#python datatypes

[pythondjangoflask]

python int()class - Binary-Octal-Hexadecimal-Decimal also integer Datatypes

[dataservicer]

String Data Types in Python #shorts #python #string

[simplesyntax85]

Datatypes

Hello everyone! There is a new blog post on SimpleSyntax covering datatypes. You can find it here https://lnkd.in/gftC9sHz

Understanding these basic datatypes is crucial to programming. #programming#python#coding#datatypes#codingbootcamp

[codeonedigest]

(via YouTube Short - Quick Cheat Sheet to Python Data types for Beginners | Learn Python Datatypes in 1 minute)

Hi, a short #video on #python #datatype is published on #codeonedigest #youtube channel. Learn the python #datatypes in 1 minute.

#pythondatatypes #pythondatatypes #pythondatatypestring #pythondatatypedeclaration #pythondatatypeprogram #pythondatatyperange #pythondatatypeofvariable #pythondatatypescheatsheet #pythondatatypeinteger #pythondatatypenumeric #pythondatatypefloat #pythondatatypecomplex #pythondatatypestring #pythondatatypelist #pythondatatypetuple #pythondatatypedictionary #pythondatatypeBoolean #pythondatatypeset #pythondatatype #pythondatatyperange #integer #numeric #string #range #tuple #list #dictionary #Boolean #complex #long

[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 :)

[izicodes]

Re-Learning C# Studies #1 and #2

Tuesday 09 May 2023

Yes starting from the beginning again! I actually started yesterday night but forgot to post about it! So, this is a combined post! Yesterday I made my study plan (I used ChatGPT to make the plan since I'm lazy~) and I started with Day 1 and 2 which focuses on "Basic Syntax and Data Types"~!

Basic Syntax

Just learnt the complete basics of any programming language; it's syntax. I already know this. I am pretty confident in this. It's understanding things like the keywords such as using, namespace, class and static void!

Data Types

Another sub-topic I already know a lot in is the C# data types! In C# you have to declare the type the data will be before using it - unlike languages like JavaScript, Lua and Python where you can just declare and the program will automatically assign the datatype based on the value. Declaring data types in C# is beneficial because it provides more clarity and specificity in the code. I think it helps catch errors quicker as well~!

That's all I learnt in the last 2 days! Really happy I got to go over the basics again! Here are the resources that I used:

Codecademy - link

Software Testing Help - link

TutorialsTeacher - link

Thanks for reading and happy coding! 🥰👍🏾💗

[dreamdolldeveloper]

dreamdoll watchlist ★

key takeaways:

python is a good beginning coding language to start with

start with: variables, datatypes, loops, functions, if statements, oop

if you covered the basics, it would and should take you approx. 2 weeks.

first project: do something interesting/useful. start small.

simple games or a food recommendation system with specific ingredients

panda dataframe

use API = application programming interface = different pieces of software interacting with each other. grabbing data from another source

after your first project, learn about data structures and algorithms. how API works. learn how to read documentation.

dictionary

linkedlists

queues

heaps

trees

graphs

learn about more things and how to implement them into projects.

correct mindset:

implementation and application > theory and concepts knowing ≠ being able to do it

stay curious.

explore things outside of what is prescribed in a resource. that's how you learn about different concepts and how you deeply understand the concepts that you already know.

the best programmers they've met are the tinkers. these are the people who play around with their code and try a bunch of different things.

getting stuck:

it all comes down to problem solving. be comfortable with not knowing things and staying calm while trying to figure out the problems

how to learn even faster:

find a community where you work on projects together. you will learn so many things from other experiences programmers just by interacting with them. and accountability because you just can't give up

learning is never ending. you will always be learning something new.

[mattupalliayyappa]

DatatypeIn Python, a datatype is a classification that specifies which type of value a variable can hold. The use of datatypes in Python is crucial for defining the kind of data a variable can store and for determining the operations that can be performed on it. Here are some key aspects related to datatypes in Python

[tipzyness]

python errors be like -> TypeError: cannot interpret '3' as a datatype.

