teleren/devto huggingface dataset:
Fifth Steps in Python: Marvelous Modules and Lively Libraries
Published: 2023-05-31 17:34:44

Welcome back, intrepid Pythonauts! We've ventured deep into the Python wilderness, wrestled with data structures, and emerged victorious. Now, it's time to unlock the treasure chest of Python's resources – modules and libraries.

Think of modules and libraries as the secret sauce of your Python recipes. They're like coding shortcuts, ready-made pieces of code that you can import and use to perform complex tasks. Instead of starting from scratch, you can stand on the shoulders of coding giants. Sounds super cool, right? It is.

## 1. Modules – Coding Power-ups

A module in Python is like a code library - a file containing a set of functions that you can include in your application. They're like cheat codes for your video game, helping you reach higher levels faster.

Here's how you import a module:

```python
import datetime
```

And here's how you use a function from that module:

```python
print(datetime.datetime.now())
```

Boom! You just printed the current date and time. All thanks to the power of modules.

## 2. Libraries – Supercharged Coding

Python's libraries are like a supermarket for coders. They contain pre-coded solutions for a wide variety of tasks, from data analysis to machine learning and much more. Some popular libraries include NumPy for mathematical tasks, pandas for data manipulation, and Matplotlib for data visualization.

Here's how you import a library:

```python
import numpy
```

Now, you can use any of the functions available in the numpy library. For example, to create an array, you can use the array function:

```python
my_array = numpy.array([1, 2, 3])
```

With libraries at your fingertips, you can do so much more, so much faster.

## Just the Beginning

The best part about Python's modules and libraries is that there are so many of them, covering just about anything you might want to do. And the Python community is always creating more. It's like a never-ending buffet of coding power-ups.

## Until We Meet Again...

Our Python journey has led us to some pretty amazing places, and there's so much more to come. Next time, we'll venture into the thrilling world of Python file handling. It's a bit like being a librarian, but with less dust and more data.

In the meantime, get to know some modules and libraries. Flex your Python muscles and see what you can create. Remember, the best way to learn Python is to use Python. So get coding, and have some fun!

---

**Found this guide helpful? Share it with your fellow Pythonauts, leave a comment, and don't forget to subscribe for more Python adventures!**

> P.S. No Pythonauts were bitten by the Python snake in the creation of this guide. Always remember, real snakes are not to be trifled with. Especially if they're constrictors.
Fourth Steps in Python: Building Castles with Data Structures
Tags: python, beginners, programming, tutorial
Published: 2023-05-31 17:34:38

Welcome back, brave dummies! Our journey into the Python wilderness is about to get a whole lot more interesting. Now that we've dipped our toes into the shallow end with functions and conditionals, it's time to venture into deeper waters. But fear not! Your trusted guide is here with the coding equivalent of water wings. 

Today, we're going to construct towering castles of code using Python's data structures. These are like the Lego bricks of the programming world – basic building blocks that can be put together in countless ways. 

## 1. Tuples – The Unchangeable Ones

In Python, a tuple is a collection of objects which are ordered and immutable. Yes, they're like the honey badger – they just don’t care. Once they’re set, they don’t change. Here's how you make a tuple:

```python
my_tuple = ("apple", "banana", "cherry")
```

That's it. Your tuple is ready, as sturdy and unchanging as a rock. Try to add or remove something, and it’ll just laugh in your face. Well, not literally, but you get the idea.

## 2. Sets - No Duplication Allowed

A set in Python is like a unique tattoo. You don’t have the same design twice. Sets are unordered collections of unique elements. Here's how you create a set:

```python
my_set = {"apple", "banana", "cherry"}
```
Try to add another "apple" to this set and Python will simply roll its eyes at you. Remember, in the land of sets, there’s no room for repetition.

## 3. Dictionaries - The Key to Finding Values

Dictionaries in Python are a bit like real dictionaries. Instead of words and definitions, though, Python dictionaries have keys and values. Each key leads to a value. Here's an example:

```python
my_dictionary = {
  "brand": "Ford",
  "model": "Mustang",
  "year": 1964
}
```
You just created a dictionary about a car. Feel free to bask in the coolness of it all.

