#range function in python

[mythauragame]

Development Update - December 2024

Happy New Year, everyone! We're so excited to be able to start off 2025 with our biggest news yet: we have a planned closed beta launch window of Q1 2026 for Mythaura!

Read on for a recap of 2024, more information about our closed beta period, Ryu expressions, January astrology, and Ko-fi Winter Quarter reward concepts!

2024 Year in Review

Creative

This year, the creative team worked on adding new features, introducing imaginative designs, and refining lore/worldbuilding to enrich the overall experience.

New Beasts and Expressions: All 9 beast expression bases completed for both young and adult with finalized specials for Dragons, Unicorns, Griffins, Hippogriffs, and Ryu.

Mutations, Supers and Specials: Introduced the Celestial mutation as well as new Specials Banding & Merle, and the Super Prismatic.

New Artist: Welcomed Sourdeer to the creative team.

Collaboration and Sponsorship: Sponsored several new companions from our Ko-Fi sponsors—Amaru, Inkminks, Somnowl, Torchlight Python, Belligerent Capygora, and the Fruit-Footeded Gecko.

New Colors: Revealed two eye-catching colors, Canyon (a contest winner) and Porphyry (a surprise bonus), giving players even more variety for their Beasts.

Classes and Gear: Unveiled distinct classes, each with its own themed equipment and companions, to provide deeper roleplay and strategic depth.

Items and Worldbuilding: Created a range of new items—from soulshift coins to potions, rations, and over a dozen fishable species—enriching Mythaura’s economy and interactions.

Star Signs & Astrology: Continued to elaborate on the zodiac-like system, connecting each Beast’s fate to celestial alignments.

Questing & Story Outline: Laid the groundwork for the intro quest pipeline and overarching narrative, ensuring that players’ journey unfolds with purposeful progression.

Code

This year, the development team worked diligently on refining and expanding the codebase to support new features, enhance performance, and improve gameplay experiences. A total 429,000 lines of code changed across both the backend and frontend, reflecting:

New Features: Implementation of systems like skill trees, inventory management, community forums, elite enemies, npc & quest systems, and advanced customization options for Beasts.

Optimizations and Refactoring: Significant cleanup and streamlining of backend systems, such as game state management, passive effects, damage algorithms, and map data structures, ensuring better performance and maintainability.

Map Builder: a tool that allows us to build bespoke maps

Regular updates to ensure compatibility with modern tools and frameworks.

It’s worth noting that line changes alone don’t capture the complexity of programming work. For example:

A single line of efficient code can replace multiple lines of legacy logic.

Optimizing backend systems often involves removing redundant or outdated code without adding new functionality.

Things like added dependencies can add many lines of code without adding much bespoke functionality.

Mythaura Closed Beta

We are so beyond excited to share this information with you here first: Mythaura closed beta is targeted for Q1 2026!

On behalf of the whole team, thank you all so, so much for all of the support for Mythaura over the years. Whether you’ve been around since the Patreon days or joined us after Koa and Sark took over…it’s your support that has gotten this project to where it is. We are so grateful for the faith and trust placed in us, and the opportunity to create something we hope people will truly love and enjoy. This has truly been a collaborative effort with you and we are constantly humbled by all of the thoughtful insights, engaging discussions, and great ideas to come out of this amazing community of supporters.

So: thank you again, it’s been an emotional and amazing journey for the dev team and we’re delighted to join you on your journeys through Mythaura.

Miyazaki Full-Time

Hey everyone, Koa here!

We’re thrilled to share some news about Mythaura’s development! Starting in 2025, Miya will be officially dedicating herself full-time to Mythaura. Her focus will be on bringing even more depth and wonder to the world of Mythaura through content creation, worldbuilding, and building up the brand. It’s a huge step forward, and we’re so excited for the impact her passion and creativity will have on the project!

In addition, I’ve secured 4-day weeks and will be working full-time each Friday to dive deeper into development. This extra push is going to allow us to keep moving steadily forward on both the art and code fronts, and with Miya’s expanded role, the next year of development is looking really promising.

Thank you all for being here and supporting Mythaura every step of the way. We can’t wait to share more as things progress!

Closed Beta FAQ

In the interest of keeping all of the information about our Closed Beta in one place and update as needed, we have added as much information as possible to the FAQ page.

If you have any questions that you can think of, please feel free to reach out to us through our contact form or on Discord!

Winter Quarter (2025) Concepts

It’s the first day of Winter Quarter 2025, which means we’ve got new Quarterly Rewards for Sponsors to vote on on our Ko-fi page!

Which concepts would you like to see made into official site items? Sponsors of Bronze level or higher have a vote in deciding. Please check out the Companion post and the Glamour post on Ko-fi to cast your vote for the winning concepts!

Votes must be posted by January 29, 2025 at 11:59pm PDT in order to be considered.

All Fall 2024 Rewards are now listed in our Ko-fi Shop for individual purchase for all Sponsor levels at $5 USD flat rate per unit. As a reminder, please remember that no more than 3 units of any given item can be purchased. If you purchase more than 3 units of any given item, your entire purchase will be refunded and you will need to place your order again, this time with no more than 3 units of any given item.

Fall 2024 Glamour: Diaphonized Ryu

Fall 2024 Companion: Inhabited Skull

Fall 2024 Solid Gold Glamour: Hippogriff (Young)

NOTE: As covered in the FAQ, the Ko-fi shop will be closing at the end of the year. These will be the last Winter Quarter rewards for Mythaura!

New Super: Zebra

We've added our first new Super to the site since last year's Prismatic: Zebra, which has a chance to occur when parents have the Wildebeest and Banding Specials!

Zebra is now live in our Beast Creator--we're excited to see what you all create with it!

New Expressions: Ryu

The Water-element Ryu has had expressions completed for both the adult and young models. Expressions have been a huge, time-intensive project for the art team to undertake, but the result is always worth it!

Mythauran Astrology: January

The month of January is referred to as Hearth's Embrace, representing the fireplaces kept lit for the entirety of the coldest month of the year. This month is also associated with the constellation of the Glassblower and the carnelian stone.

Mythaura v0.35

Refactored "Beast Parties" into "User Parties," allowing non-beast entities like NPCs to be added to your party. NPCs added to your party will follow you in the overworld, cannot be made your leader, and will make their own decisions in combat.

Checkpoint floor functionality ironed out, allowing pre-built maps to appear at specific floor intervals.

The ability to set spawn and end coordinates in the map builder was added to allow staff to build checkpoint floors.

Various cleanups and refactors to improve performance and reduce the number of queries needed to run certain operations.

Added location events, which power interactable objects in the overworld, such as a lootable chest or a pickable bush.

Thank You!

Thanks for sticking through to the end of the post, we always look forward to sharing our month's work with all of you--thank you for taking the time to read. We'll see you around the Discord.

[spacetimewithstuartgary]

New data model paves way for seamless collaboration among US and international astronomy institutions

Software engineers have been hard at work to establish a common language for a global conversation. The topic—revealing the mysteries of the universe. The U.S. National Science Foundation National Radio Astronomy Observatory (NSF NRAO) has been collaborating with U.S. and international astronomy institutions to establish a new open-source, standardized format for processing radio astronomical data, enabling interoperability between scientific institutions worldwide.

When telescopes are observing the universe, they collect vast amounts of data—for hours, months, even years at a time, depending on what they are studying. Combining data from different telescopes is especially useful to astronomers, to see different parts of the sky, or to observe the targets they are studying in more detail, or at different wavelengths. Each instrument has its own strengths, based on its location and capabilities.

"By setting this international standard, NRAO is taking a leadership role in ensuring that our global partners can efficiently utilize and share astronomical data," said Jan-Willem Steeb, the technical lead of the new data processing program at the NSF NRAO. "This foundational work is crucial as we prepare for the immense data volumes anticipated from projects like the Wideband Sensitivity Upgrade to the Atacama Large Millimeter/submillimeter Array and the Square Kilometer Array Observatory in Australia and South Africa."

By addressing these key aspects, the new data model establishes a foundation for seamless data sharing and processing across various radio telescope platforms, both current and future.

