React apps.

Although, as of now, not a single major company has disclosed whether they use MUI or not. But, OpenClassrooms and QuintoAndar, to name a few, use it.

But why are developers using it, and what are the pros of using it? Well, MUI has the following pros:

Easily accessible themes from the component props. This eliminates the need to search for them.
Clear consistency in UI.
Fast performance.
Compatibility with any theme.

Now, the bigger question is how to install it. Well, just install it using the npm package by writing,

npm install @material-ui/core

And tada!

But for beginners, is MUI hard to learn? Well, no.

This is because its documentation is way too detailed. They have lots of use cases of their library as well. So, it’s really useful to learn using all their documentation. But this doesn’t mean that MUI doesn’t have any cons.

The bundle size of MUI is large. This is probably because of the comprehensiveness of its component collection. At times, some also feel like MUI also puts unnecessary design constraints.

GitLab vs GitHub vs BitBucket

Shariq Ahmed — Mon, 08 Jul 2024 09:37:38 +0000

There are many cloud-based Git hosting platforms, including GitLab, GitHub, and Bitbucket, to name a few. But which one is the best? Well, it depends on your preference. Your preferred choice should hinge on collaboration features, automation, tools, integrations, etc. For instance, if cost is your main concern, then you should go with GitHub because you can enjoy many of its features in its free plan. GitLab is comparatively expensive.

Now, before moving towards their differences, let me first give you basic information about each. So, let’s start with GitHub. It is a platform used to share code. It was released in 2008, and within years, it gained traction. As of 2024, it is considered one of the biggest repositories with more than 30 million projects. You can learn more about GitHub here:

https://medium.com/@shariq.ahmed525/what-is-github-and-its-advantages-b7643fb680a3

GitLab, on the other hand, was created by Ukrainian developers Dmitriy Zaporozhets and Valery Sizov. It was released in 2011 with the same purpose — to share code. According to Wikipedia, GitLab also has more than 30 million users.

Bitbucket was also released in 2008. Initially, it was specific to Mercurial projects. But in 2011, it started using Git hosting. Although it hasn’t garnered as many users, it still has 10 million users. Developers love its CI/CD integration, which helps in building, deploying, and testing code.

Project Distribution

Bitbucket is best suited to be used by team members. GitHub, on the other hand, is mostly used at the company level. This is because the repository itself uses the organization level for project distribution. When we talk about GitLab, it has project distribution in groups. This means that you can share the code within the group.

Repository Import

In GitHub, you are given free rein to import the repository from one platform to another. In Bitbucket, the only restriction is on Mercurial platforms. This means that you can import the repository, but the platform must be Mercurial. Otherwise, you can’t import. GitLab users can import the repositories, but the platform should be Git.

Open-Source Repository Support

For now, open-source repository support is only provided in GitHub. Though, in both Bitbucket and GitLab, there are some features that will help you. But, in all of them, you won’t be able to fully support open-source projects.

CI Integration

In GitHub, there are many CI integrations that can help you in sharing codes. Unfortunately, this facility isn’t provided to users who are using GitLab. However, in Bitbucket, users can freely access CI.

API Integrations

These integrations are used for app development. GitHub has its own integrations. Bitbucket, on the other hand, offers the option to integrate various APIs with services. Developers have to use their own APIs in GitLab.

Best For

GitHub is best when you have to collaborate with developers around the globe. This is because developers can also contribute. It is important in the development stage required for code development and research. On the other hand, GitLab is best when you need comprehensive features that support the end-to-end development process. When it comes to BitBucket, it’s best for teams that want to collaborate on private software. BitBucket is also more flexible.

What is Actually GitLab?

Shariq Ahmed — Wed, 03 Jul 2024 05:38:27 +0000

There is a genuine problem with managing and securing the deployment of software that is faced by a lot of companies. So, to help companies sort out this issue, GitLab was introduced.

It’s an open-source code repository that helps in DevOps and DevSecOps projects. Developers can test as well as deploy code. The selling point of GitLab is actually its cloud-based Git repository that a lot of developers love. The best part? It’s totally free. There is an option for storing your online code and tracking your own issue. In fact, by using GitLab, you can host varying development chains and versions.

GitLab was created in 2011 by developers, Valery Sizov and Dmitriy Zaporozhets. They were both from Ukraine. The reason they made GitLab was that they needed a tool that could help them collaborate with other team members. Even now GitLab is used for the same purpose. It offers everything from tracking to planning.

Moreover, GitLab also gives end-to-end DevOps capabilities that you can add to every development cycle. Additionally, there is this option to automate as well as test code by using GitLab’s continuous integration(CI). In GitHub, developers have to choose their CI/CD tools after integration. But in GitLab, CI/CD tools are already integrated. In fact, you can also edit the role of users and this is really easy to do. Its main focus is on reliability.

Developers can also use many security-related capabilities. There is one dashboard that can help in vulnerability management as well. Developers can also use fuzz testing which is basically used to detect software defects as well as problems. Additionally, there are some public and private branches. Again, you don’t have to pay a single penny just to use them. Though, GitHub has some paid plans.

Now, let’s see some prominent features of GitLab.

They can import code repositories from Bitbucket.
The community is open-source. This means that if you find any error in it then you can mention that as well.
The best maintenance is present between repositories.
There are a range of tools that are used to track time as well as issues.
The code can be easily maintained as well as previewed.
There are a lot of self-hosted versions that are free of cost.
Users can also review project development charts.

However, like any other technology, there are some cons as well. For instance, the server of GitLab is really slow. There are a lot of bugs that you might need to manage. There are some features that are related to software development and they are missing. In a lot of repositories, there are many common problems. Contrary to GitHub, there are a lot of plans that GitLab have that can help in load performance testing. But GitLab has varying paid plans. But yes. At times, GitLab can be really complicated for beginners.