## The Adventure Continues

Learning Python is an adventure, with treasures and pitfalls at every turn. You're doing great, trust me. In fact, you’re not a dummy anymore. But let’s keep that as our little secret for now.

## Until Next Time…

That’s all for this episode, intrepid dummies. Next time, we’ll explore the fascinating world of modules and libraries. It’s like discovering the Python supermarket – so many ready-made solutions, all waiting to be put in your coding basket.

Remember, Python isn’t a spectator sport. You learn by doing. So roll up your sleeves, fire up your computer, and build something amazing with your new Python data structures knowledge.

---

**Did you enjoy the journey? Share this guide with fellow adventurers and leave your footprints (comments) behind. Don’t forget to subscribe for more Python journeys, and remember, I'm here to navigate the Python wilderness with you.**

> P.S. As always, no dummies, honey badgers, or Lego pieces were harmed in the creation of this guide. Python safety is our number one priority.
Third Steps in Python: Fun with Functions and Cracking Conditionals
Tags: python, beginners, programming, tutorial
Published: 2023-05-31 17:34:31

Greetings, my dear dummies! Back for more, I see. I’m thrilled to see you've survived the loops and lists, and didn't get lost in the Python wilderness. Now, with a bit of coding confidence under your belt, it's time to conquer the next two Python pillars - Functions and Conditionals. Sounds serious, right? It’s not. Well, not as serious as running out of coffee on a Monday morning.

## 1. Functions - Your Coding Shortcuts

A function is like your favorite recipe, packed neatly into an easily reusable form. Write it once, use it again and again. Just like the secret recipe of grandma’s apple pie, but with less apple and more code.

Here's how you make a function:

```python
def my_function():
  print("Hello from my function!")
```
And here’s how you use it, or as the techies say, “call” it:

```python
my_function()
```

Now your function is out there, saying hello to the world. It’s a little bit like sending your child off to their first day of school. They grow up so fast, don’t they?

## 2. Conditionals - Making Decisions, Python-Style

In Python, as in life, you often have to make decisions based on certain conditions. Is it raining? Take an umbrella. Out of coffee? Cry. That’s the essence of conditionals.

In Python, we use `if`, `elif` (short for 'else if'), and `else` to make these decisions. Here's an example:

```python
weather = "raining"

if weather == "raining":
  print("Better take an umbrella!")
else:
  print("Enjoy the sunshine!")
```

In the above scenario, Python's going to tell you to take an umbrella because it's raining. Always looking out for you, Python is.

## 3. Patience and Practice

By now, you’ve probably realized that learning Python isn't a sprint; it's more like a long game of snakes and ladders. You’ll climb up the ladders of understanding and occasionally slide down the snakes of confusion. But that's okay! Every snake slide is a chance to learn something new.

## Until We Meet Again…

That's all for today, fellow dummies. Next time, we’ll play with Python's building blocks – Data Structures. Imagine Lego blocks, but less painful when you step on them.

In the meantime, try writing some functions and conditionals of your own. Get creative! Remember, Python is not just a language, it's a journey. And every line of code you write is a step on that journey. So keep coding!

---

> **Did this guide help? Share it with your friends, family, and neighborhood coding enthusiasts. Don’t forget to subscribe for more thrilling Python escapades! And remember, I'm always here to navigate the Python wilderness alongside you.**

P.S. No dummy was harmed or exposed to real snakes during the creation of this content. Always remember, real snakes are not to be trifled with. Especially if you've forgotten your umbrella.
Award Winning Cobwebs: The ‘Grand’ Era of AI and the 'Extinct' Art of Programming
Tags: ai, programming
Published: 2023-05-31 17:32:42

Behold, my friends! Artificial Intelligence has made programming as relevant as an 8-track tape. Who needs syntax errors when AI can brew perfect Java?