[exhydra]

OverflowError: Python int too large to convert to C long

A datatype with date format was tried to converted to an integer , but due to the size of the integer, there was an overflow error. ddate = df['dat'].astype('int') return arr.astype(dtype, copy=True) OverflowError: Python int too large to convert to C long try: ddate = df['dat'].astype('int') except OverflowError: ddate = df['dat'].astype('int64')

[govindhtech]

How ONNX Runtime is Evolving AI in Microsoft with Intel

With the goal of bringing AI features to devices, the Microsoft Office team has been working with Intel and ONNX Runtime for over five years to integrate AI capabilities into their array of productivity products. The extension of AI inference deployment from servers to Windows PCs enhances responsiveness, preserves data locally to protect privacy, and increases the versatility of AI tooling by removing the requirement for an internet connection. These advancements keep powering Office features like neural grammar checker, ink form identification, and text prediction.

What is ONNX Runtime

As a result of their extensive involvement and more than two decades of cooperation, Intel and Microsoft are working more quickly to integrate AI features into Microsoft Office for Windows platforms. The ONNX Runtime, which enables machine learning models to scale across various hardware configurations and operating systems, is partially responsible for this accomplishment. The ONNX runtime is continuously refined by Microsoft, Intel, and the open-source community. When used in this way, it enhances the efficiency of Microsoft Office AI models running on Intel platforms.

AI Generative

With ONNX Runtime, you can incorporate the power of large language models (LLMs) and generative artificial intelligence (AI) into your apps and services. State-of-the-art models for image synthesis, text generation, and other tasks can be used regardless of the language you develop in or the platform you need to run on.

ONNX Runtime Web

With a standard implementation, ONNX Runtime Web enables cross-platform portability for JavaScript developers to execute and apply machine learning models in browsers. Due to the elimination of the need to install extra libraries and drivers, this can streamline the distribution process.

ONNX Runtime Java

Using the same API as cloud-based inferencing, ONNX Runtime Mobile runs models on mobile devices. Swift, Objective-C, Java, Kotlin, JavaScript, C, and C++ developers can integrate AI to Android, iOS, react-native, and MAUI/Xamarin applications by using their preferred mobile language and development environment.

ONNX Runtime Optimization

Inference models from various source frameworks (PyTorch, Hugging Face, TensorFlow) may be efficiently solved by ONNX Runtime on various hardware and software stacks. In addition to supporting APIs in many languages (including Python, C++, C#, C, Java, and more), ONNX Runtime Inference leverages hardware accelerators and functions with web browsers, cloud servers, and edge and mobile devices.

Ensuring optimal on-device AI user experience necessitates ongoing hardware and software optimization, coordinated by seasoned AI-versed experts. The most recent ONNX Runtime capabilities are regularly added to Microsoft Office’s AI engine, guaranteeing optimal performance and seamless AI model execution on client devices.

Intel and Microsoft Office have used quantization, an accuracy-preserving technique for optimizing individual AI models to employ smaller datatypes. “Microsoft Office’s partnership with Intel on numerous inference projects has achieved notable reductions in memory consumption, enhanced performance, and increased parallelization all while maintaining accuracy by continuing to focus on our customers,” stated Joshua Burkholder, Principal Software Engineer of Microsoft’s Office AI Platform.

With the help of Intel’s DL Boost, a collection of specialized hardware instruction sets, this method reduces the on-device memory footprint, which in turn reduces latency. The ONNX Runtime has been tuned to work with Intel’s hybrid CPU design, which combines efficiency and performance cores. With Intel Thread Director, this is further enhanced by utilising machine learning to schedule activities on the appropriate core, guaranteeing that they cooperate to maximise performance-per-watt.

Furthermore, on-device AI support for Office web-based experiences is being provided by Intel and Microsoft in partnership. The ONNX Runtime Web makes this feasible by enabling AI feature support directly in web applications, like Microsoft Designer.

