#Python List Data Structure

[trendingnow3-blog]

Day-4: Unlocking the Power of Randomization in Python Lists

Python Boot Camp 2023 - Day-4

Randomization and Python List Introduction Randomization is an essential concept in computer programming and data analysis. It involves the process of generating random elements or sequences that have an equal chance of being selected. In Python, randomization is a powerful tool that allows developers to introduce an element of unpredictability and make programs more dynamic. This article…

[network-cloud-service]

Functions in Python

Functions in Python A function in Python is a block of code that performs a specific task and returns a result. Functions help break down a program into smaller, more manageable pieces and make the code easier to read, test, and maintain. Functions define using the def keyword, followed by the name of the function, a set of parentheses, and a colon. The code within the function indent under the…

[devsenv]

 "Mastering C Programming: A Comprehensive Guide for Beginners and Experts"

Understanding C Programming: The Foundation of Modern Software Development

C programming, developed in the early 1970s by Dennis Ritchie at Bell Labs, is one of the most enduring and influential programming languages in the history of computing. Its design has directly influenced many other popular languages such as C++, Java, and Python. Despite the emergence of numerous high-level programming languages, C remains a cornerstone in both academic and professional software development environments due to its efficiency, control, and widespread use in system-level programming.

The Essence of C Programming

C is a general-purpose programming language that is particularly well-suited for system programming, including the development of operating systems, compilers, and embedded systems. It provides low-level access to memory through the use of pointers, which allows for efficient manipulation of data and memory management. This is one reason why the UNIX operating system, originally written in assembly language, was eventually rewritten in C.

The syntax of C is concise and powerful. Its constructs map efficiently to typical machine instructions, which makes it an ideal language for performance-critical applications. The simplicity of C's syntax, compared to other languages at the time of its creation, made it easier to learn and use while still being robust enough to handle complex programming tasks.

Key Features of C

Portability: One of the primary reasons for C's enduring popularity is its portability. Programs written in C can be compiled and run on different types of computers with little or no modification, which is crucial in today's diverse computing environments.

Efficiency: C is known for producing fast and efficient code. Its low-level capabilities allow programmers to write code that executes quickly and uses system resources judiciously. This makes it an excellent choice for performance-sensitive applications.

Modularity: C encourages the modular design of software. Functions in C can be defined and called independently, which makes code easier to understand, maintain, and reuse. This modularity is further enhanced by the use of header files and libraries.

Rich Library Support: The standard C library provides a rich set of built-in functions for performing common tasks, such as input/output operations, string manipulation, mathematical computations, and memory management. This extensive library support accelerates the development process.

C in Modern Context

Despite being over four decades old, C remains relevant in modern computing. It is extensively used in developing firmware and embedded systems, where direct hardware manipulation is required. The language's influence is also evident in many contemporary programming languages. For example, C++ was developed as an extension of C to include object-oriented features while maintaining C's efficiency and low-level capabilities.

Learning C provides a strong foundation for understanding computer science concepts and principles. Many educational institutions use C as an introductory programming language because it teaches fundamental programming techniques, such as variable manipulation, control structures, data structures, and algorithm development, which are applicable to learning other languages.

Conclusion

C programming is more than just a language; it is a gateway to understanding the inner workings of computers and software development. Its efficiency, control, portability, and rich library support make it an indispensable tool for programmers. Whether used in system programming, embedded systems, or as a stepping stone to other languages, C continues to be a fundamental part of the software development landscape. Its influence on modern programming practices and its role in the development of many other languages underscore its importance and enduring legacy in the world of computing.

[codewithnazam]

DataFrame in Pandas: Guide to Creating Awesome DataFrames

Explore how to create a dataframe in Pandas, including data input methods, customization options, and practical examples.

Data analysis used to be a daunting task, reserved for statisticians and mathematicians. But with the rise of powerful tools like Python and its fantastic library, Pandas, anyone can become a data whiz! Pandas, in particular, shines with its DataFrames, these nifty tables that organize and manipulate data like magic. But where do you start? Fear not, fellow data enthusiast, for this guide will…

[sad--tree]

what do u do when u tell ur parent in no uncertain terms Thank You For The Offer But I Do Not Want A Tutor For This Course It Will Not Help And I Am Deeply Uncomfortable With It Do Not Get Me One

and then they go and book u with an online tutor. without asking. what the fuck.