Once upon a time, in a world far, far away, there was a thing called "programming". Young, bright-eyed souls spent countless hours hunched over keyboards, engaging in what was endearingly called 'debugging'. The ritual involved squinting at screens until their retinas protested, and drinking enough caffeine to sustain a small mountain village. But those were the old days. Thanks to our benevolent overlord, AI, these primitive customs are now as trendy as powdered wigs and dial-up modems.

Enter the reign of Artificial Intelligence, where every device you own is cleverer than your pet hamster. Why learn to code when your smartphone can order pizza, run your bathtub, and probably even solve quadratic equations? And, let's not forget the joy of allowing Alexa to control your entire home. Why turn off the lights yourself when you can waste precious breath on commanding Alexa to do it for you? Progress, my friends, progress.

Indeed, the art of programming has been driven to the brink of extinction. So, go ahead and throw away those archaic textbooks and uninstall that Python you've been wrestling with. Programming languages are as dead as the dodo. Our AI overlords have made sure of that. To boot, they’ve been kind enough to put an end to those sleepless nights debugging code. We’ve evolved into a new era of blissful ignorance.

With AI, we can now sit back and watch as our devices take the reins, leading us into a glorious future where human error is a thing of the past. I mean, who doesn't adore the idea of autonomous cars that drive us into the next lamppost? It’s all in the name of technological advancements.

In short, forget about the primal joy of building something from scratch. Why bother when we've got AI to save us from such tedious tasks? Goodbye to those meddlesome IF statements, farewell to the enigmatic semicolons, and a warm adieu to the joyful debugging sessions at 3 am. You won't be missed. Not when we have AI to do the heavy lifting for us.

Let us raise a toast to our AI overlord, for freeing us from the burdensome chains of programming. May we never know the sweet taste of a successfully executed code again. After all, AI isn't a fad, it's the future, and programming isn't dead, it's just... hibernating indefinitely.
[PT-BR] Descobrindo a realidade por trás do título: A jornada turbulenta de um "sênior" em busca de conhecimento
Tags: development, desenvolvedor, programador, programming
Published: 2023-05-31 17:19:53

Decidi criar este perfil para mostrar uma visão que vejo na área de programação com pessoas que dizem ser Sênior ou Pleno, mas ainda precisa de muita lapidação.

Preparados para uma história real sobre as ilusões e desafios enfrentados por aqueles que se autodenominam sênior ou pleno? Acompanhe essa jornada de autoconhecimento repleta de tropeços, dúvidas e uma busca incessante por aprimoramento.

Pode parecer contraditório, mas muitas vezes aqueles que se consideram sêniores no mercado de trabalho estão longe de dominar completamente seus conhecimentos. Eles acreditam que têm todas as respostas, mas na prática, enfrentam dificuldades e ficam repletos de incertezas ao aplicar seus conhecimentos.

Imagine entregar um problema mediano a um profissional pleno e testemunhar uma verdadeira mágica acontecer. No entanto, ao entregar o mesmo problema a um suposto sênior, você pode se surpreender com a falta de organização e clareza em sua execução. É perceptível que algo não está certo.

Não estamos sugerindo que um sênior deva se limitar a apenas gerenciar equipes. Pelo contrário, um verdadeiro profissional sênior trabalha com uma combinação poderosa de organização e planejamento, além de ter um profundo conhecimento técnico.

Infelizmente, muitas vezes nos deparamos com pessoas que se autodenominam sêniores, mas que possuem dúvidas que normalmente seriam atribuídas a um profissional júnior. Isso nos faz questionar se podemos realmente confiar nessa designação.

Aqui vai uma reflexão: se alguém é sênior, essa pessoa deveria ser capaz de se destacar em qualquer linguagem ou framework. Não deveria haver essa divisão entre "sênior em uma linguagem" e "júnior em outra". A verdadeira expertise transcende as barreiras das tecnologias, e um profissional sênior deveria ter uma base sólida que lhe permitisse se adaptar a qualquer contexto.

