What is NumPy?

NumPy (Numerical Python) is a Python library used for working with arrays and matrices. It is a powerful tool for scientific computing and data analysis. NumPy is widely used in a variety of fields, including physics, engineering, finance, and economics.

What is top use cases of NumPy?

Top use cases of NumPy:

• Data Analysis: NumPy is extensively used for data analysis tasks, such as data cleaning, filtering, and transformation. It allows for efficient manipulation and computation on large datasets.
• Scientific Computing: NumPy is essential for scientific computing tasks, including simulations, modeling, and statistical analysis. It provides efficient algorithms for numerical operations, linear algebra, Fourier transforms, and more.
• Machine Learning: NumPy is a fundamental library in the field of machine learning. It provides the building blocks for implementing various algorithms, such as linear regression, logistic regression, and neural networks. It offers efficient array operations, which are crucial for handling large datasets.
• Image Processing: NumPy is widely used in image processing tasks, including image manipulation, enhancement, and analysis. It provides efficient methods for handling image data as arrays, making it easy to perform operations like cropping, resizing, and filtering.

What are feature of NumPy?

Features of NumPy:

• Fast: NumPy is highly optimized for performing calculations on large arrays and matrices. It uses C code for its core operations, which makes it much faster than pure Python code.
• Flexible: NumPy arrays can be multidimensional, which means they can have more than one dimension. This makes them a powerful tool for representing data with multiple dimensions, such as images and time series data.
• Versatile: NumPy provides a wide range of functions for manipulating and analyzing arrays. These functions cover a wide range of topics, including linear algebra, statistical analysis, and data visualization.

What is the workflow of NumPy?

The typical workflow for using NumPy is as follows:

1. Import NumPy: The first step is to import the NumPy library into your Python program. This can be done using the following import statement:

import numpy as np

2. Create a NumPy array: Once NumPy is imported, you can create a NumPy array using the np.array() function. This function takes a list or other iterable object as input and creates a NumPy array with the same elements.

array = np.array([1, 2, 3, 4, 5])

3. Manipulate the array: NumPy provides a wide range of functions for manipulating arrays. These functions can be used to perform calculations, filter data, and sort data.

array_sum = np.sum(array)
print(array_sum)

4. Analyze the array: NumPy also provides a wide range of functions for analyzing arrays. These functions can be used to calculate statistics, perform hypothesis tests, and fit models to data.

array_mean = np.mean(array)
print(array_mean)

How NumPy Works & Architecture?

NumPy arrays are stored in contiguous memory blocks, which makes them very fast for calculations. NumPy also uses a variety of optimization techniques, such as vectorization and caching, to further improve performance.

NumPy facilitates an extensive range of mathematical computations on arrays, enhancing Python with robust data structures for efficient calculations involving arrays and matrices. It also provides an extensive library of high-level mathematical functions specifically designed to manipulate these arrays and matrices.

How to Install and Configure NumPy?

Here step-by-step guide for Installing and configuring NumPy:

Step 1: Check Python Installation

Ensure you have Python installed on your system. You can check the Python version using the terminal:

python3 --version

If Python is not installed, download and install the appropriate version for your operating system.

Step 2: Install NumPy

Open a terminal window (Command Prompt or Terminal) and use the pip command to install NumPy:

pip install numpy

This command will download and install the NumPy library for your Python environment.

Step 3: Verify NumPy Installation

To verify that NumPy is installed correctly, start a Python interpreter session using the following command:

python3

Within the Python interpreter, import NumPy and try printing a version information:

import numpy as np
print(np.__version__)

This should print the installed NumPy version, confirming successful installation.

Step 4: Using NumPy

With NumPy installed, you can start using it in your Python scripts. Import NumPy as usual:

import numpy as np

Now you can create and manipulate NumPy arrays, perform calculations, and utilize the vast array of functions and operations provided by NumPy.

The post What is NumPy and How NumPy Works & Architecture? appeared first on Artificial Intelligence.

Python is a high-level programming language that was first released in 1991. It is an open-source language, which means that it is freely available for anyone to use and modify. Python has gained significant popularity over the years, and it is now one of the most widely used programming languages in the world.