Balancing Cloud and On-device

With the advent of AI PCs, particularly those featuring the latest Intel Core Ultra processor, more workloads are being able to move from cloud-based systems to client devices. Combining CPU ,  GPU , and NPU , Intel Core Ultra processors offer complementary AI compute capabilities that, when combined with model and software optimizations, can be leveraged to provide optimal user experience.

Even while the AI PC opens up new possibilities for executing AI activities on client devices, it is necessary to assess each model separately to ascertain whether or not running locally makes sense. AI computation may take on a hybrid form in the future, with a large number of models running on client devices and additional cloud computing used for more complicated tasks. In order to aid with this, Intel AI PC development collaborates with the Office team to determine which use cases are most appropriate for customers using the Intel Core Ultra processor.

The foundation of Intel and Microsoft’s continued cooperation is a common goal of an AI experience optimized to span cloud and on-device with products such as AI PC. Future Intel processor generations will enhance the availability of client compute for AI workloads. As a result, Intel may anticipate that essential tools like Microsoft Office will be created to provide an excellent user experience by utilizing the finest client and cloud technologies.

Read more on govindhtech.com

[develeran]

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The SQL module equips beginners with fundamental skills for managing and querying relational databases, making it an excellent starting point for Data Science and Analyst jobs.

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[dataservicer]

Data Types in Python #shorts #python

[pythonway]

Python's Built-in Datatypes

Python, a versatile and powerful programming language, provides a range of built-in datatypes that make it easier to work with different types of data. In this blog post, we will explore the most commonly used datatypes in Python and learn how they can be utilized in your programs.

Datatypes can be categorized as either mutable or immutable. Understanding the difference between mutable and immutable types is crucial for writing efficient and bug-free code.

Mutable Types: Mutable types are those that can be modified after they are created. This means that you can change their values, add or remove elements, or modify their properties without creating a new instance of the datatype. Examples of mutable types include lists, sets, dictionaries, and bytearrays.

Immutable Types: Immutable types are those whose values cannot be changed once they are created. If you want to modify an immutable type, you need to create a new instance with the desired changes. Examples of immutable types include integers, floats, strings, tuples, and frozensets.

Why Does It Matter? Understanding the mutability of datatypes is important because it affects how data is stored and manipulated in memory. Mutable types consume more memory because they allow for in-place modifications. On the other hand, immutable types are more memory-efficient because they require the creation of a new instance whenever changes are made.

Additionally, mutability affects how data is passed between functions. When a mutable object is passed as an argument to a function, any modifications made to the object within the function will be reflected outside the function as well. However, immutable objects are passed by value, meaning that any modifications made within a function will not affect the original object outside the function.

By understanding the mutability of different datatypes, you can write more efficient and bug-free code by choosing the appropriate datatype for your specific use case.

You can see a datatype of a variable by type(). It returns the specific datatype of the variable

Numbers: Python supports various numeric datatypes, including integers (int), floating-point numbers (float), and complex numbers (complex). These datatypes allow you to perform arithmetic operations and handle mathematical calculations with ease.

x = 10 y = 3.14 z = complex(2, 3) print(type(x)) # Output: <class 'int'> print(type(y)) # Output: <class 'float'> print(type(z)) # Output: <class 'complex'>

Strings: Strings are used to represent text data in Python. They can be enclosed in single quotes (''), double quotes (""), or triple quotes (''' '''). Python provides a wide range of string manipulation methods, allowing you to concatenate, split, and modify strings.

name = "John" greeting = "Hello, " + name + "!" print(greeting) # Output: Hello, John!

