#sql functions

[marsdevs]

Simplify Your SQL Statements with These Handy Functions

Mastering a new programming language might be scary. Like any other, it has a wide vocabulary that you must learn. But what better way to learn SQL than mastering the SQL functions? There are numerous SQL functions for working with various data types. Once you are all versed with some of the most common, you'll have enough confidence to go on to more challenging ones. Once you have mastered the fundamentals, consider digging further into these topics to expand on what you already know.

Click here to know more: https://www.linkedin.com/posts/marsdevs_carousel-sql-statements-activity-7112682284868120578-0by5

[irishbreakfst]

Call me old but arcmap 10.8.2... I miss you

[barnabusthebarmyballetschool]

as someone who loves Excel...

An F1 team should have dedicated software for this. At the very least they need an actual database for monitoring parts.

No company, big or small, should use Excel this way. It's meant for accounting finances calculations, not looking up part numbers on race weekends!

So uh it turns out that the outdated system to track their car parts that Vowles was talking about last year was actually Microsoft Excel. Williams had been using Excel to list and track 20,000 parts of a Formula One car every year.

[tpointtechblog]

Mastering Aggregate Functions in SQL: A Comprehensive Guide

Introduction to SQL: In the realm of relational databases, Structured Query Language (SQL) serves as a powerful tool for managing and manipulating data. Among its many capabilities, SQL offers a set of aggregate functions that allow users to perform calculations on groups of rows to derive meaningful insights from large datasets.

Learn how to use SQL aggregate functions like SUM, AVG, COUNT, MIN, and MAX to analyze data efficiently. This comprehensive guide covers syntax, examples, and best practices to help you master SQL queries for data analysis.

[thedbahub]

Simplify SQL Queries with the OVER Clause

Introduction Have you ever written a complex SQL query that used window functions like ROW_NUMBER(), RANK(), SUM(), or AVG()? If so, you know how tricky it can be to get the syntax just right. Luckily, SQL Server provides a handy feature called the OVER clause that makes these types of queries much simpler to write and understand. In this article, I’ll explain what the OVER clause does and show…

[satyakideworld]

Exploring the new Polars library in Python

Today, I will present the Python packages where you can explore most of the complex SQLs by using this new package named "Polars". #Python #polars #sql #analytic #function

Today, I will present some valid Python packages where you can explore most of the complex SQLs by using this new package named “Polars,” which can be extremely handy on many occasions. This post will be short posts where I’ll prepare something new on LLMs for the upcoming posts for the next month. Why not view the demo before going through it? Demo Python Packages: pip install polars pip…

[anonymusbosch]

on wanting to do a million things

prompted by @bloodshack 's

i wanna learn SQL but i wanna learn haskell but i wanna learn statistics but i wanna start a degree in macroeconomics also sociology also library science but i wanna learn norwegian but i wanna learn mandarin but i wanna paint but i wanna do pottery but i wanna get better at woodworking but i wanna get better at cooking but i wanna bake one of those cakes that's just 11 crepes stacked on top of each other but i wanna watch more movies but i wanna listen to more podcast episodes but i need to rest but i need to exercise but i wanna play with my dog but i wanna go shopping but i need to go grocery shopping but i need to do the dishes but i need to do laundry but i need to buy a new x y and z but i need to save money but i wanna give all my money away to people who need it more but i wanna pivot my career to book editing but to do that i have to read more and i wanna read more nonfiction but i wanna read more novels but i wanna get better at meditating but i wanna volunteer but i wanna plan a party but i wanna go to law school. but what im gonna do is watch a dumbass youtube video and go to bed

I think I've been doing slightly better this year about Actually Doing Things. not great! but I do a lot and I've been "prototyping" ways to get closer to doing as much as is possible. and if I actually talk about it it's a bunch of very obvious statements but I'll try to make them a little more concrete

rule number one: experiment on yourself

there's no one approach that's right for everyone and there's not even one approach for me that works at all times. try things out. see what works. pay attention to what doesn't. try something else.