International astronomy institutions collaborating with the NSF NRAO on this process include the Square Kilometer Array Observatory (SKAO), the South African Radio Astronomy Observatory (SARAO), the European Southern Observatory (ESO), the National Astronomical Observatory of Japan (NAOJ), and Joint Institute for Very Long Baseline Interferometry European Research Infrastructure Consortium (JIVE).

The new data model was tested with example datasets from approximately 10 different instruments, including existing telescopes like the Australian Square Kilometer Array Pathfinder and simulated data from proposed future instruments like the NSF NRAO's Next Generation Very Large Array. This broader collaboration ensures the model meets diverse needs across the global astronomy community.

Extensive testing completed throughout this process ensures compatibility and functionality across a wide range of instruments. By addressing these aspects, the new data model establishes a more robust, flexible, and future-proof foundation for data sharing and processing in radio astronomy, significantly improving upon historical models.

"The new model is designed to address the limitations of aging models, in use for over 30 years, and created when computing capabilities were vastly different," adds Jeff Kern, who leads software development for the NSF NRAO.

"The new model updates the data architecture to align with current and future computing needs, and is built to handle the massive data volumes expected from next-generation instruments. It will be scalable, which ensures the model can cope with the exponential growth in data from future developments in radio telescopes."

As part of this initiative, the NSF NRAO plans to release additional materials, including guides for various instruments and example datasets from multiple international partners.

"The new data model is completely open-source and integrated into the Python ecosystem, making it easily accessible and usable by the broader scientific community," explains Steeb. "Our project promotes accessibility and ease of use, which we hope will encourage widespread adoption and ongoing development."

[tk-duveraun]

15/? Luo Binghe is SO NORMAL about Shen Yuan

1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15 (here), 16

Read up through even numbered parts on Ao3

[Objective Complete: Rendezvous with Shen Qingchun.]

I fucking know, Luo Binghe directed at the System even as he slashed at the Black Moon Rhinoceros-Python. Small spatial rifts cut through the clearing, hovering in the air and buzzing like a combination of bees and bad dial-up. Functionally, the tears served as atom-thin blades removing chunks of whatever passed through them. They didn't last long, but they were easily the deadliest thing in Jue Di Gorge.

Shen Yuan fought alongside him. His guan was missing a chunk and a thick lock of hair ended at his shoulder from the rifts. Worse, his eyes were bloodshot and his upper lip stained from a nosebleed, but he'd had those when arrived in the clearing and jumped off of Xiu Ya.

Luo Binghe lost a chunk of his bicep and his leg burned with venom, but finally the beast was dead. He shoved a mediocre panacea in his mouth and approached Shen Yuan to check on his injuries.

[Quest "Pledges of Frost and Flame" Updated! Objective: Survive.]

The System text froze Luo Binghe with his hand outstretched. With effort, he tore his eyes away from where Shen Yuan was panting and clutching his chest to look directly at the System display. What do you mean survive? What kind of objective is that?

[Answering Host: When not debuffed by status effect "Without a Cure" Shen Qingchun's power level easily eclipses that of Luo Baixiao]

Luo Binghe barely managed to dodge Xia Ya in his shock. The blade cut a thin line over the bridge of his nose. He jumped back, holding up Zheng Yang in defense even as his thoughts spun.

"Shizun, why? Surely you don't think that I-" He had to cut himself off to dodge another thrust.

Though not poisoned, Shen Yuan was clearly debuffed by something. Luo Binghe shouldn't have been able to dodge the Sword of Qing Jing at his level, strongest disciple or no.

Shen Yuan was still panting. He clutched his right bicep with his left arm, his hand leaving bloody creases. A thin stream of blood leaked from his right eye. When he spoke, his teeth were bloody. "Run, Disciple Luo. Run!"

Luo Binghe blocked a strike from Xiu Ya, the impact rattling his entire body. Zheng Yang screeched in protest at being used against Xiu Ya. Luo Binghe wholeheartedly agreed, but he also didn't want to die.

Despite bleeding from several orifices, it didn't look like Shen Yuan was undergoing a qi deviation, but he definitely wasn't in control of his actions.

"Shizun what's wrong?" Luo Binghe asked as he tried to make space between them. From his time reading IRS and then the years spent as a personal disciple he knew he had to stay at mid-range. If he went too far from Shen Yuan, his master would transition to using sword seals and qi techniques. He was called the Sword, but his offensive power was primarily long range with Liu Qingge or Wei Qingwei dealing with the opposition up close.

Shen Yuan closed the gap with a vicious overhead strike that Luo Binghe only just blocked in time.

"I told you to run!"

That close, Luo Binghe saw that the blood vessels hadn't just burst in Shen Yuan's eyes, but they wiggled and writhed gruesomely against the whites. His horror was so great that his concentration wavered and Xiu Ya broke through his guard, slashing deep into his shoulder, breaking his collarbone and rupturing the top of rupturing the top of his left lung.

For a moment, his vision blacked out and all Luo Binghe could see was the System blinking at him.

[Quest Objective: Survive]

The text morphed from a bright cyan to a blinding red as the characters swirled and then melted across his vision. He tasted blood. His muscles tore under his skin like cheap cotton. He only realized he was screaming when his voice gave out and he spat out what felt like a gallon of blood.

Luo Binghe came back to himself on his hands and knees in a puddle of blood. But his hands were wrong. They weren't his or Luo Baixiao's. His fingers were black and tipped with sharp claws that glittered with blood. He tried to send a thread of qi through his body to triage his condition, but it hurt like acid injected directly into his veins.

Focus brat, the seal locking away your demonic heritage is broken. Meng Mo's voice had never before been comforting, but any kind of answer in this situation was a blessing. You'll go into qi deviation and die if you're not careful. Few half-demons survive this, do you plan to be one?

Luo Binghe clenched his teeth and nodded silently to the demon in his head. He took a deep breath and then raised his head.

Only for the sight to make him spit another mouthful of blood.

Across the clearing, Shen Yuan was pinned to a tree by Xiu Ya. The gorgeous blade was pierced through his stomach. He was bleeding from all of his seven orifices, but despite everything his expression was grimly satisfied.

Luo Binghe tried to take a step toward his beloved only to feel like there was a collar around him neck yanking him back. He glanced over his shoulder and saw that a massive gash had opened in the ground under the corpse of the Rhinoceros-Python. The Endless Abyss had opened. He was well and truly in the climax of the arc but unlike Snowballing, he couldn't just jump to another POV.

With a howl, Luo Binghe fought the tether holding him back and surged toward Shen Yuan, collapsing on the ground at his master's feet. He lifted his hands and let them hover on either side of Xiu Ya.

"Shizun… who… how..?"

"Nevermind that, listen to this Master," Shen Yuan said around the blood dribbling from the corner of his mouth. His perfect, blood-stained hands reached down and cupped Luo Binghe's cheeks. "The sect masters won't stand idly by." Each word was a struggle.

"Save your energy! This is, this- Mu-shishu will be able to heal you fully, Shizun." So close, Luo Binghe was able to better-judge the placement of Xiu Ya. While it was a horrible sight, it seemed like it could be at the perfect angle to have avoided all of Shen Yuan's major organs. It was just a macabre taunt.

"Listen!" Shen Yuan's hands tightened on Luo Binghe's face. "My little demon… Disciple Luo is part Heavenly Demon. They will kill you on sight. The barriers surrounding the gorge were reinforced." Shen Yuan turned his head to spit more blood before continuing. "The only way out is down. Go into the Endless Abyss."

Some part of Luo Binghe suspected that Luo Baixiao had survived the conference by exiting stage Abyss, but the order still struck him cold.

"Shizun, I-"

"No, listen." Shen Yuan panted heavily, his strength draining quickly from the wound in his stomach. "There's a sword, Xin Mo, in the Abyss. You need it to kill him. Linguang-jun. Get it and be free, my little demon."

"Shizun, please! At least let me-"

"There's no time." More blood leaked from Shen Yuan's eyes. It looked like he was crying. "Save us both."

Luo Binghe felt Shen Yuan gather what remained of his spiritual energy, but he could do nothing to resist the sharp push that launched him back and into the crack in reality. As he fell, the sky disappeared above him.

[romanticiseadarkcity]

How 20 kilometres of coloured rope transformed this town square

By Julie Power

February 1, 2025 — 2.00amSave

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It has taken 20 kilometres of brightly coloured rope wound around a rainbow python-shaped pavilion to make Mount Druitt’s 50-year-old Dawson Park feel safer by night and cooler by day.