[developerhelp]

What is the difference between Python's list methods append and extend

What is the difference between Python’s list methods append and extend

append appends a specified object at the end of the list: >>> x = [1, 2, 3] >>> x.append([4, 5]) >>> print(x) [1, 2, 3, [4, 5]] extend extends the list by appending elements from the specified iterable: >>> x = [1, 2, 3] >>> x.extend([4, 5]) >>> print(x) [1, 2, 3, 4, 5]

[snowcoding]

hi. i had a very similar experince to trying to look through the code camp scams and everything online and not living near anything useful. if you can find an online real college thats what i did, granted its a community college and an associates but. other than that, don't sleep on utilizing chatgpt to teach you. thats how i learn all of my material. you can ask it questions or say "can you teach me about x", and if you dont like its response you can say things like "make that more simple" or "make that interactive". but helpful tip, all programming languages basically do the same things and work in very, very similar ways. if you just learn the fundamentals of programming you can just translate that to any language. in my opinion, the basics to learn are: the structures of programming (sequential, conditional, iterative), variables, datatypes (integer, string, float, etc)(in python those are it), conditional statements(these are those if-else things you see), iterative aka loops(do..while, for x in list, do until, etc), functions(keep em one purpose), passing data. i would say these are the fundamentals. every language does it (besides html bc thats not a programming language but just a mark up language), so once you know about the conditonal structure for example, just find out "how do i use this in x language". if you are learning python now, its a great language to learn about programming and you've probably realized by now that people most often use it in an object oriented way, but you don't have to and don't have to learn about classes or objects if you don't have the fundamentals down yet. i hope this helps and if you have any questions feel free to ask me

Oh I 100% agree with this advice. After looking for a long, long time, I realised the most legitimate courses were from 'real' colleges and education suppliers that offered 'brick n mortar' schooling as well as e-learning.

I'm definitely going to utilise the free resources online and then work towards building a profile and generally seeing what the jobs online look for and work towards that alongside the usual path of learning :)

Also, I love how supportive folk generally are in this area of learning. I knew it would be competitive, especially when it comes to getting a job in a year or so...but seeing folk lift each other up instead of put each other down is heart-warming on so many levels. It makes me think I've found my correct career path :)

[mirastudiesphysics]

That one post that mentions how studying is about playing with the material has really wormed its way inside my brain. I have two python classes that I need to understand and I'm making a jupyter notebook to list out data structures and plot the relevant Fits files. I feel like I'm learning the alphabet through blocks all over again.

[priya-joshi]

The Roadmap to Full Stack Developer Proficiency: A Comprehensive Guide

Embarking on the journey to becoming a full stack developer is an exhilarating endeavor filled with growth and challenges. Whether you're taking your first steps or seeking to elevate your skills, understanding the path ahead is crucial. In this detailed roadmap, we'll outline the stages of mastering full stack development, exploring essential milestones, competencies, and strategies to guide you through this enriching career journey.

Beginning the Journey: Novice Phase (0-6 Months)

As a novice, you're entering the realm of programming with a fresh perspective and eagerness to learn. This initial phase sets the groundwork for your progression as a full stack developer.

Grasping Programming Fundamentals:

Your journey commences with grasping the foundational elements of programming languages like HTML, CSS, and JavaScript. These are the cornerstone of web development and are essential for crafting dynamic and interactive web applications.

Familiarizing with Basic Data Structures and Algorithms:

To develop proficiency in programming, understanding fundamental data structures such as arrays, objects, and linked lists, along with algorithms like sorting and searching, is imperative. These concepts form the backbone of problem-solving in software development.

Exploring Essential Web Development Concepts:

During this phase, you'll delve into crucial web development concepts like client-server architecture, HTTP protocol, and the Document Object Model (DOM). Acquiring insights into the underlying mechanisms of web applications lays a strong foundation for tackling more intricate projects.

Advancing Forward: Intermediate Stage (6 Months - 2 Years)

As you progress beyond the basics, you'll transition into the intermediate stage, where you'll deepen your understanding and skills across various facets of full stack development.

Venturing into Backend Development:

In the intermediate stage, you'll venture into backend development, honing your proficiency in server-side languages like Node.js, Python, or Java. Here, you'll learn to construct robust server-side applications, manage data storage and retrieval, and implement authentication and authorization mechanisms.