rule number two: ask what's stopping you and then take it seriously

example: I often want to do Everything in the evening at like 2 PM, but then get home and am tempted sorely by the couch, and then get stuck inertia'd and not doing much but being tired and kind of bored. why?

if I don't have plans, it's easy to leave work later than planned and hard to make myself do something by a specific time

i'm generally tiredish after work. 4 out of 5 times, that'll go away if I actually start Doing Something, but 1 out of 5 it's real and I will go hardcore sleepmode at 8 PM and just be Done

i use up a ton of my program management/executive function/Deciding Things brain at work and usually find it noticeably harder to string together "want to do Thing > make list of Things > decide on a Thing > do Thing" after I'm home. Even if I have a list of Things to Do, how does one decide! how does one start! and god forbid there's a Necessary thing. then it's all downhill

therefore, mitigations: have concrete time-specific plans in advance.

if I have an art class at 6:00 PM I need to leave work by 5:15 and NO LATER and I can't get sucked into "oh 10 more minutes to finish this" *one hour later*

that also means I have to have a fridge or freezer dinner ready and can't spend 45 minutes cooking "fuck it, what the hell did I put in the fridge, why don't we have soy sauce" evil meal that is not good

plans with friends: dinner! art night! music night! repair-your-clothes night! seeing a show! occasionally, Accountability Time where a friend comes over for We Are Doing Tasks with tea and snacks etc.

for some reason I'm way better about Actually Doing Things when the plan exists already. magically I overcome couch inertia even though I am the same amount of tired! and while I never learn the ability to decouch without plans I at least learn to make them

still working on:

a "prototype" for maybe next month is a weeklyish Study Session for a thing I want to learn about. I want to somehow make it employer-proof (I am accountable to some entity to being at place X at time Y) and haven't figured out a good way. Maybe I can leverage that the local library is open til 8 on wednesdays and somehow make it a Thing? maybe I'll try it!

oh god oh fuck the thing about plans is that if you want to have them you need to make them. christ. a lot of the time I can cover this with some combo of weekend planning + recurring events (things like weekly friend dinner/weekly class) + having cool friends who reach out proactively but it still requires active planning and it can fall thru the cracks

rule three: cool friends

they can take you to things

they can remind you that you can do whatever the fuck you please

i have a friend who is somehow Always doing cool classes and learning shit. and this reminds me that I can ... do that. and sometimes I do

you can take them to things!!

rule four: try to kill the anon hate in your head

obv this depends on your circumstance but sometimes it's worth it to me to look at constraints that "feel real" and check whether they're an active choice I made thoughtfully or, like, the specters of people I don't know judging my choices

time and money are obvious ones. recently was gently nudged towards looking at whether i could give myself more time to Do Things by cooking less. imaginary specters of judgmental twitterites: "it's illegal to spend money. if you get takeout you're the first up against the wall when the revoution comes. make all your lunches and dinners and hoard the money for Later. for Something. how dare you get lunch at the store. you bourgeois hoe. taking charity donations from the mouths of the poor cause you don't have your life together enough to cook artisanal bespoke dinners every night. fuck you." and obviously eating takeout 24/7 is not the answer, but realizing I was not making an active choice helped me try making the active choice instead. "how much do I actually want to balance cost, time, tastiness, and wastefulness of my food, given my amount of free time and my salary and the tradeoff against doing something else? can I approach it differently to do more quick cheap food + some takeout?" -> current prototype: substitute in 1 takeout dinner or restaurant-with-friends a week, 1 frozen type dinner, and then batch cook or sandwiches lunches w/ "permission" to get fast lunch at the store. we'll see how it goes!

i am really really bad at this and find it helpful to talk to other people who can help point out when I'm being haunted by ghosts about it.

rule five: what would it take? what's the next step?