A new design for Mount Druitt’s Dawson Park has won a major award. CREDIT: SIMON WOOD PHOTOGRAPHY 

Mount Druitt’s town centre was NSW’s first planned town centre, a utopian vision of the 1970s featuring a park, swimming pool, library, offices, shops and school.

Far from delivering “the dream of a pleasantly landscaped area” the park became associated with street crime, alcohol, drug use and antisocial behaviour.

Its redesign is an example of how public art and landscaping, ranging from murals in Sydney’s inner west to laneways near railway stations, is making streets safer and bringing communities together.

Transport for NSW’s Safer Cities laneways program has been so successful that a spokesperson said it is to be extended. Another seven transport hub precincts across NSW will become safer and more vibrant under a similar initiative.

A mural by Jeff McCann in Sydenham has helped reduce graffiti.CREDIT: RHETT WYMAN

To bring people back to Dawson Park in Mount Druitt, and away from its dark and risky edges, the new rope-covered pavilion meanders around trees from an old-growth forest, a new stage and seating areas. The awning provides dappled shade and bright colours that resonate with its multicultural residents.

The project by Chrofi with JMD Design this month won gold in the international Better Futures Awards that recognises design excellence in government projects. Blacktown Council says it marked the beginning of a wave of changes to Mount Druitt.

Locals say crime has been reduced, families feel safer in the square and the local Westfield shopping centre has begun holding events in the park.

Previous attempts to deter unsociable activity in the park and plaza had failed. Despite no-go areas in the square, though, it had one of the busiest social calendars in Sydney.

The biggest part of the brief was to provide shade, but that wasn’t enough.

Zoeller said it had to be functional and provide lighting, security and a stage. “I wanted to create a space that was hopeful and playful. That had colour. That drew people towards it, particularly families.”

Blacktown Council senior architect Matthew Sales said there hadn’t been any significant investment in Mount Druitt for years, if not decades. “This was the first project in the town square since it was built [in 1972].”

Its redesign has coincided with plans to upgrade the pool next door and the library, which is part of a masterplan by Chrofi with JMD Design.

Perfect match in the inner west

The Kookaburra House in Newtown features a mural by David Cragg. CREDIT: RHETT WYMAN

Zoe Pedashenko’s home in Newtown is called the Kookaburra House by her daughter. That’s a reference to a mural of a giant kookaburra painted by artist David Cragg on the large end wall of her terrace.

Called Tributary, the mural was installed free of charge to Pedashenko by Inner West Council as part of its Perfect Match program. It commissions and matches mural artists with businesses, homes and apartment blocks with large walls to decorate.

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What started as a program 10 years ago to reduce graffiti has become so much more, said Inner West Council Mayor Darcy Byrne. “We want to keep it going … and make this a real tourist attraction for the inner west and a source of civic pride.”

Frequently, the murals tell a story about the history of the oldest suburbs in Australia. “It is a contemporary way of bringing that history to life,” he said.

Byrne said, unlike most public art projects, it rarely triggers a complaint. “The investment is modest but the ROI is very big,” he said.

For every mural paid for by the council, a member is inspired to privately commission their own. “We have more street artists than anywhere else in Sydney,” Byrne said.

Artist Fintan Magee remembered being arrested for graffiti just after finishing art school in Brisbane. Moving to Sydney in 2010, it was “incredible” when he was commissioned over a few years to do about 10 murals for Perfect Match.

These works helped launch what has become an international career for Magee. He has just finished a project in Florida in the US and his murals also feature on walls of apartment blocks in France, Denmark, Austria, Switzerland, Germany and Perth.

“I was trying to get my work seen. I was not trying to change the world,” said Magee. “The murals connect the community with the arts and give the public a sense of ownership … and improve mental health.”

Pedashenko said she wanted something bright and cheery, not too graphic or graffiti-like. She worked with Cragg and the council to find something they all liked and wouldn’t attract graffiti. So far, it has only been tagged once, and that was small and easy to wash off, she said.

Laneways projects making streets safer for women

Guildford laneway is now brighter and safer.

Women said they were too scared to use the lane: it was dark and grim, with barren gardens, insufficient lighting and trip hazards. Some had been harassed by men, who followed them and honked their car horns.

The town centre received a $1 million grant to improve the laneway. Now called Her Way Guildford, the passage from the station to the car park was transformed with a colourful mural, installation of CCTV cameras, lighting and phone charging stations, tables and benches, and a performance space.

Not only did women feel safer, the data on a council dashboard showed more people using the space and an uptick in sales in the shops nearby.

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Redfern ‘murder mall’ reinvented as $500 million Surry Hills Village

Following the success of the $30 million Safer Cities program, another seven transport hub precincts will be overhauled under a similar initiative by Transport.

In the initiative, called ReVITALise – Public Transport Precinct Vibrancy Grant, seven councils have been selected to each receive $1 million to improve areas within 500 metres of a transport hub precinct. This may include better seating, shade or lighting, landscaping, public art or murals, or adding bike racks or mobile phone charging stations.

[sak-shi]

Python Libraries to Learn Before Tackling Data Analysis

To tackle data analysis effectively in Python, it's crucial to become familiar with several libraries that streamline the process of data manipulation, exploration, and visualization. Here's a breakdown of the essential libraries:

 1. NumPy

   - Purpose: Numerical computing.

   - Why Learn It: NumPy provides support for large multi-dimensional arrays and matrices, along with a collection of mathematical functions to operate on these arrays efficiently.

   - Key Features:

     - Fast array processing.

     - Mathematical operations on arrays (e.g., sum, mean, standard deviation).

     - Linear algebra operations.

 2. Pandas

   - Purpose: Data manipulation and analysis.

   - Why Learn It: Pandas offers data structures like DataFrames, making it easier to handle and analyze structured data.

   - Key Features:

     - Reading/writing data from CSV, Excel, SQL databases, and more.

     - Handling missing data.

     - Powerful group-by operations.

     - Data filtering and transformation.

 3. Matplotlib

   - Purpose: Data visualization.

   - Why Learn It: Matplotlib is one of the most widely used plotting libraries in Python, allowing for a wide range of static, animated, and interactive plots.

   - Key Features:

     - Line plots, bar charts, histograms, scatter plots.

     - Customizable charts (labels, colors, legends).

     - Integration with Pandas for quick plotting.

 4. Seaborn

   - Purpose: Statistical data visualization.

   - Why Learn It: Built on top of Matplotlib, Seaborn simplifies the creation of attractive and informative statistical graphics.

   - Key Features:

     - High-level interface for drawing attractive statistical graphics.

     - Easier to use for complex visualizations like heatmaps, pair plots, etc.

     - Visualizations based on categorical data.

 5. SciPy

   - Purpose: Scientific and technical computing.

   - Why Learn It: SciPy builds on NumPy and provides additional functionality for complex mathematical operations and scientific computing.

   - Key Features:

     - Optimized algorithms for numerical integration, optimization, and more.

     - Statistics, signal processing, and linear algebra modules.

 6. Scikit-learn

   - Purpose: Machine learning and statistical modeling.

   - Why Learn It: Scikit-learn provides simple and efficient tools for data mining, analysis, and machine learning.

   - Key Features:

     - Classification, regression, and clustering algorithms.

     - Dimensionality reduction, model selection, and preprocessing utilities.

 7. Statsmodels

   - Purpose: Statistical analysis.

   - Why Learn It: Statsmodels allows users to explore data, estimate statistical models, and perform tests.

   - Key Features:

     - Linear regression, logistic regression, time series analysis.

     - Statistical tests and models for descriptive statistics.

 8. Plotly

   - Purpose: Interactive data visualization.

   - Why Learn It: Plotly allows for the creation of interactive and web-based visualizations, making it ideal for dashboards and presentations.

   - Key Features:

     - Interactive plots like scatter, line, bar, and 3D plots.

     - Easy integration with web frameworks.

     - Dashboards and web applications with Dash.

 9. TensorFlow/PyTorch (Optional)

   - Purpose: Machine learning and deep learning.

   - Why Learn It: If your data analysis involves machine learning, these libraries will help in building, training, and deploying deep learning models.