Lists: Lists are versatile and mutable collections of items in Python. They can store elements of different datatypes and are enclosed in square brackets ([]). Lists can be modified by adding, removing, or modifying elements.

fruits = ["apple", "banana", "orange"] print(fruits) # Output: ['apple', 'orange', 'orange']

Tuples: Tuples are similar to lists but are immutable, meaning their elements cannot be modified after creation. They are enclosed in parentheses (()) and can store different types of data. Tuples are commonly used to represent a collection of related values.

point = (5, 10) print(point[0]) # Output: 5 print(point[1]) # Output: 10

Dictionaries: Dictionaries are key-value pairs that allow you to store and retrieve data based on unique keys. They are enclosed in curly braces ({}) and provide efficient lookup operations. Dictionaries are useful for building data structures and organizing data.

student = {"name": "John", "age": 25, "grade": "A"} print(student["name"]) # Output: John print(student["age"]) # Output: 25

Set: Sets are unordered collections of unique elements in Python. They are enclosed in curly braces ({}) or can be created using the set() function. Sets allow you to perform operations like union, intersection, and difference, making them useful for tasks like removing duplicates from a list.

fruits = {"apple", "banana", "orange"} print(fruits) # Output: {'apple', 'banana', 'orange'}

Frozenset: Similar to sets, frozensets are immutable sets in Python. Once created, the elements of a frozenset cannot be modified. Frozensets are useful when you need a set-like object that can be used as a key in a dictionary.

colors = frozenset(["red", "green", "blue"]) print(colors) # Output: frozenset({'red', 'green', 'blue'})

8. Range: The range datatype represents a sequence of numbers. It is commonly used in for loops to iterate over a specific range of values. The range() function takes start, stop, and step arguments to generate the desired sequence.

for i in range(1, 5): print(i) # Output: # 1 # 2 # 3 # 4

Pay attention to location of print statement. Here let me tell you very important thing. Indentation of code lines is VERY, VERY important in python. It crucial in determining the order of execution for code.

Indentation is used to define the structure and hierarchy of code blocks. Unlike other programming languages that use braces or keywords to denote code blocks, Python uses indentation to determine the scope of statements within loops, conditionals, functions, and classes.

Here are code structure and code execution aspects where indentation plays role:

Code Readability: Indentation improves code readability by visually representing the logical structure of the code. It makes it easier for developers to understand the flow and organization of the program. Proper indentation ensures that code is visually appealing and easier to comprehend, especially when working with complex projects.

Block Identification: Indentation helps Python identify the beginning and end of code blocks. It distinguishes between different levels of nesting within loops, conditionals, function definitions, and classes. By indenting code blocks consistently, it becomes clear which statements belong to a particular block and which ones are outside of it.

Syntax Requirement: In Python, correct indentation is a syntax requirement. If the code is not indented properly, Python will raise an "IndentationError" and the program will fail to execute. This strict enforcement of indentation ensures that code is well-structured and reduces the chances of introducing errors due to incorrect block placement.

Code Execution: Indentation affects the execution of code in Python. Statements that are indented at the same level are considered part of the same block and are executed together. The indentation level determines the scope and order in which statements are executed. Correct indentation ensures that code behaves as intended and produces the desired results.

Byte and Bytearray: Byte and bytearray are datatypes used to represent sequences of bytes in Python. They are often used in scenarios where binary data needs to be processed or manipulated, such as reading and writing files in binary mode.

data = b'Hello' data_array = bytearray(b'World') print(data) # Output: b'Hello' print(data_array) # Output: bytearray(b'World')

Memoryview: The memoryview datatype in Python provides a memory-efficient way to access and manipulate the underlying data of an object. It allows you to view the data in different formats and perform operations directly on the memory without creating additional copies.

[codeonedigest]

Quick Cheat Sheet to Python Data types for Beginners | Learn Python Data...

Hi, a short #video on #python #datatype is published on #codeonedigest #youtube channel. Learn the python #datatypes in 1 minute.

#pythondatatypes #pythondatatypes #pythondatatypestring #pythondatatypedeclaration #pythondatatypeprogram #pythondatatyperange #pythondatatypeofvariable #pythondatatypescheatsheet #pythondatatypeinteger #pythondatatypenumeric #pythondatatypefloat #pythondatatypecomplex #pythondatatypestring #pythondatatypelist #pythondatatypetuple #pythondatatypedictionary #pythondatatypeBoolean #pythondatatypeset #pythondatatype #pythondatatyperange #integer #numeric #string #range #tuple #list #dictionary #Boolean #complex #long