this one i give a lot of credit to @adiantum-sporophyte in particular for, especially for prompting me with questions when I muse about the million-ideal-lives on car rides. what would it look like to do xyz? what's something I could do right now to move in that direction? what's the obstacle? like, actually ask the question and think through it. with a person talking to you! damn! maybe the obstacle to x is that I don't know if I'll like it or if I just like the idea of it. and I don't want to commit to x without knowing. Okay, so maybe an approach would be to find someone who does x and talk to them about how their life is, or maybe it's "spend 15 minutes looking up intro-to-x near me", or "actively schedule 1 instance of x", or something like that. Or maybe it's that I don't know what it takes to do x. Okay, how about on Tues after dinner Adiantum fixes a sweater at my apartment while I spend 20 min looking at prereqs for x. like, it's so basic to say "to do a thing, you could try figuring out how to do it" but I think the important thing here is the feedback/prompting to even recognize "hey, step back, if you don't know the next step then figuring out the next step is the next step"

rule six: habits

prototyping: exercise

I do a lot better when I exercise in the mornings. I do a lot better when I do PT exercises regularly. For a while I was doing PT with friend in the morning every morning before work (accountability! a friendly face to make it more pleasant!) but that didn't really solve - it's not the kind of exercise that makes me feel awake/active, it's like dumb little foot botherings. but: having the habit of morning exercise made it easier to swap out 2 of the 5 days for more intense exercise, and then to swap those 2 for a different more intense exercise when I needed a break. it's easier to build a low-effort version of the habit and then work in the higher-effort one than to just Decide to be the kind of person who gets up at ass o clock to do cardio or whatever

rule seven: set up the structure of your life to make it easy

this is also a "duh" thing but like. on so many levels it comes down to structure your life to make the choice more doable. this can be something like "i structure my life to make vegetarian cooking baseline and vegan cooking the majority by stocking the pantry with staples and spices from cuisines that work well that way" or "i chose an apartment that lets me commute by bike" or "i have my camping gear put away in a fashion that makes it easier to gather frequently and lowers the barrier to trips" or "i keep physical books around to prompt myself to read xyz" to "i don't use instagram or twitter or snapchat or facebook" to . idk.

and in terms of charitable giving: similar deal. I have an explicit budget at the beginning of the year (~10% of my before-tax income), I know in advance what charities I give to, and I know what timing I will use (basically, alerts for donation matching around specific fundraising times). Anything outside the Plan comes from my discretionary budget/fun money. That makes it less of a mental load (the choice is already made; I don't grapple with every donation request or every bleeding-heart trap because I have a very solid anchor on "I give to xyz, the money's set aside") and it's armor against impulsive-but-not-useful scrupulosity. I structure the rest of my spending/life to prioritize a set amount and it makes it easier to follow through

rule eight: if you can do it at work a tiny bit that counts for real life

(infrequently used)

"hi mr. manager I think it would be great if I could use enough SQL to make basic queries in the database so we don't have to go through the software team for common/basic questions. I'd like to take 1 hr on Friday to go through some basic tutorials and then 1 hr with Pat on Monday so he can walk me through an intro for our specific use case. I estimate this will help save the team a couple hours a week of waiting for answers from the other team." and then you have enough of a handle with baby's first SQL that you can add little bits and bobs as you exercise it. this is responsible for a medium amount of my knowledge of python and all 3 brain cells worth of SQL.

rule nine: life is an optimization problem

not in, like, "you need to optimize your skincare and career and exercise and social life and have everything all at once" that's not what optimization means. optimization is like, maximize something with respect to a set of constraints. i explicitly Do Not do skincare beyond "wash face" and "sunscreen" bc I want to optimize my life for like looking at weird plants in the mountains. explicitly choosing to put time and money elsewhere! can't have it all all at once. so fuck them pores. who give a shit. yeah i ate a lot of protein shakes instead of home cooked breakfasts this week bc i was prioritizing morning exercise. im looking at this beautiful bug and it doesn't know what fashion is or what my resume looks like. im holding a lizard. im not spending time on picking cool clothes or whatever bc i spent that time looking up lizard hotspots on purpose.

that's really long and probably mostly, like, not surprising? but i keep benefiting from ppl being like "hey have you considered Obvious Thing" framed very gently

[moderator-monnie]

Does Slammer have the hots for the rot? Virus yaoi? /j

(Slammer Sonic Artwork made by @lazy-charlie )

(The Rot/Contaminated Au/Artwork made by @sonicexelle-junkary )

Moderator Monnie: PERSONALLY, It's something I have begun to ship personally, Ironically enough being Slammers creator.