Mastering Database Management:

A pivotal aspect of backend development is comprehending databases. You'll delve into relational databases like MySQL and PostgreSQL, as well as NoSQL databases like MongoDB. Proficiency in database management systems and design principles enables the creation of scalable and efficient applications.

Exploring Frontend Frameworks and Libraries:

In addition to backend development, you'll deepen your expertise in frontend technologies. You'll explore prominent frameworks and libraries such as React, Angular, or Vue.js, streamlining the creation of interactive and responsive user interfaces.

Learning Version Control with Git:

Version control is indispensable for collaborative software development. During this phase, you'll familiarize yourself with Git, a distributed version control system, to manage your codebase, track changes, and collaborate effectively with fellow developers.

Achieving Mastery: Advanced Phase (2+ Years)

As you ascend in your journey, you'll enter the advanced phase of full stack development, where you'll refine your skills, tackle intricate challenges, and delve into specialized domains of interest.

Designing Scalable Systems:

In the advanced stage, focus shifts to designing scalable systems capable of managing substantial volumes of traffic and data. You'll explore design patterns, scalability methodologies, and cloud computing platforms like AWS, Azure, or Google Cloud.

Embracing DevOps Practices:

DevOps practices play a pivotal role in contemporary software development. You'll delve into continuous integration and continuous deployment (CI/CD) pipelines, infrastructure as code (IaC), and containerization technologies such as Docker and Kubernetes.

Specializing in Niche Areas:

With experience, you may opt to specialize in specific domains of full stack development, whether it's frontend or backend development, mobile app development, or DevOps. Specialization enables you to deepen your expertise and pursue career avenues aligned with your passions and strengths.

Conclusion:

Becoming a proficient full stack developer is a transformative journey that demands dedication, resilience, and perpetual learning. By following the roadmap outlined in this guide and maintaining a curious and adaptable mindset, you'll navigate the complexities and opportunities inherent in the realm of full stack development. Remember, mastery isn't merely about acquiring technical skills but also about fostering collaboration, embracing innovation, and contributing meaningfully to the ever-evolving landscape of technology.

[the-coding-cat]

Project Introduction: Text Based Monopoly Game.

Look I'm just going to be frank with you, I am not the smartest individual, nor do I have much experience programming, but what I do have is the gall, the absolute nerve to believe that I can do anything even with very little experience. Some call it the Dunning-Kruger Effect, I like to call it a gift from the All Mighty.

This led me to idea of making a text based version of monopoly with about 2 hours worth of python tutorials, absolutely no understanding of data structures and algorithms, and the help of chatgpt.

So far I have already implemented:

Adding, removing, and naming player. With a required minimum of 2 players and cap of 6 players.

Allowing players to declare bankruptcy

Added a win state when there is only one player who is not bankrupt.

Display the player number, name, and current funds.

Random dice rolls.

Allowing players to move within 40 spaces.

Display on which numbered space the player is on the board along with the name of the space that they are located.

Player automatically collect $200 when they pass go.

They can also end their turn.

What I need to implement:

Buy properties, selling properties, and collecting rent.

Morgeting properties

Buying houses

Chance and community cards.

Jail

Trading

View Current Properties

There are probably other things that need to be added for the list but for the moment those are the most present things.

My plan for the text based game is two parts. 1. Getting the game to work. 2. Is migrating and reworking the code into a discord bot which allows users to play this text based version of Monopoly their servers.

I hope to have updates coming steadily. My current focus is on implementing properties but right now I have no idea where to start or how to efficiently do it. So it is still very much a work in progress.

In dev updates going forwards I'm going to be calling the project Textopoly, once the game is in a playable state I will be posting the code over on github along with the discord bot once it is finished.

Tumbler is going to function for mini updates on my project, official and more detailed updates will be posted on my main blog (https://voidcatstudios.blogspot.com/) but those aren't coming anytime soon.

If you have read this far... thank you very much. I'm still very much a noob programmer, but your support means the world and I hope that as I get more experience and knowledge I'm able to make and share more awesome projects with people like you.

Alright then, this has gotten quite long, have a great rest of your day!

[newcodesociety]

ByteByteGo | Newsletter/Blog

From the newsletter:

Imperative Programming Imperative programming describes a sequence of steps that change the program’s state. Languages like C, C++, Java, Python (to an extent), and many others support imperative programming styles.