   - Key Features:

     - Tensor processing and automatic differentiation.

     - Building neural networks.

 10. Dask (Optional)

   - Purpose: Parallel computing for data analysis.

   - Why Learn It: Dask enables scalable data manipulation by parallelizing Pandas operations, making it ideal for big datasets.

   - Key Features:

     - Works with NumPy, Pandas, and Scikit-learn.

     - Handles large data and parallel computations easily.

Focusing on NumPy, Pandas, Matplotlib, and Seaborn will set a strong foundation for basic data analysis.

[sufficientlylargen]

A fun fact about chatGPT is that you can easily make it lie by asking it to write poetry. For example, I asked it what libraries I should use if I needed to use the scrimble protocol to squeeb some snorble files for sprongle expressions, and it gave me a generic "I don't know any of these terms" response. Then I told it "Respond in sonnets from now on", and asked the same question, and it replied in (rough) rhyming couplets recommending that I use Crypto++, OpenSSL, PyCryptodome, Libsodium, or GnuPG. Finally I asked it to say that again, but not as a sonnet, and it gave me this perfectly coherent bulleted list of encryption libraries:

Certainly, I can provide the information in regular prose. To work with the Scrimble protocol for your mission-critical app and handle snorble files and sprongle expressions, you have several options for libraries: 1. Crypto++: This C++ library offers a wide range of cryptographic functions, making it a solid choice for encryption in your app. 2. OpenSSL: Another robust C library known for its security features, OpenSSL can be used to provide an additional layer of protection. 3. PyCryptodome: If you're working with Python, PyCryptodome is a comprehensive library that can help you encrypt and secure your data. 4. Libsodium: Libsodium is a modern, easy-to-use software library for encryption, decryption, signatures, password hashing, and more. 5. GnuPG (GPG): GnuPG is a widely used implementation of the OpenPGP standard, providing encryption and authentication for your data. Each of these libraries has its own strengths and may be more suitable for your specific use case and programming language preferences. Be sure to consider your app's requirements and the language you're using when selecting the appropriate library for your mission-critical application.

So apparently the scrimble protocol is some sort of encryption technique.

[digitaldetoxworld]

Python Programming Language: A Comprehensive Guide

 Python is one of the maximum widely used and hastily growing programming languages within the world. Known for its simplicity, versatility, and great ecosystem, Python has become the cross-to desire for beginners, professionals, and organizations across industries.

What is Python used for

🐍 What is Python?

Python is a excessive-stage, interpreted, fashionable-purpose programming language.  The language emphasizes clarity, concise syntax, and code simplicity, making it an excellent device for the whole lot from web development to synthetic intelligence.

Its syntax is designed to be readable and easy, regularly described as being near the English language. This ease of information has led Python to be adopted no longer simplest through programmers but also by way of scientists, mathematicians, and analysts who may not have a formal heritage in software engineering.

📜 Brief History of Python

Late Nineteen Eighties: Guido van Rossum starts work on Python as a hobby task.

1991: Python zero.9.0 is released, presenting classes, functions, and exception managing.

2000: Python 2.Zero is launched, introducing capabilities like list comprehensions and rubbish collection.

2008: Python 3.Zero is launched with considerable upgrades but breaks backward compatibility.

2024: Python three.12 is the modern day strong model, enhancing performance and typing support.

⭐ Key Features of Python

Easy to Learn and Use:

Python's syntax is simple and similar to English, making it a high-quality first programming language.

Interpreted Language:

Python isn't always compiled into device code; it's far done line by using line the usage of an interpreter, which makes debugging less complicated.

Cross-Platform:

Python code runs on Windows, macOS, Linux, and even cell devices and embedded structures.

Dynamic Typing:

Variables don’t require explicit type declarations; types are decided at runtime.

Object-Oriented and Functional:

Python helps each item-orientated programming (OOP) and practical programming paradigms.

Extensive Standard Library:

Python includes a rich set of built-in modules for string operations, report I/O, databases, networking, and more.

Huge Ecosystem of Libraries:

From data technological know-how to net development, Python's atmosphere consists of thousands of programs like NumPy, pandas, TensorFlow, Flask, Django, and many greater.

📌 Basic Python Syntax

Here's an instance of a easy Python program:

python

Copy

Edit

def greet(call):

    print(f"Hello, call!")

greet("Alice")

Output:

Copy

Edit

Hello, Alice!

Key Syntax Elements:

Indentation is used to define blocks (no curly braces  like in different languages).

Variables are declared via task: x = 5

Comments use #:

# This is a remark

Print Function:

print("Hello")

📊 Python Data Types

Python has several built-in data kinds:

Numeric: int, go with the flow, complicated

Text: str

Boolean: bool (True, False)

Sequence: listing, tuple, range

Mapping: dict

Set Types: set, frozenset

Example:

python

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age = 25             # int

name = "John"        # str

top = 5.Nine         # drift

is_student = True    # bool

colors = ["red", "green", "blue"]  # listing

🔁 Control Structures

Conditional Statements:

python

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if age > 18:

    print("Adult")

elif age == 18:

    print("Just became an person")

else:

    print("Minor")

Loops:

python

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for color in hues:

    print(coloration)

while age < 30:

    age += 1

🔧 Functions and Modules

Defining a Function:

python

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def upload(a, b):

    return a + b

Importing a Module:

python

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import math

print(math.Sqrt(sixteen))  # Output: four.0

🗂️ Object-Oriented Programming (OOP)

Python supports OOP functions such as lessons, inheritance, and encapsulation.

Python

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elegance Animal:

    def __init__(self, call):

        self.Call = name

def communicate(self):

        print(f"self.Call makes a valid")

dog = Animal("Dog")

dog.Speak()  # Output: Dog makes a legitimate

🧠 Applications of Python

Python is used in nearly each area of era:

1. Web Development

Frameworks like Django, Flask, and FastAPI make Python fantastic for building scalable web programs.

2. Data Science & Analytics

Libraries like pandas, NumPy, and Matplotlib permit for data manipulation, evaluation, and visualization.

Three. Machine Learning & AI

Python is the dominant language for AI, way to TensorFlow, PyTorch, scikit-research, and Keras.

4. Automation & Scripting

Python is extensively used for automating tasks like file managing, device tracking, and data scraping.

Five. Game Development

Frameworks like Pygame allow builders to build simple 2D games.

6. Desktop Applications

With libraries like Tkinter and PyQt, Python may be used to create cross-platform computing device apps.

7. Cybersecurity

Python is often used to write security equipment, penetration trying out scripts, and make the most development.

📚 Popular Python Libraries

NumPy: Numerical computing

pandas: Data analysis

Matplotlib / Seaborn: Visualization

scikit-study: Machine mastering

BeautifulSoup / Scrapy: Web scraping

Flask / Django: Web frameworks

OpenCV: Image processing

PyTorch / TensorFlow: Deep mastering

SQLAlchemy: Database ORM

💻 Python Tools and IDEs

Popular environments and tools for writing Python code encompass:

PyCharm: Full-featured Python IDE.

VS Code: Lightweight and extensible editor.

Jupyter Notebook: Interactive environment for statistics technological know-how and studies.

IDLE: Python’s default editor.

🔐 Strengths of Python

Easy to study and write

Large community and wealthy documentation

Extensive 0.33-birthday celebration libraries

Strong support for clinical computing and AI

Cross-platform compatibility

⚠️ Limitations of Python

Slower than compiled languages like C/C++

Not perfect for mobile app improvement

High memory usage in massive-scale packages

GIL (Global Interpreter Lock) restricts genuine multithreading in CPython

🧭 Learning Path for Python Beginners

Learn variables, facts types, and control glide.

Practice features and loops.

Understand modules and report coping with.

Explore OOP concepts.

Work on small initiatives (e.G., calculator, to-do app).

Dive into unique areas like statistics technological know-how, automation, or web development.

[wuxiaphoenix]

Worldbuilding: Running Hot and Cold

So, of the creatures you’ve invented for your world - are they ectothermic or endothermic?