But Canonically, Slammer can't feel that way, Slammer's goal as a virus to spread, but it does enjoy the rot's company, and thinks combing both of their abilities together, could be the ultimate way to spread.

Like just imagine this right? SQL!ROT ((also the name of their fusion but can be used as a ship name lmao))

They combine together, then they are not only able to enter the internet, but due to how the rot itself functions enter the real world too.

Infecting game world's but once enough is consumed... one day... thousands. Maybe even million's of computers having black goop come out of their monitor's all at the same time,

And if anything organic or anything powered by electricity, has this 'goop' enter it, they become infected and thus now become part of a singular mind.

A truly horrific thought, when not only are your friends, family and pet's not safe, not not even something as simple as a desk lamp, can be trusted.

Now that's peak horror.

A Mixture of a computer worm, and an alien virus.

[chappydev]

Future of LLMs (or, "AI", as it is improperly called)

Posted a thread on bluesky and wanted to share it and expand on it here. I'm tangentially connected to the industry as someone who has worked in game dev, but I know people who work at more enterprise focused companies like Microsoft, Oracle, etc. I'm a developer who is highly AI-critical, but I'm also aware of where it stands in the tech world and thus I think I can share my perspective. I am by no means an expert, mind you, so take it all with a grain of salt, but I think that since so many creatives and artists are on this platform, it would be of interest here. Or maybe I'm just rambling, idk.

LLM art models ("AI art") will eventually crash and burn. Even if they win their legal battles (which if they do win, it will only be at great cost), AI art is a bad word almost universally. Even more than that, the business model hemmoraghes money. Every time someone generates art, the company loses money -- it's a very high energy process, and there's simply no way to monetize it without charging like a thousand dollars per generation. It's environmentally awful, but it's also expensive, and the sheer cost will mean they won't last without somehow bringing energy costs down. Maybe this could be doable if they weren't also being sued from every angle, but they just don't have infinite money.

Companies that are investing in "ai research" to find a use for LLMs in their company will, after years of research, come up with nothing. They will blame their devs and lay them off. The devs, worth noting, aren't necessarily to blame. I know an AI developer at meta (LLM, really, because again AI is not real), and the morale of that team is at an all time low. Their entire job is explaining patiently to product managers that no, what you're asking for isn't possible, nothing you want me to make can exist, we do not need to pivot to LLMs. The product managers tell them to try anyway. They write an LLM. It is unable to do what was asked for. "Hm let's try again" the product manager says. This cannot go on forever, not even for Meta. Worst part is, the dev who was more or less trying to fight against this will get the blame, while the product manager moves on to the next thing. Think like how NFTs suddenly disappeared, but then every company moved to AI. It will be annoying and people will lose jobs, but not the people responsible.

ChatGPT will probably go away as something public facing as the OpenAI foundation continues to be mismanaged. However, while ChatGPT as something people use to like, write scripts and stuff, will become less frequent as the public facing chatGPT becomes unmaintainable, internal chatGPT based LLMs will continue to exist.

This is the only sort of LLM that actually has any real practical use case. Basically, companies like Oracle, Microsoft, Meta etc license an AI company's model, usually ChatGPT.They are given more or less a version of ChatGPT they can then customize and train on their own internal data. These internal LLMs are then used by developers and others to assist with work. Not in the "write this for me" kind of way but in the "Find me this data" kind of way, or asking it how a piece of code works. "How does X software that Oracle makes do Y function, take me to that function" and things like that. Also asking it to write SQL queries and RegExes. Everyone I talk to who uses these intrernal LLMs talks about how that's like, the biggest thign they ask it to do, lol.