Declarative Programming Declarative programming emphasizes expressing logic and functionalities without describing the control flow explicitly. Functional programming is a popular form of declarative programming.

Object-Oriented Programming (OOP) Object-oriented programming (OOP) revolves around the concept of objects, which encapsulate data (attributes) and behavior (methods or functions). Common object-oriented programming languages include Java, C++, Python, Ruby, and C#.

Aspect-Oriented Programming (AOP) Aspect-oriented programming (AOP) aims to modularize concerns that cut across multiple parts of a software system. AspectJ is one of the most well-known AOP frameworks that extends Java with AOP capabilities.

Functional Programming Functional Programming (FP) treats computation as the evaluation of mathematical functions and emphasizes the use of immutable data and declarative expressions. Languages like Haskell, Lisp, Erlang, and some features in languages like JavaScript, Python, and Scala support functional programming paradigms.

Reactive Programming Reactive Programming deals with asynchronous data streams and the propagation of changes. Event-driven applications, and streaming data processing applications benefit from reactive programming.

Generic Programming Generic Programming aims at creating reusable, flexible, and type-independent code by allowing algorithms and data structures to be written without specifying the types they will operate on. Generic programming is extensively used in libraries and frameworks to create data structures like lists, stacks, queues, and algorithms like sorting, searching.

Concurrent Programming Concurrent Programming deals with the execution of multiple tasks or processes simultaneously, improving performance and resource utilization. Concurrent programming is utilized in various applications, including multi-threaded servers, parallel processing, concurrent web servers, and high-performance computing.

[watchmorecinema]

Normally I just post about movies but I'm a software engineer by trade so I've got opinions on programming too.

Apparently it's a month of code or something because my dash is filled with people trying to learn Python. And that's great, because Python is a good language with a lot of support and job opportunities. I've just got some scattered thoughts that I thought I'd write down.

Python abstracts a number of useful concepts. It makes it easier to use, but it also means that if you don't understand the concepts then things might go wrong in ways you didn't expect. Memory management and pointer logic is so damn annoying, but you need to understand them. I learned these concepts by learning C++, hopefully there's an easier way these days.

Data structures and algorithms are the bread and butter of any real work (and they're pretty much all that come up in interviews) and they're language agnostic. If you don't know how to traverse a linked list, how to use recursion, what a hash map is for, etc. then you don't really know how to program. You'll pretty much never need to implement any of them from scratch, but you should know when to use them; think of them like building blocks in a Lego set.

Learning a new language is a hell of a lot easier after your first one. Going from Python to Java is mostly just syntax differences. Even "harder" languages like C++ mostly just mean more boilerplate while doing the same things. Learning a new spoken language in is hard, but learning a new programming language is generally closer to learning some new slang or a new accent. Lists in Python are called Vectors in C++, just like how french fries are called chips in London. If you know all the underlying concepts that are common to most programming languages then it's not a huge jump to a new one, at least if you're only doing all the most common stuff. (You will get tripped up by some of the minor differences though. Popping an item off of a stack in Python returns the element, but in Java it returns nothing. You have to read it with Top first. Definitely had a program fail due to that issue).

The above is not true for new paradigms. Python, C++ and Java are all iterative languages. You move to something functional like Haskell and you need a completely different way of thinking. Javascript (not in any way related to Java) has callbacks and I still don't quite have a good handle on them. Hardware languages like VHDL are all synchronous; every line of code in a program runs at the same time! That's a new way of thinking.

Python is stereotyped as a scripting language good only for glue programming or prototypes. It's excellent at those, but I've worked at a number of (successful) startups that all were Python on the backend. Python is robust enough and fast enough to be used for basically anything at this point, except maybe for embedded programming. If you do need the fastest speed possible then you can still drop in some raw C++ for the places you need it (one place I worked at had one very important piece of code in C++ because even milliseconds mattered there, but everything else was Python). The speed differences between Python and C++ are so much smaller these days that you only need them at the scale of the really big companies. It makes sense for Google to use C++ (and they use their own version of it to boot), but any company with less than 100 engineers is probably better off with Python in almost all cases. Honestly thought the best programming language is the one you like, and the one that you're good at.