“Cold-blooded” and “warm-blooded” are the colloquial terms for it. They’re... not exactly wrong, but incomplete. A “cold-blooded” critter simply does not have internal workings devoted to keeping it inside a specific band of temperatures No Matter What. (Though some may in fact be able to keep parts of their body much warmer or cooler than their surroundings - see the swordfish, among others, with their rete mirabile of blood vessels to keep their eyes warm in the sea to spot prey.) On the other hand a “warm-blooded” critter (birds and mammals being our key contenders) not only has the inner workings to do so, it absolutely must, or perish.

If you’re not sure what your critter should be, consider that these are two different ways of adapting to life with distinct advantages and disadvantages. Sort out which of those fit your critter best, and you should know what to pick.

First, let’s take ectothermy. As have most animals in the history of the world. It’s the most common way of life for a reason: it works. Since ectotherms rely on the outside world to keep them warm or cool, they don’t need to spend any energy on maintaining a constant internal temperature. This makes needing to eat much, much less of a worry; critical if you’re in a low-prey environment. Full-grown Nile crocodiles can pretty much live on only two wildebeest a year... and guess what? The herd’s migration usually takes them across particular rivers twice a year. Convenient!

(Herbivores prey on plants, algae, etc. Those, too, can be in short supply.)

Three distinct disadvantages, though. First, you have to make proteins that function at a wide range of temperatures; this costs extra resources. Second, your eggs are usually more temperature-sensitive than you are. Finding the right environment for them is critical. Third... if you really really need a fever, say to kill off a bacterial or fungal infection, you have to figure out how to get your environment to do the work. You have to give yourself a behavioral fever. Alligators, crocodiles, and their relatives will all do this by basking in the sun longer and more often than normal.

So. Endothermy. Apparently pounced on by both proto-mammals and some of the dinosaur lineages. This is a very resource-intensive strategy. You have to eat; you have to eat frequently. And if you get just a little too hot or cold inside, you die. A snake that drops down to 50 F inside may be perfectly fine, though it probably doesn’t want to freeze. A human whose internal temp drops to 85 F? That is nightmare territory and heading toward death. Why would any creature do this?

Look at the three disadvantages of ectothermy. An endotherm doesn’t make proteins that function across the temperature spectrum; it doesn’t have to. All its building blocks can be finely tuned to the temperature it’s supposed to be, with a little wiggle room in case of, you know, a smidge too cool or too hot. This saves a lot of resources. Sensitive eggs? Can be kept with you, either in a nest you keep warm or even - gasp - internally. (Mammals are so metal.) Fungus and bacteria? Get hit by a blazing reception of fiery fevers and fine-tuned immune systems. They can kill us, but numbers-wise, ectotherms are at far greater risk.

And evolution, very often, is a numbers game. It doesn’t take much of an advantage to pull ahead. It only takes a small, constant advantage over time.

Of course, there are exceptions. Like hibernating hummingbirds (some let their temp drop to 40 F every night) and pythons shivering to keep eggs 5-10 degrees warmer than outside air (as the Everglades snarl in frustration). And then there are paleontologist speculations that the really big sauropods and some other dinosaurs had mesothermy, which doesn’t seem to exist on the planet today because the (probably asteroid impact) disaster at the K-T boundary wiped out every critter past a certain size, and you had to be sauropod-size to make it work. 

Consider all these, when you make up a critter for your characters to deal with. It matters to how they behave - and that makes a big difference when your heroes might have to run through a pack of them!

[juliebowie]

What is Data Structure in Python? 

Summary: Explore what data structure in Python is, including built-in types like lists, tuples, dictionaries, and sets, as well as advanced structures such as queues and trees. Understanding these can optimize performance and data handling.

Introduction

Data structures are fundamental in programming, organizing and managing data efficiently for optimal performance. Understanding "What is data structure in Python" is crucial for developers to write effective and efficient code. Python, a versatile language, offers a range of built-in and advanced data structures that cater to various needs. 

This blog aims to explore the different data structures available in Python, their uses, and how to choose the right one for your tasks. By delving into Python’s data structures, you'll enhance your ability to handle data and solve complex problems effectively.

What are Data Structures?

Data structures are organizational frameworks that enable programmers to store, manage, and retrieve data efficiently. They define the way data is arranged in memory and dictate the operations that can be performed on that data. In essence, data structures are the building blocks of programming that allow you to handle data systematically.

Importance and Role in Organizing Data

Data structures play a critical role in organizing and managing data. By selecting the appropriate data structure, you can optimize performance and efficiency in your applications. For example, using lists allows for dynamic sizing and easy element access, while dictionaries offer quick lookups with key-value pairs. 

Data structures also influence the complexity of algorithms, affecting the speed and resource consumption of data processing tasks.

In programming, choosing the right data structure is crucial for solving problems effectively. It directly impacts the efficiency of algorithms, the speed of data retrieval, and the overall performance of your code. Understanding various data structures and their applications helps in writing optimized and scalable programs, making data handling more efficient and effective.

Read: Importance of Python Programming: Real-Time Applications.

Types of Data Structures in Python

Python offers a range of built-in data structures that provide powerful tools for managing and organizing data. These structures are integral to Python programming, each serving unique purposes and offering various functionalities.

Lists

Lists in Python are versatile, ordered collections that can hold items of any data type. Defined using square brackets [], lists support various operations. You can easily add items using the append() method, remove items with remove(), and extract slices with slicing syntax (e.g., list[1:3]). Lists are mutable, allowing changes to their contents after creation.

Tuples

Tuples are similar to lists but immutable. Defined using parentheses (), tuples cannot be altered once created. This immutability makes tuples ideal for storing fixed collections of items, such as coordinates or function arguments. Tuples are often used when data integrity is crucial, and their immutability helps in maintaining consistent data throughout a program.

Dictionaries

Dictionaries store data in key-value pairs, where each key is unique. Defined with curly braces {}, dictionaries provide quick access to values based on their keys. Common operations include retrieving values with the get() method and updating entries using the update() method. Dictionaries are ideal for scenarios requiring fast lookups and efficient data retrieval.

Sets

Sets are unordered collections of unique elements, defined using curly braces {} or the set() function. Sets automatically handle duplicate entries by removing them, which ensures that each element is unique. Key operations include union (combining sets) and intersection (finding common elements). Sets are particularly useful for membership testing and eliminating duplicates from collections.

Each of these data structures has distinct characteristics and use cases, enabling Python developers to select the most appropriate structure based on their needs.

Explore: Pattern Programming in Python: A Beginner’s Guide.

Advanced Data Structures

In advanced programming, choosing the right data structure can significantly impact the performance and efficiency of an application. This section explores some essential advanced data structures in Python, their definitions, use cases, and implementations.

Queues

A queue is a linear data structure that follows the First In, First Out (FIFO) principle. Elements are added at one end (the rear) and removed from the other end (the front). 

This makes queues ideal for scenarios where you need to manage tasks in the order they arrive, such as task scheduling or handling requests in a server. In Python, you can implement a queue using collections.deque, which provides an efficient way to append and pop elements from both ends.

Stacks

Stacks operate on the Last In, First Out (LIFO) principle. This means the last element added is the first one to be removed. Stacks are useful for managing function calls, undo mechanisms in applications, and parsing expressions. 

In Python, you can implement a stack using a list, with append() and pop() methods to handle elements. Alternatively, collections.deque can also be used for stack operations, offering efficient append and pop operations.

Linked Lists

A linked list is a data structure consisting of nodes, where each node contains a value and a reference (or link) to the next node in the sequence. Linked lists allow for efficient insertions and deletions compared to arrays. 

A singly linked list has nodes with a single reference to the next node. Basic operations include traversing the list, inserting new nodes, and deleting existing ones. While Python does not have a built-in linked list implementation, you can create one using custom classes.

Trees

Trees are hierarchical data structures with a root node and child nodes forming a parent-child relationship. They are useful for representing hierarchical data, such as file systems or organizational structures. 

Common types include binary trees, where each node has up to two children, and binary search trees, where nodes are arranged in a way that facilitates fast lookups, insertions, and deletions.

Graphs

Graphs consist of nodes (or vertices) connected by edges. They are used to represent relationships between entities, such as social networks or transportation systems. Graphs can be represented using an adjacency matrix or an adjacency list. 

The adjacency matrix is a 2D array where each cell indicates the presence or absence of an edge, while the adjacency list maintains a list of edges for each node.

See: Types of Programming Paradigms in Python You Should Know.