This still has some ethical problems. It's bad for the enivronment, but it's not being done in some datacenter in god knows where and vampiring off of a power grid -- it's running on the existing servers of these companies. Their power costs will go up, contributing to global warming, but it's profitable and actually useful, so companies won't care and only do token things like carbon credits or whatever. Still, it will be less of an impact than now, so there's something. As for training on internal data, I personally don't find this unethical, not in the same way as training off of external data. Training a language model to understand a C++ project and then asking it for help with that project is not quite the same thing as asking a bot that has scanned all of GitHub against the consent of developers and asking it to write an entire project for me, you know? It will still sometimes hallucinate and give bad results, but nowhere near as badly as the massive, public bots do since it's so specialized.

The only one I'm actually unsure and worried about is voice acting models, aka AI voices. It gets far less pushback than AI art (it should get more, but it's not as caustic to a brand as AI art is. I have seen people willing to overlook an AI voice in a youtube video, but will have negative feelings on AI art), as the public is less educated on voice acting as a profession. This has all the same ethical problems that AI art has, but I do not know if it has the same legal problems. It seems legally unclear who owns a voice when they voice act for a company; obviously, if a third party trains on your voice from a product you worked on, that company can sue them, but can you directly? If you own the work, then yes, you definitely can, but if you did a role for Disney and Disney then trains off of that... this is morally horrible, but legally, without stricter laws and contracts, they can get away with it.

In short, AI art does not make money outside of venture capital so it will not last forever. ChatGPT's main income source is selling specialized LLMs to companies, so the public facing ChatGPT is mostly like, a showcase product. As OpenAI the company continues to deathspiral, I see the company shutting down, and new companies (with some of the same people) popping up and pivoting to exclusively catering to enterprises as an enterprise solution. LLM models will become like, idk, SQL servers or whatever. Something the general public doesn't interact with directly but is everywhere in the industry. This will still have environmental implications, but LLMs are actually good at this, and the data theft problem disappears in most cases.

Again, this is just my general feeling, based on things I've heard from people in enterprise software or working on LLMs (often not because they signed up for it, but because the company is pivoting to it so i guess I write shitty LLMs now). I think artists will eventually be safe from AI but only after immense damages, I think writers will be similarly safe, but I'm worried for voice acting.

[markgo7]

Lead Oracle PL/SQL Developer@ Nashville, TN (Remote)

Job Title: Lead Oracle PL/SQL Developer Location: Nashville, TN (Remote) Apply Here: https://jobshorn.com/job/lead-oracle-pl-sql-developer/7370 Duration: 5+ Months Interview Type: Skype or Phone Job Type: #C2C, #W2, #1099 🔹 Job Description: Minimum of five years of experience with Oracle PL/SQL and a relational database such as Oracle 19g. Experience in writing complex SQL and PL/SQL queries, including the development of stored procedures, functions, and triggers Please share resumes to mark@sohanit.com |📞 Phone: +1 470-410-5352 Ext:111

#OraclePLSQLDeveloper #oracle #Tennessee

[kuai-kuai]

webdev log 2

implemented a gallery. I originally wanted it to be more grid-like but I decided I didn't want to mess too much with that, and I like the simple look anyways. forces you to really take in every shitty drawing.

it features a search function that only works for tags. its purpose is mostly just to search multiple tags, because I couldn't be fucked to add a feature where you could click on multiple tags there at the tags list at the top. it lists out all used tags in the table that stores art so you have an idea of what there all is.

at the bottom there's pagination. it's INSANELY easy to do with this framework I'm using. I was gushing about it to my partner on call!! they made fun of me but that's okay!!!!

anyways, clicking on the date underneath the drawing takes you to a view with the image itself (a kind of "post", if I can call it that) here you can view comments and leave one yourself if you so desire. guests are NOT allowed to reply to existing comments because I'd rather things not get too clogged up. I can't stop anyone if they did an "@{name} {message}" type comment, but I don't think anyone is gonna be chatting it up on my site, so idc. I just want it very minimal, and no nesting beyond one single reply.

of course, you can comment on story chapters too so here's what it looks like for a user (me). of course, if a user (me) posts then it gets automatically approved.

the table that stores comments differentiates story comments and art comments with foreign keys to the primary keys of the the chapter and art tables. it's a little convoluted and I kind of wish I didn't do it this way but it's too damn late isn't it. but honestly it might've been the only way to do it. the problem is just repeating code for both chapter and art views.. making a change to one means I gotta manually make the same change to the other. huge pain..

added user authentication and a really shitty bare bones dashboard for myself to approve/reject comments directly on the site in case someone comes along and wants to be mean to me :( rejecting a comment deletes it OFF my site forever. though I kind of want to be able to keep hate mail so I dunno.. oh, and also a big fat logout button because I have nowhere else to put it.