Design patterns mostly don't matter. They really were only created to make up for language failures of C++; in the original design patterns book 17 of the 23 patterns were just core features of other contemporary languages like LISP. C++ was just really popular while also being kinda bad, so they were necessary. I don't think I've ever once thought about consciously using a design pattern since even before I graduated. Object oriented design is mostly in the same place. You'll use classes because it's a useful way to structure things but multiple inheritance and polymorphism and all the other terms you've learned really don't come into play too often and when they do you use the simplest possible form of them. Code should be simple and easy to understand so make it as simple as possible. As far as inheritance the most I'm willing to do is to have a class with abstract functions (i.e. classes where some functions are empty but are expected to be filled out by the child class) but even then there are usually good alternatives to this.

Related to the above: simple is best. Simple is elegant. If you solve a problem with 4000 lines of code using a bunch of esoteric data structures and language quirks, but someone else did it in 10 then I'll pick the 10. On the other hand a one liner function that requires a lot of unpacking, like a Python function with a bunch of nested lambdas, might be easier to read if you split it up a bit more. Time to read and understand the code is the most important metric, more important than runtime or memory use. You can optimize for the other two later if you have to, but simple has to prevail for the first pass otherwise it's going to be hard for other people to understand. In fact, it'll be hard for you to understand too when you come back to it 3 months later without any context.

Note that I've cut a few things for simplicity. For example: VHDL doesn't quite require every line to run at the same time, but it's still a major paradigm of the language that isn't present in most other languages.

Ok that was a lot to read. I guess I have more to say about programming than I thought. But the core ideas are: Python is pretty good, other languages don't need to be scary, learn your data structures and algorithms and above all keep your code simple and clean.

[smoothstack-blog]

How to Prepare for a Coding Interview

Coding interviews are a crucial part of the tech hiring process. As a candidate, you'll need to showcase your technical skills, problem-solving abilities, and understanding of data structures and algorithms. To succeed, it's essential to be well-prepared. In this article, we'll discuss the steps you can take to prepare effectively and increase your chances of getting hired.

Understanding the Coding Interview Process

Before diving into the preparation, it's important to understand the coding interview process. 

These interviews are not just about writing code; they also test your ability to think critically under pressure and explain your reasoning behind the solutions you propose. Interviewers are interested in understanding your approach to problem solving and how you handle challenges in a real-time setting.

The Role of Coding Interviews in Tech Hiring for Candidates

By evaluating your coding abilities, interviewers can determine if you're the right fit for the job and the company culture. However, it's not just a one-sided test. It's also an opportunity for the candidate to learn more about the company and its work environment.

Developers will only be successful in a role if the environment is one that is conducive to how they like to work, collaborate, and engage. It’s a win-win benefit for both candidates and employers if both sides are equally excited at the prospect of working together.

Different Types of Coding Interviews

Coding interviews can vary in format and structure. Some companies may conduct coding interviews in-person, while others prefer remote interviews. Additionally, coding interviews can be divided into different types, such as whiteboard coding exercises, take-home assignments, or pair programming sessions.

Each type of coding interview has its own unique challenges and requirements. Whiteboard coding exercises, for example, test your ability to write code on a whiteboard while explaining your thought process out loud. Take-home assignments, on the other hand, give you the opportunity to work on a coding problem at your own pace and showcase your problem-solving skills in a more relaxed environment. Pair programming sessions involve collaborating with an interviewer to solve a problem together, demonstrating your ability to work effectively in a team setting.

Essential Skills for Coding Interviews

Preparing for a coding interview requires mastering several essential skills. Let's explore these skills in detail.

Programming Languages to Master

Individuals should be proficient  in at least one programming language commonly used in the industry, such as Python, Java, C++, or JavaScript. Understanding the syntax, data types, control structures, and libraries of your chosen language will help you solve coding problems efficiently.

Problem-Solving Skills

Problem-solving skills are an integral part of a coding interview. Being able to approach a problem strategically, break it down into smaller subproblems, and devise an efficient solution is highly valued. Practice solving coding challenges and algorithms to sharpen your problem-solving abilities.

Understanding Data Structures and Algorithms

Data structures and algorithms are the building blocks of computer science. Familiarize yourself with various data structures like arrays, linked lists, stacks, queues, trees, and graphs. Additionally, learn popular algorithms for searching, sorting, and optimization. Understanding these concepts will help you analyze problems and devise optimal solutions.