A história que compartilhei é uma visão crua da realidade que muitos enfrentam. A ilusão de ser sênior pode ser facilmente abalada quando confrontada com desafios reais. É uma chamada para a humildade, o aprendizado contínuo e a busca incansável pelo domínio em todas as áreas relevantes.

Lembre-se: ninguém é perfeito, e todos nós estamos em constante evolução. Não se deixe enganar por títulos vazios. Busque conhecimento, desafie-se e mantenha-se aberto a novas perspectivas. Somente assim poderemos nos tornar verdadeiros profissionais sêniores, independentemente da linguagem ou framework.

E você, já passou por situações semelhantes? Compartilhe suas experiências nos comentários abaixo. Vamos aprender uns com os outros e construir um futuro onde o conhecimento genuíno prevaleça!
1476. LeetCode's Subrectangle Queries - 🔥Simple LOGICAL Java Solution - Beats 66%
Tags: java, math, tutorial, learning
Published: 2023-05-31 17:08:30

For a more in-depth explanation, please view my GitHub repository: https://github.com/VerisimilitudeX/LeetCode

# Intuition
The problem is to implement a class that can update and query a subrectangle of a given rectangle. A subrectangle is defined by its four corners (row1, col1, row2, col2). One way to solve this problem is to store the original rectangle and a list of updates in the class. Each update contains the coordinates of the subrectangle and the new value to fill it with. To query a value at a given position (row, col), we can iterate through the updates in reverse order and check if the position falls within any subrectangle. If yes, we return the corresponding value. If no, we return the original value from the rectangle.

# Approach
To implement this approach, we need two data structures: a 2D array to store the original rectangle and a list to store the updates. The constructor takes the rectangle as input and initializes the array and the list. The updateSubrectangle method appends a new update to the list with the given parameters. The getValue method iterates through the list from the end and checks if the given position is within any update. If yes, it returns the value from that update. If no, it returns the value from the array.

# Complexity
- Time complexity:
The time complexity of the constructor is $$O(mn)$$ where $$m$$ and $$n$$ are the number of rows and columns of the rectangle respectively. The time complexity of the updateSubrectangle method is $$O(1)$$ since it only appends to the list. The time complexity of the getValue method is $$O(k)$$ where $$k$$ is the number of updates in the list.

- Space complexity:
The space complexity of the class is $$O(mn + k)$$ where $$m$$, $$n$$ and $$k$$ are defined as above. We need $$O(mn)$$ space to store the array and $$O(k)$$ space to store the list.

# Code
```java
import java.util.*;

class SubrectangleQueries {
 private int[][] rect;
 private List<int[]> updates;

 public SubrectangleQueries(int[][] rectangle) {
 rect = rectangle;
 updates = new ArrayList<>();
 }

 public void updateSubrectangle(int row1, int col1, int row2, int col2, int newValue) {
 updates.add(new int[]{row1, col1, row2, col2, newValue});
 }

 public int getValue(int row, int col) {
 for (int i = updates.size() - 1; i >= 0; i--) {
 int[] update = updates.get(i);
 if (row >= update[0] && row <= update[2] && col >= update[1] && col <= update[3]) {
 return update[4];
 }
 }
 return rect[row][col];
 }
}
```

My LeetCode Solutions: https://leetcode.com/VerisimilitudeX/
Dynamically generate docker-compose.yaml for YugabyteDB and more!
Tags: docker, yugabyte, distributedsystems
Published: 2023-05-31 17:04:44

`yb-docker-composer` is Python-based CLI for generating docker-compose.yaml files dynamically based on the arguments specified at the CLI. 

Git repo: https://github.com/hiimivantang/yb-docker-composer

## Motivation

I always find the notion of running docker containers from declarative config files a more elegant solution as opposed to just running them via `docker run` commands. With docker-compose, I can easily set up and tear down different combination of services for testing or demo purposes.