I'll spare everyone the more technical ramblings.

anyways, I'm hoping to add more things later. these are my plans:

allow users (me) to post stories/art through the site itself instead of doing it manually in the vscode terminal for every. single. story. and drawing. (probably took me 6+ hours total just doing this. I don't know why I did it.) (btw this consists of writing commands to store information via the terminal. also, sql and similar databases don't store things like markup or even line breaks. I had to alter all my stories and put \n every time there was a line break... and you have to escape apostrophes (or quotes, depending on which you use) so every "it's" had to be made into "it\'s" HUGE. PAIN. I didn't do this manually obviously but sifting and plugging my stories into character replacers was so time consuming)

delete comments button.... For my eyes and fingers only

make an About page. I've been avoiding all the fun things and doing just the scary stff

figure out SSH stuff...

clean up the shitty css. I refuse to use tailwind even tho it's trying to force me.. I don't want some sleek polished site I want it look like it's in shambles, because it is

but yeah thanks for reading about my webdev and coding journey. even though using the laravel framework made things a thousand times easier it's still a crazy amount of work. let's say building a site completely from scratch means buying every material and designing the house yourself, and using a website builder like wix is just like buying a pre built home and you're just decorating it. using this framework is like putting together a build-your-own-house kit. you're still building a fucking house.

I feel crazy. it felt like the site was close to breaking several times. been sleep deprived for several days working on this nonstop I think I'm getting a little sick 😵‍💫

going to bed now. it's 9 am.

[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.

[digitaldetoxworld]

Structured Query Language (SQL): A Comprehensive Guide

 Structured Query Language, popularly called SQL (reported "ess-que-ell" or sometimes "sequel"), is the same old language used for managing and manipulating relational databases. Developed in the early 1970s by using IBM researchers Donald D. Chamberlin and Raymond F. Boyce, SQL has when you consider that end up the dominant language for database structures round the world.

Structured query language commands with examples

Today, certainly every important relational database control system (RDBMS)—such as MySQL, PostgreSQL, Oracle, SQL Server, and SQLite—uses SQL as its core question language.

What is SQL?

SQL is a website-specific language used to:

Retrieve facts from a database.

Insert, replace, and delete statistics.

Create and modify database structures (tables, indexes, perspectives).

Manage get entry to permissions and security.

Perform data analytics and reporting.

In easy phrases, SQL permits customers to speak with databases to shop and retrieve structured information.

Key Characteristics of SQL

Declarative Language: SQL focuses on what to do, now not the way to do it. For instance, whilst you write SELECT * FROM users, you don’t need to inform SQL the way to fetch the facts—it figures that out.

Standardized: SQL has been standardized through agencies like ANSI and ISO, with maximum database structures enforcing the core language and including their very own extensions.

Relational Model-Based: SQL is designed to work with tables (also called members of the family) in which records is organized in rows and columns.

Core Components of SQL

SQL may be damaged down into numerous predominant categories of instructions, each with unique functions.

1. Data Definition Language (DDL)

DDL commands are used to outline or modify the shape of database gadgets like tables, schemas, indexes, and so forth.

Common DDL commands:

CREATE: To create a brand new table or database.

ALTER:     To modify an present table (add or put off columns).

DROP: To delete a table or database.

TRUNCATE: To delete all rows from a table but preserve its shape.

Example:

sq.

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CREATE TABLE personnel (

  id INT PRIMARY KEY,

  call VARCHAR(one hundred),

  income DECIMAL(10,2)

);

2. Data Manipulation Language (DML)

DML commands are used for statistics operations which include inserting, updating, or deleting information.

Common DML commands:

SELECT: Retrieve data from one or more tables.