Pre-Interview Preparation

Embarking on the journey of pre-interview preparation is akin to laying the foundation for a successful career in the tech industry. The diligence and effort you put into honing your skills now will pave the way for future opportunities and growth. Here are a few steps you should take before the interview.

Review Common Coding Questions

Start by researching and reviewing common coding questions asked in interviews. Online platforms like LeetCode or HackerRank offer a vast collection of coding problems categorized by difficulty levels. Spend time solving these problems to familiarize yourself with the question patterns and potential solutions.

Practice on Coding Platforms

Practicing on coding platforms simulates the interview environment and helps improve your coding skills. These platforms often offer interactive coding challenges, enabling you to practice solving problems under time constraints. Engage in coding competitions or take part in mock interviews to get a real feel for the interview process.

Brush Up on Technical Knowledge

Aside from coding skills, brush up on your technical knowledge. Review key concepts related to your preferred programming language, data structures, algorithms, and system design. Understanding these concepts in-depth will help you during technical discussions and coding challenges.

During the Interview

Now let's focus on what to do during the coding interview to maximize your chances of getting hired.

When you enter the interview room, remember that your body language and demeanor also play a significant role in making a positive impression. A firm handshake, good posture, and maintaining eye contact can convey confidence and professionalism. These non-verbal cues can complement your technical skills and create a well-rounded image for the interviewer.

Effective Communication During Coding Interviews

During the interview, effective communication is crucial. Clearly explain your thought process, rationale, and any assumptions you make while solving a problem. Communicating effectively helps the interviewer understand your approach and evaluate your problem-solving abilities.

Moreover, active listening is equally important during coding interviews. Pay close attention to the interviewer's prompts and questions. This demonstrates your ability to process information in real time and adapt your problem-solving strategy accordingly. Engaging in a dialogue rather than a monologue can showcase your collaborative skills and willingness to take feedback.

Demonstrating Your Thought Process

Interviewers are interested in how you approach problems and arrive at solutions, rather than just the end result. Be vocal about your thought process throughout the interview. Explain the steps you're taking, potential optimizations or trade-offs, and any ideas that come to mind. This demonstrates your ability to analyze problems and think critically.

Also, don't hesitate to ask clarifying questions if you encounter ambiguity in the problem statement. Seeking clarification shows your attention to detail and ensures that you and the interviewer are on the same page. It's better to take a moment to clarify the problem than to proceed with a misunderstanding that could lead you down the wrong path.

Handling Unknown Problems

Not all problems presented in coding interviews are familiar or straightforward. Interviewers often assess candidates' ability to handle unknown problems and think on their feet. When faced with unfamiliar problems, stay calm and logical. Break down the problem, identify patterns or similarities to other problems you've solved, and come up with a step-by-step approach.

Remember, the interviewer is not just evaluating your technical skills but also your problem-solving methodology. Demonstrating resilience in the face of uncertainty and showcasing adaptability can leave a lasting impression. Embrace the challenge of unknown problems as an opportunity to showcase your creativity and resourcefulness in problem solving.

In Conclusion

Preparing for a coding interview requires a combination of technical skills, problem-solving abilities, and effective communication. Understanding the coding interview process, mastering essential skills, and thorough preparation will increase your chances of getting hired. Remember, practice makes perfect, so devote enough time to solving coding problems, reviewing concepts, and gaining confidence. 

[python-programming-language]

Python: Lists, Tuples, Sets and Dictionaries ...

Post #115: Python.Hub / Instagram, Python Data Structures: Lists, Tubles, Sets and Dictionaries, 2024.

[olderthannetfic]

Do you have some advice for where to get started with doing fandom meta (ressources on ways to extract the meta data of fics, which programs work best with data like that, etc.)?

I'm already using nianeyna's script for my own stuff, so I'm familiar with that.

--

I'm not actually good at any of that.

What I am good at is defining the right question.

There exist many programs for looking at big sets of statistical data. The vast majority of fandom meta writers, including me, use none of these. If you look at the charts that show up in meta posts, you'll notice that most of them show all the signs of Google Sheets default settings. People are literally just dumping their data in the nearest spreadsheet and turning it into a graph. It's as simple as that.