But wait. There’s already `yb-docker-ctl` and that allows anyone to easily run YugabyteDB as containers while allowing customization such as the ability to specify replication factor or yb-master / yb-tserver flags. However, I realised that in order to set `environment` attribute or to add linux capabilities via `cap-add`, I probably have to hardcode them somewhere within [yb-docker-ctl code](https://github.com/yugabyte/yugabyte-db/blob/v2.18.0.1/bin/yb-docker-ctl#L343C27-L344). 

Instead of copying, pasting and editing existing `docker-compose.yaml` files and ending up with a truckload of them, you can use `yb-docker-composer` to dynamically generate `docker-compose.yaml` on the fly based on arguments you supply when running `yb-docker-composer` CLI. 

## How does it work?

Simply run `python yb-docker-composer.py` and it will generate a docker-compose.yaml for running a YugabyteDB cluster with RF=3 (i.e. 3 x YB-Master and 3 x YB-TServer containers)

```
python yb-docker-composer.py
```

Specifying `--prometheus` option will generate service definitions for both Prometheus and Grafana for monitoring your YugabyteDB cluster. Grafana dashboard included.

```
python yb-docker-composer.py --prometheus
```

The python script will take in your CLI arguments and render the respective service definitions based on Jinja templates that can be found in https://github.com/hiimivantang/yb-docker-composer/tree/master/templates.

## What's next?

I'm planning to include templates for Kafka brokers and connect workers, sqlpad, and some accompanying apps. Appreciate and welcome any thoughts and comments.
Unleashing the Power of Write-Ahead Logging in PostgreSQL: Safeguarding Data with Resilience
Published: 2023-05-31 17:03:36



## Introduction:
In the realm of PostgreSQL, where data integrity and recovery reign supreme, Write-Ahead Logging (WAL) emerges as a formidable guardian. WAL is a powerful mechanism that ensures the safety of your valuable data and enables swift recovery in the face of unexpected events. Join us on this captivating journey as we delve into the world of Write-Ahead Logging in PostgreSQL and unlock the secrets behind its strength.

## Understanding Write-Ahead Logging:

Explore the essence of Write-Ahead Logging and its vital role in PostgreSQL's durability and crash recovery mechanisms.
Discover the transaction log, where changes are initially recorded before being applied to the data files.
Grasp the significance of the WAL buffer and the seamless coordination between disk and memory, where changes are gracefully staged before being written to disk.
The Sequence of WAL Spells:

## Embark on a fascinating exploration of the sequential order in which PostgreSQL writes and applies WAL records.
Uncover how WAL records encapsulate transaction changes, ensuring data atomicity and durability.
Discover the magic behind WAL segments, checkpoints, and archiving, safeguarding the database's integrity and enabling efficient recovery.

## WAL and Database Performance:

Delve into the performance implications of Write-Ahead Logging in PostgreSQL.
Understand the trade-offs between durability and write performance, and how PostgreSQL allows tuning for different levels of durability.
Unleash the potential of WAL configuration, such as adjusting the WAL buffer size and checkpoint settings, to optimize database performance.

## WAL Archiving and Point-in-Time Recovery:

Unlock the resilience of WAL archiving and its role in enabling point-in-time recovery.
Discover how archived WAL segments act as snapshots, enabling precise restoration of the database to a specific point in time.
Master the art of performing point-in-time recovery, resurrecting your database from unexpected mishaps.

## The Power of Write-Ahead Logging in PostgreSQL:

Embrace the power of Write-Ahead Logging as a guardian of your data's integrity and recovery.
Experience the peace of mind knowing that your data remains protected, even in the face of hardware failures, power outages, or system crashes.
Marvel at the seamless recovery capabilities provided by WAL, ensuring your database remains intact and accessible, no matter the circumstances.
In the realm of PostgreSQL, Write-Ahead Logging stands tall as a powerful ally, safeguarding your data and enabling swift recovery. By understanding the inner workings of WAL, you gain the strength of data durability and the confidence to overcome unexpected challenges. Embrace the power of Write-Ahead Logging in PostgreSQL, and let your data thrive with resilience and reliability.