INSERT: Add new records.

UPDATE: Modify existing statistics.

DELETE: Remove information.

Example:

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INSERT INTO employees (id, name, earnings)

VALUES (1, 'Alice Johnson', 75000.00);

three. Data Query Language (DQL)

Some specialists separate SELECT from DML and treat it as its very own category: DQL.

Example:

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SELECT name, income FROM personnel WHERE profits > 60000;

This command retrieves names and salaries of employees earning more than 60,000.

4. Data Control Language (DCL)

DCL instructions cope with permissions and access manage.

Common DCL instructions:

GRANT: Give get right of entry to to users.

REVOKE: Remove access.

Example:

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GRANT SELECT, INSERT ON personnel TO john_doe;

five. Transaction Control Language (TCL)

TCL commands manage transactions to ensure data integrity.

Common TCL instructions:

BEGIN: Start a transaction.

COMMIT: Save changes.

ROLLBACK: Undo changes.

SAVEPOINT: Set a savepoint inside a transaction.

Example:

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BEGIN;

UPDATE personnel SET earnings = income * 1.10;

COMMIT;

SQL Clauses and Syntax Elements

WHERE: Filters rows.

ORDER BY: Sorts effects.

GROUP BY: Groups rows sharing a assets.

HAVING: Filters companies.

JOIN: Combines rows from  or greater tables.

Example with JOIN:

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SELECT personnel.Name, departments.Name

FROM personnel

JOIN departments ON personnel.Dept_id = departments.Identity;

Types of Joins in SQL

INNER JOIN: Returns statistics with matching values in each tables.

LEFT JOIN: Returns all statistics from the left table, and matched statistics from the right.

RIGHT JOIN: Opposite of LEFT JOIN.

FULL JOIN: Returns all records while there is a in shape in either desk.

SELF JOIN: Joins a table to itself.

Subqueries and Nested Queries

A subquery is a query inside any other query.

Example:

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SELECT name FROM employees

WHERE earnings > (SELECT AVG(earnings) FROM personnel);

This reveals employees who earn above common earnings.

Functions in SQL

SQL includes built-in features for acting calculations and formatting:

Aggregate Functions: SUM(), AVG(), COUNT(), MAX(), MIN()

String Functions: UPPER(), LOWER(), CONCAT()

Date Functions: NOW(), CURDATE(), DATEADD()

Conversion Functions: CAST(), CONVERT()

Indexes in SQL

An index is used to hurry up searches.

Example:

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CREATE INDEX idx_name ON employees(call);

Indexes help improve the performance of queries concerning massive information.

Views in SQL

A view is a digital desk created through a question.

Example:

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CREATE VIEW high_earners AS

SELECT call, salary FROM employees WHERE earnings > 80000;

Views are beneficial for:

Security (disguise positive columns)

Simplifying complex queries

Reusability

Normalization in SQL

Normalization is the system of organizing facts to reduce redundancy. It entails breaking a database into multiple related tables and defining overseas keys to link them.

1NF: No repeating groups.

2NF: No partial dependency.

3NF: No transitive dependency.

SQL in Real-World Applications

Web Development: Most web apps use SQL to manipulate customers, periods, orders, and content.

Data Analysis: SQL is extensively used in information analytics systems like Power BI, Tableau, and even Excel (thru Power Query).

Finance and Banking: SQL handles transaction logs, audit trails, and reporting systems.

Healthcare: Managing patient statistics, remedy records, and billing.

Retail: Inventory systems, sales analysis, and consumer statistics.

Government and Research: For storing and querying massive datasets.

Popular SQL Database Systems

MySQL: Open-supply and extensively used in internet apps.

PostgreSQL: Advanced capabilities and standards compliance.

Oracle DB: Commercial, especially scalable, agency-degree.

SQL Server: Microsoft’s relational database.

SQLite: Lightweight, file-based database used in cellular and desktop apps.

Limitations of SQL

SQL can be verbose and complicated for positive operations.

Not perfect for unstructured information (NoSQL databases like MongoDB are better acceptable).

Vendor-unique extensions can reduce portability.

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