The biggest impediment to most people's meta is that they don't have enough social or historical context to decide which data to look for. And often, they're too lazy to do any hand-collection even though the question they want to answer cannot be answered by AO3 or by existing metadata that's clearly labeled.

The big first step is always to figure out what's out there in terms of archives and other places people post and how its metadata is structured. This both tells you what you can scrape vs. hand-classify and tells you interesting things about what different spaces prioritize or how they conceptualize their content. To pick my favorite obvious example, AO3 classifies by ship type as a top category, with 'no ship' on an even footing with each other type rather than ship vs. no ship with types under ship. Meanwhile, FFN's big classification is genre, many of which are bookstore genres, not fandom trope jargon.

Meta writers extract metadata either with scripts like destinationtoast's (which are on github) or by hand. Some questions require a combination of the two.

I often use a spreadsheet formula to get the URLs I want. Many things increment in predictable ways or are otherwise predictable. You can generate a list of what you need and then just open the links when you want to see fic totals for that particular search or whatever.

I guess if you're asking on a purely technical level, python scripts seem to be the most common.

[shemsuji432]

Tips for the Best Way to Learn Python from Scratch to Pro

Python, often regarded as one of the most beginner-friendly programming languages, offers an excellent entry point for those looking to embark on a coding journey. Whether you aspire to become a Python pro or simply want to add a valuable skill to your repertoire, the path to Python proficiency is well-paved. In this blog, we’ll outline a comprehensive strategy to learn Python from scratch to pro, and we’ll also touch upon how ACTE Institute can accelerate your journey with its job placement services.

1. Start with the basics:

Every journey begins with a single step. Familiarise yourself with Python’s fundamental concepts, including variables, data types, and basic operations. Online platforms like Codecademy, Coursera, and edX offer introductory Python courses for beginners.

2. Learn Control Structures:

Master Python’s control structures, such as loops and conditional statements. These are essential for writing functional code. Sites like HackerRank and LeetCode provide coding challenges to practice your skills.

3. Dive into Functions:

Understand the significance of functions in Python. Learn how to define your functions, pass arguments, and return values. Functions are the building blocks of Python programmes.

4. Explore Data Structures:

Delve into Python’s versatile data structures, including lists, dictionaries, tuples, and sets. Learn their usage and when to apply them in real-world scenarios.

5. Object-Oriented Programming (OOP):

Python is an object-oriented language. Learn OOP principles like classes and objects. Understand encapsulation, inheritance, and polymorphism.

6. Modules and Libraries:

Python’s strength lies in its extensive libraries and modules. Explore popular libraries like NumPy, Pandas, and Matplotlib for data manipulation and visualisation.

7. Web Development with Django or Flask:

If web development interests you, pick up a web framework like Django or Flask. These frameworks simplify building web applications using Python.

8. Dive into Data Science:

Python is a dominant language in the field of data science. Learn how to use libraries like SciPy and Scikit-Learn for data analysis and machine learning.

9. Real-World Projects:

Apply your knowledge by working on real-world projects. Create a portfolio showcasing your Python skills. Platforms like GitHub allow you to share your projects with potential employers.

10. Continuous learning:

Python is a dynamic language, with new features and libraries regularly introduced. Stay updated with the latest developments by following Python communities, blogs, and podcasts.

The ACTE Institute offers a structured Python training programme that covers the entire spectrum of Python learning. Here’s how they can accelerate your journey:

Comprehensive Curriculum: ACTE’s Python course includes hands-on exercises, assignments, and real-world projects. You’ll gain practical experience and a deep understanding of Python’s applications.

Experienced Instructors: Learn from certified Python experts with years of industry experience. Their guidance ensures you receive industry-relevant insights.

Job Placement Services: One of ACTE’s standout features is its job placement assistance. They have a network of recruiting clients, making it easier for you to land a Python-related job.

Flexibility: ACTE offers both online and offline Python courses, allowing you to choose the mode that suits your schedule.

The journey from Python novice to pro involves continuous learning and practical application. ACTE Institute can be your trusted partner in this journey, providing not only comprehensive Python training but also valuable job placement services. Whether you aspire to be a Python developer, data scientist, or web developer, mastering Python opens doors to diverse career opportunities. So, take that first step, start your Python journey, and let ACTE Institute guide you towards Python proficiency and a rewarding career.

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