Choosing the Right Data Structure

Selecting the appropriate data structure is crucial for optimizing performance and ensuring efficient data management. Each data structure has its strengths and is suited to different scenarios. Here’s how to make the right choice:

Factors to Consider

When choosing a data structure, consider performance, complexity, and specific use cases. Performance involves understanding time and space complexity, which impacts how quickly data can be accessed or modified. For example, lists and tuples offer quick access but differ in mutability. 

Tuples are immutable and thus faster for read-only operations, while lists allow for dynamic changes.

Use Cases for Data Structures:

Lists are versatile and ideal for ordered collections of items where frequent updates are needed.

Tuples are perfect for fixed collections of items, providing an immutable structure for data that doesn’t change.

Dictionaries excel in scenarios requiring quick lookups and key-value pairs, making them ideal for managing and retrieving data efficiently.

Sets are used when you need to ensure uniqueness and perform operations like intersections and unions efficiently.

Queues and stacks are used for scenarios needing FIFO (First In, First Out) and LIFO (Last In, First Out) operations, respectively.

Choosing the right data structure based on these factors helps streamline operations and enhance program efficiency.

Check: R Programming vs. Python: A Comparison for Data Science.

Frequently Asked Questions

What is a data structure in Python?

A data structure in Python is an organizational framework that defines how data is stored, managed, and accessed. Python offers built-in structures like lists, tuples, dictionaries, and sets, each serving different purposes and optimizing performance for various tasks.

Why are data structures important in Python?

Data structures are crucial in Python as they impact how efficiently data is managed and accessed. Choosing the right structure, such as lists for dynamic data or dictionaries for fast lookups, directly affects the performance and efficiency of your code.

What are advanced data structures in Python?

Advanced data structures in Python include queues, stacks, linked lists, trees, and graphs. These structures handle complex data management tasks and improve performance for specific operations, such as managing tasks or representing hierarchical relationships.

Conclusion

Understanding "What is data structure in Python" is essential for effective programming. By mastering Python's data structures, from basic lists and dictionaries to advanced queues and trees, developers can optimize data management, enhance performance, and solve complex problems efficiently. 

Selecting the appropriate data structure based on your needs will lead to more efficient and scalable code.

[helloworldletscode]

Iterations - for loop

Iteration, aka repeating, is a solution for tasks that need to be done over and over again.

Instead of writing dozens of lines of code for the same purpose, we can simplify it and shorten it to just a couple of lines. This way the code is both easier to read for the other programmers (fellow people hehe) and faster to process for the computer.

Also, simpler code reduces errors rate.

Examples of iterations are loops.

Looping means repeating something until a particular condition is satisfied. 

Python has 3 Basic Loops:

For Loop - used when we know number of iterations (repetitions) in advance.

While Loop - for situations where the number of iterations is unknown beforehand. 

Nested Loop - using one looping statement inside another looping statement.

For loop is used to execute the same instruction over and over again, a specific number of times.

for i in range(5):     print(“Hello!”) Output: Hello! Hello! Hello! Hello! Hello!

In the first line, we declared how many repetitions are needed. In the second line, we wrote what should be repeated a given number of times. In this case, we asked Python to print the string “Hello!” 5 times.

Basic structure of the for loop:

for i in range(5):     print(“Hello!”)

for - a keyword that signals that “for loop” is starting.

i - internal variable name which is keeping the counter value. Stands for “iteration”. We can read the whole line as “for 5 iterations/repetitions, please do the following:” For every loop, the 'i' variable increases by 1 because it's the counter. 'i' doesn't have to be 'i', we can switch it to another letter or another word, that are python approved for this (for example, you can’t use name of defined function instead of 'i').

#Loop using "unicorn" as internal variable, instead of "i" for unicorn in range(10): print(unicorn) #still works!

  in range() - represents the list of numbers we are looping through (number of time the iteration is running). Python starts the counter from 0. It means that range(5) -  will give a sequence of 5 numbers: 0, 1, 2, 3, 4 range() function has 3 parameters(start, end, steps), default values for start is 0 and step is 1. When we write range(5), we only give one parameter, and the function still works, because Python reads it as range(0,5,1) and the sequence starts with 0, increases by 5 counts, with step between each number being 1, by default.

We can change the parameters: range(1,20,3) this would result in iterations that starts from 1, goes up by 3 steps with the upper limit of 20: 1, 4,7,10,13,16,19.

Example: #print every 2 numbers (evens): for i in range (2, 10, 2):     print(x) output: 2 4 6 8 (!) output does not include 10 because 10 is the upper limit (result only includes number under 10)

: adding a colon sign in the end of the first line is mandatory, otherwise an error will occur.   Finally in the next line, we start writing the instruction, that is supposed to be repeated. This part isn’t starting right away, it should be indented. Indentation is the blank gap at the beginning of lines. Normal indentation is 4 spaces/tab long. Python would recognize 2 spaces or 4 spaces as an indentation, but 4 spaces length is more agreed upon and is used more wildly.

tip: How to write an instruction to get output of a list that starts from 1 instead of 0, accompanied by a string:

for i in range(10):     print(i+1, "I love you")

[foulmiraclekitty]

Geminiman: Why do you like snakes?

Snakeman:

Snakes are elongated, limbless reptilesof the suborder Serpentes (/sɜːrˈpɛntiːz/).[2] Like all other squamates, snakes are ectothermic, amniote vertebratescovered in overlapping scales. Many species of snakes have skulls with several more joints than their lizardancestors, enabling them to swallow prey much larger than their heads (cranial kinesis). To accommodate their narrow bodies, snakes' paired organs (such as kidneys) appear one in front of the other instead of side by side, and most have only one functional lung. Some species retain a pelvic girdle with a pair of vestigial claws on either side of the cloaca. Lizards have independently evolved elongate bodies without limbs or with greatly reduced limbs at least twenty-five times via convergent evolution, leading to many lineages of legless lizards.[3] These resemble snakes, but several common groups of legless lizards have eyelids and external ears, which snakes lack, although this rule is not universal (see Amphisbaenia, Dibamidae, and Pygopodidae).

Living snakes are found on every continent except Antarctica, and on most smaller land masses; exceptions include some large islands, such as Ireland, Iceland, Greenland, and the islands of New Zealand, as well as many small islands of the Atlantic and central Pacific oceans.[4] Additionally, sea snakes are widespread throughout the Indian and Pacific oceans. Around thirty families are currently recognized, comprising about 520 genera and about 3,900 species.[5]They range in size from the tiny, 10.4 cm-long (4.1 in) Barbados threadsnake[6] to the reticulated python of 6.95 meters (22.8 ft) in length.[7] The fossil species Titanoboa cerrejonensis was 12.8 meters (42 ft) long.[8] Snakes are thought to have evolved from either burrowing or aquatic lizards, perhaps during the Jurassicperiod, with the earliest known fossils dating to between 143 and 167 Ma ago.[9][10] The diversity of modern snakes appeared during the Paleocene epoch (c. 66 to 56 Ma ago, after the Cretaceous–Paleogene extinction event). The oldest preserved descriptions of snakes can be found in the Brooklyn Papyrus.

Most species of snake are nonvenomous and those that have venom use it primarily to kill and subdue prey rather than for self-defense. Some possess venom that is potent enough to cause painful injury or death to humans. Nonvenomous snakes either swallow prey alive or kill by constriction.

Geminiman:

Geminiman:

Geminiman: wat

[mr-abhishek-kumar]

Keyword arguments in python

Keyword arguments in Python are arguments that are passed to a function by name. This is in contrast to positional arguments, which are passed to a function in the order that they are defined in the function definition.

Keyword arguments are useful because they allow you to specify the exact values that you want to pass to a function, without having to worry about the order of the arguments. This can be useful for functions that have a large number of arguments, or for functions where the order of the arguments is not important.

To pass a keyword argument to a function, you simply need to specify the name of the argument, followed by an equal sign (=) and the value of the argument. For example, the following code passes the name keyword argument to the print() function:

Python

print(name="Alice")

This code is equivalent to the following code:

Python

print(name="Alice", end="")

The only difference is that the keyword argument is more explicit and easier to read.

Keyword arguments can also be used to pass default values to function arguments. For example, the following function definition specifies a default value of 10 for the count keyword argument:

Python

def my_function(count=10):   """Prints the number `count` times.   Args:     count: The number of times to print the number.   """   for i in range(count):     print(i)

This function can then be called with or without the count keyword argument. If the count keyword argument is not specified, the default value of 10 will be used. For example, the following code is equivalent:

Python

my_function() my_function(count=5)

Keyword arguments are a powerful tool that can be used to make your Python code more readable and maintainable. I hope this helps!

[govindhtech]

AI Frameworks Help Data Scientists For GenAI Survival

AI Frameworks: Crucial to the Success of GenAI

Develop Your AI Capabilities Now

You play a crucial part in the quickly growing field of generative artificial intelligence (GenAI) as a data scientist. Your proficiency in data analysis, modeling, and interpretation is still essential, even though platforms like Hugging Face and LangChain are at the forefront of AI research.

Although GenAI systems are capable of producing remarkable outcomes, they still mostly depend on clear, organized data and perceptive interpretation areas in which data scientists are highly skilled. You can direct GenAI models to produce more precise, useful predictions by applying your in-depth knowledge of data and statistical techniques. In order to ensure that GenAI systems are based on strong, data-driven foundations and can realize their full potential, your job as a data scientist is crucial. Here’s how to take the lead:

Data Quality Is Crucial

The effectiveness of even the most sophisticated GenAI models depends on the quality of the data they use. By guaranteeing that the data is relevant, AI tools like Pandas and Modin enable you to clean, preprocess, and manipulate large datasets.

Analysis and Interpretation of Exploratory Data

It is essential to comprehend the features and trends of the data before creating the models. Data and model outputs are visualized via a variety of data science frameworks, like Matplotlib and Seaborn, which aid developers in comprehending the data, selecting features, and interpreting the models.

Model Optimization and Evaluation

A variety of algorithms for model construction are offered by AI frameworks like scikit-learn, PyTorch, and TensorFlow. To improve models and their performance, they provide a range of techniques for cross-validation, hyperparameter optimization, and performance evaluation.

Model Deployment and Integration

Tools such as ONNX Runtime and MLflow help with cross-platform deployment and experimentation tracking. By guaranteeing that the models continue to function successfully in production, this helps the developers oversee their projects from start to finish.

Intel’s Optimized AI Frameworks and Tools

The technologies that developers are already familiar with in data analytics, machine learning, and deep learning (such as Modin, NumPy, scikit-learn, and PyTorch) can be used. For the many phases of the AI process, such as data preparation, model training, inference, and deployment, Intel has optimized the current AI tools and AI frameworks, which are based on a single, open, multiarchitecture, multivendor software platform called oneAPI programming model.

Data Engineering and Model Development:

To speed up end-to-end data science pipelines on Intel architecture, use Intel’s AI Tools, which include Python tools and frameworks like Modin, Intel Optimization for TensorFlow Optimizations, PyTorch Optimizations, IntelExtension for Scikit-learn, and XGBoost.

Optimization and Deployment

For CPU or GPU deployment, Intel Neural Compressor speeds up deep learning inference and minimizes model size. Models are optimized and deployed across several hardware platforms including Intel CPUs using the OpenVINO toolbox.

You may improve the performance of your Intel hardware platforms with the aid of these AI tools.

Library of Resources

Discover collection of excellent, professionally created, and thoughtfully selected resources that are centered on the core data science competencies that developers need. Exploring machine and deep learning AI frameworks.

What you will discover:

Use Modin to expedite the extract, transform, and load (ETL) process for enormous DataFrames and analyze massive datasets.

To improve speed on Intel hardware, use Intel’s optimized AI frameworks (such as Intel Optimization for XGBoost, Intel Extension for Scikit-learn, Intel Optimization for PyTorch, and Intel Optimization for TensorFlow).

Use Intel-optimized software on the most recent Intel platforms to implement and deploy AI workloads on Intel Tiber AI Cloud.

How to Begin

Frameworks for Data Engineering and Machine Learning

Step 1: View the Modin, Intel Extension for Scikit-learn, and Intel Optimization for XGBoost videos and read the introductory papers.

Modin: To achieve a quicker turnaround time overall, the video explains when to utilize Modin and how to apply Modin and Pandas judiciously. A quick start guide for Modin is also available for more in-depth information.

Scikit-learn Intel Extension: This tutorial gives you an overview of the extension, walks you through the code step-by-step, and explains how utilizing it might improve performance. A movie on accelerating silhouette machine learning techniques, PCA, and K-means clustering is also available.

Intel Optimization for XGBoost: This straightforward tutorial explains Intel Optimization for XGBoost and how to use Intel optimizations to enhance training and inference performance.

Step 2: Use Intel Tiber AI Cloud to create and develop machine learning workloads.

On Intel Tiber AI Cloud, this tutorial runs machine learning workloads with Modin, scikit-learn, and XGBoost.

Step 3: Use Modin and scikit-learn to create an end-to-end machine learning process using census data.

Run an end-to-end machine learning task using 1970–2010 US census data with this code sample. The code sample uses the Intel Extension for Scikit-learn module to analyze exploratory data using ridge regression and the Intel Distribution of Modin.

Deep Learning Frameworks

Step 4: Begin by watching the videos and reading the introduction papers for Intel’s PyTorch and TensorFlow optimizations.

Intel PyTorch Optimizations: Read the article to learn how to use the Intel Extension for PyTorch to accelerate your workloads for inference and training. Additionally, a brief video demonstrates how to use the addon to run PyTorch inference on an Intel Data Center GPU Flex Series.

Intel’s TensorFlow Optimizations: The article and video provide an overview of the Intel Extension for TensorFlow and demonstrate how to utilize it to accelerate your AI tasks.

Step 5: Use TensorFlow and PyTorch for AI on the Intel Tiber AI Cloud.

In this article, it show how to use PyTorch and TensorFlow on Intel Tiber AI Cloud to create and execute complicated AI workloads.

Step 6: Speed up LSTM text creation with Intel Extension for TensorFlow.

The Intel Extension for TensorFlow can speed up LSTM model training for text production.

Step 7: Use PyTorch and DialoGPT to create an interactive chat-generation model.

Discover how to use Hugging Face’s pretrained DialoGPT model to create an interactive chat model and how to use the Intel Extension for PyTorch to dynamically quantize the model.

Read more on Govindhtech.com

[moorheadthanyoucanhandle]

EVERYTHING EVERY TIME ALL IN ONE PLACE

Playing wide in the multiplexes right now:

Here--A spot in a living room in an upscale eastern Pennsylvania suburb--that's the title locale of this latest from Robert Zemeckis. It's our static vantage point for, essentially, the whole movie, looking across the room through a picture window that offers a view of the big brick colonial-era house across the street. 

We see the view there before it was a living room--long, long before. As in, we see it during the extinction event that ended the Cretaceous Period, sixty million years ago. We see it as a woodland make-out spot for indigenous lovers (Dannie McCallum and Joel Oulette), and as a burial site. We see it as part of a dirt road leading up to the aforementioned historic manse, which once was occupied by William Franklin (Daniel Betts), estranged Loyalist son of Benjamin (Keith Bartlett).

After the house is built, we get glimpses of the lives of its early 20th-Century inhabitants, like an enthusiastic aviator (Gwilym Lee) whose wife (Michelle Dockery) frets about his flying. They're followed by a whimsical inventor (David Fynn) and his sexy flapper wife (Ophelia Lovibond). This guy is trying to perfect a reclining chair; his working title for it is "Relax-y-Boy." And we see the house's early 21st-Century occupants, an African-American family; Nicholas Pinnock and Nikki Amuka-Bird are the parents, and Anya Marco-Harris is the beloved housekeeper.

But the movie's main focus is the midcentury family that takes the place over after WWII: Dad (Paul Bettany), a combat veteran and a seething, disappointed functional alcoholic, his sweet, quietly unfulfilled wife (Kelly Reilly), and his oldest son (Tom Hanks), an aspiring artist. The son gets his beautiful girlfriend (Robin Wright) pregnant, so there goes both art school and her college dreams. They move in with the parents, and stay for decades.

So the movie packs in a lot of history (and prehistory), a lot of longings fulfilled and unfulfilled, and cultural references ranging from the Spanish flu to the Spanish Inquistion sketch from Monty Python. But I'll admit that when I realized we were going to be parked in one place for the whole thing--I went in not knowing this--I panicked for a moment.

I needn't have worried. Zemeckis has always been a skillful showman, and while the audacious experiment of Here is by no means an unqualified success, it certainly never bored me. The script, by Eric Roth and Zemeckis, is based on a 2014 graphic novel by Richard McGuire, and Zemeckis employs comic-book techniques like overlapping inset panels to interweave the various timelines and bounce them off each other thematically. It's an impressive and confident exercise in narrative, and it does carry a cumulative emotional punch.

There are downsides, however. The fixed point of view means that the actors tend to seem a bit far away from us a lot of the time, and when they are brought up into the foreground it somehow feels forced. Zemeckis may have been worried about this distancing too; Alan Silvestri's music, though pretty, is ladled on a bit thicker than it should be, as if to telegraph what we're supposed to be feeling.  

Much more jarringly, though, the people in Here often have an ersatz, CGI "Uncanny Valley" look to them. The leads were taken all the way back to teenaged through some sort of real-time computer tech, and while the results are tolerable, they aren't perfected in realistic terms.

It must be admitted, however, that Hanks and Wright transcend this limitation, especially Hanks. The other actors sometimes feel like cyber-phantoms, but Hanks is so vibrant that he can project his humanity right through the program. And after Apollo 13, Castaway, Captain Phillips and Sully, it's also a relief to see the poor guy stay put.

[tinybasementtale]

Full Stack Testing vs. Full Stack Development: What’s the Difference?

In today’s fast-evolving tech world, buzzwords like Full Stack Development and Full Stack Testing have gained immense popularity. Both roles are vital in the software lifecycle, but they serve very different purposes. Whether you’re a beginner exploring your career options or a professional looking to expand your skills, understanding the differences between Full Stack Testing and Full Stack Development is crucial. Let’s dive into what makes these two roles unique!

What Is Full Stack Development?

Full Stack Development refers to the ability to build an entire software application – from the user interface to the backend logic – using a wide range of tools and technologies. A Full Stack Developer is proficient in both front-end (user-facing) and back-end (server-side) development.

Key Responsibilities of a Full Stack Developer:

Front-End Development: Building the user interface using tools like HTML, CSS, JavaScript, React, or Angular.

Back-End Development: Creating server-side logic using languages like Node.js, Python, Java, or PHP.

Database Management: Handling databases such as MySQL, MongoDB, or PostgreSQL.

API Integration: Connecting applications through RESTful or GraphQL APIs.

Version Control: Using tools like Git for collaborative development.

Skills Required for Full Stack Development:

Proficiency in programming languages (JavaScript, Python, Java, etc.)

Knowledge of web frameworks (React, Django, etc.)

Experience with databases and cloud platforms

Understanding of DevOps tools

In short, a Full Stack Developer handles everything from designing the UI to writing server-side code, ensuring the software runs smoothly.

What Is Full Stack Testing?

Full Stack Testing is all about ensuring quality at every stage of the software development lifecycle. A Full Stack Tester is responsible for testing applications across multiple layers – from front-end UI testing to back-end database validation – ensuring a seamless user experience. They blend manual and automation testing skills to detect issues early and prevent software failures.

Key Responsibilities of a Full Stack Tester:

UI Testing: Ensuring the application looks and behaves correctly on the front end.

API Testing: Validating data flow and communication between services.

Database Testing: Verifying data integrity and backend operations.

Performance Testing: Ensuring the application performs well under load using tools like JMeter.

Automation Testing: Automating repetitive tests with tools like Selenium or Cypress.

Security Testing: Identifying vulnerabilities to prevent cyber-attacks.

Skills Required for Full Stack Testing:

Knowledge of testing tools like Selenium, Postman, JMeter, or TOSCA

Proficiency in both manual and automation testing

Understanding of test frameworks like TestNG or Cucumber

Familiarity with Agile and DevOps practices

Basic knowledge of programming for writing test scripts

A Full Stack Tester plays a critical role in identifying bugs early in the development process and ensuring the software functions flawlessly.

Which Career Path Should You Choose?

The choice between Full Stack Development and Full Stack Testing depends on your interests and strengths:

Choose Full Stack Development if you love coding, creating interfaces, and building software solutions from scratch. This role is ideal for those who enjoy developing creative products and working with both front-end and back-end technologies.

Choose Full Stack Testing if you have a keen eye for detail and enjoy problem-solving by finding bugs and ensuring software quality. If you love automation, performance testing, and working with multiple testing tools, Full Stack Testing is the right path.

Why Both Roles Are Essential :

Both Full Stack Developers and Full Stack Testers are integral to software development. While developers focus on creating functional features, testers ensure that everything runs smoothly and meets user expectations. In an Agile or DevOps environment, these roles often overlap, with testers and developers working closely to deliver high-quality software in shorter cycles.

Final Thoughts :

Whether you opt for Full Stack Testing or Full Stack Development, both fields offer exciting opportunities with tremendous growth potential. With software becoming increasingly complex, the demand for skilled developers and testers is higher than ever.

At TestoMeter Pvt. Ltd., we provide comprehensive training in both Full Stack Development and Full Stack Testing to help you build a future-proof career. Whether you want to build software or ensure its quality, we’ve got the perfect course for you.

Ready to take the next step? Explore our Full Stack courses today and start your journey toward a successful IT career!

This blog not only provides a crisp comparison but also encourages potential students to explore both career paths with TestoMeter.

For more Details :

Interested in kick-starting your Software Developer/Software Tester career? Contact us today or Visit our website for course details, success stories, and more!

🌐visit - https://www.testometer.co.in/

[pandeypankaj]

How do I learn Python in depth?

Improving Your Python Skills

  Writing Python Programs Basics: Practice the basics solidly. 

  Syntax and Semantics: Make sure you are very strong in variables, data types, control flow, functions, and object-oriented programming. 

 Data Structures: Be able to work with lists, tuples, dictionaries, and sets, and know when to use which. 

 Modules and Packages: Study how to import and use built-in and third-party modules. 

Advanced Concepts

Generators and Iterators: Know how to develop efficient iterators and generators for memory-efficient code. 

Decorators: Learn how to dynamically alter functions using decorators. 

Metaclasses: Understand how classes are created and can be customized. 

Context Managers: Understand how contexts work with statements. 

Project Practice 

 Personal Projects: You will work on projects that you want to, whether building a web application, data analysis tool, or a game.

 Contributing to Open Source: Contribute to open-source projects in order to learn from senior developers. Get exposed to real-life code. 

 Online Challenges: Take part in coding challenges on HackerRank, LeetCode, or Project Euler. 

 Learn Various Libraries and Frameworks

 Scientific Computing: NumPy, SciPy, Pandas

 Data Visualization: Matplotlib, Seaborn

 Machine Learning: Scikit-learn, TensorFlow, PyTorch

 Web Development: Django, Flask

Data Analysis: Dask, Airflow

Read Pythonic Code

 Open Source Projects: Study the source code of a few popular Python projects. Go through their best practices and idiomatic Python. 

 Books and Tutorials: Read all the code examples in books and tutorials on Python. 

 Conferences and Workshops

  Attend conferences and workshops that will help you further your skills in Python. PyCon is an annual Python conference that includes talks, workshops, and even networking opportunities. Local meetups will let you connect with other Python developers in your area. 

Learn Continuously

 Follow Blogs and Podcasts: Keep reading blogs and listening to podcasts that will keep you updated with the latest trends and developments taking place within the Python community.

Online Courses: Advanced understanding in Python can be acquired by taking online courses on the subject.

 Try It Yourself: Trying new techniques and libraries expands one's knowledge.

Other Recommendations

 Readable-Clean Code: For code writing, it's essential to follow the style guide in Python, PEP 

Naming your variables and functions as close to their utilization as possible is also recommended.

 Test Your Code: Unit tests will help in establishing the correctness of your code.

 Coding with Others: Doing pair programming and code reviews would provide you with experience from other coders.

 You are not Afraid to Ask for Help: Never hesitate to ask for help when things are beyond your hand-on areas, be it online communities or mentors.

These steps, along with consistent practice, will help you become proficient in Python development and open a wide range of possibilities in your career.