Top Use Cases of Python

Python has a wide range of applications, and it is used in various industries. Some of the top use cases of Python include:

• Web Development: Python is widely used for web development, and it is the language of choice for many developers. Python has several frameworks such as Django and Flask, which make it easy to develop web applications.
• Data Science: Python is widely used in data science, and it is the language of choice for many data scientists. Python has several libraries such as NumPy, Pandas, and Matplotlib, which make it easy to work with data.
• Machine Learning: Python is widely used in machine learning, and it is the language of choice for many machine learning engineers. Python has several libraries such as Scikit-Learn, TensorFlow, and Keras, which make it easy to build machine learning models.

Features of Python

Python has several features that make it a popular language among developers. Some of the key features of Python include:

• Easy to Learn: Python has a simple and easy-to-learn syntax, which makes it easy for beginners to learn.
• Open-Source: Python is an open-source language, which means that it is freely available for anyone to use and modify.
• Interpreted Language: Python is an interpreted language, which means that it does not need to be compiled before it can be executed.
• Portable: Python is a portable language, which means that it can run on different platforms without any modifications.

Workflow of Python

The workflow of Python involves several steps, which include:

• Writing Code: The first step in the workflow of Python is to write code. Python code can be written using a text editor such as Notepad or an integrated development environment (IDE) such as PyCharm.
• Saving Code: Once the code has been written, it needs to be saved as a Python file with the .py extension.
• Compiling Code: Python code does not need to be compiled, but it needs to be interpreted. This means that the code needs to be run through a Python interpreter, which converts the code into machine-readable code.
• Executing Code: Once the code has been interpreted, it can be executed. Python code can be executed using a command line interface (CLI) or an IDE.

How Python Works & Architecture

Python works by interpreting the Python code and converting it into machine-readable code. The Python interpreter reads the code line by line and executes it in real-time.

The architecture of Python consists of several components, which include:

• Python Interpreter: The Python interpreter is the core component of the Python architecture. It reads and interprets the Python code.
• Standard Library: The standard library is a collection of modules that are included with Python. These modules provide functionality for tasks such as file I/O, networking, and threading.
• Third-Party Libraries: Python has a vast ecosystem of third-party libraries, which provide additional functionality for tasks such as data manipulation and machine learning.

How to Install and Configure Python

To install Python, follow these steps:

1. Go to the Python website and download the latest version of Python.(https://www.python.org/)
2. Run the installer and follow the instructions.
3. Once Python is installed, you can configure it by setting the environment variables and installing any necessary packages.

Fundamental Tutorials of Python: Getting Started Step by Step

Here’s a step-by-step guide to getting started with Python:

1. Installing Python: – Visit the official Python website at www.python.org. – Go to the Downloads section and choose the version suitable for your operating system (e.g., Windows, macOS, Linux). – Run the installer and follow the instructions to complete the installation.

2. Setting Up a Development Environment: – Once Python is installed, you can use any text editor or an Integrated Development Environment (IDE) to write and run your Python code. – Some popular IDEs for Python are PyCharm, Visual Studio Code, and IDLE (comes bundled with Python).

3. Writing Your First Program: – Open your preferred text editor or IDE. – Create a new file with a `.py` extension, such as `hello_world.py`. – Inside the file, write the following code:

     print("Hello, World!")
     

– Save the file.

4. Running the Program: – Open your command prompt or terminal. – Navigate to the directory where you saved your Python script (e.g., `cd path/to/your/script`). – Type the following command to execute the script:

     python hello_world.py
     

– You should see the output `Hello, World!` printed on the screen.

5. Learning Python Basics: – Once you have your environment set up, you can start learning the basics of Python. – Variables, data types, operators, control flow statements (if-else, loops), and functions are fundamental concepts to begin with. – There are plenty of online resources, tutorials, and books available to help you learn Python.

6. Practice and Explore: – Python is widely used in various domains, including web development, data analysis, machine learning, and more. – As you grasp the basics, try building small projects or solving coding challenges to cement your understanding. – Refer to official Python documentation (docs.python.org) to explore the vast capabilities and standard libraries of Python.

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The Python programming language has been in the game for so long, and it is here to stay.

Artificial intelligence projects are different from traditional software projects. The difference lies in the technology stack, the skills required for AI-based projects, and the need for in-depth research. To implement AI aspirations, you need to use a programming language that is stable, flexible, and has available tools. Python provides all of these, which is why we see many Python AI projects today.

Python facilitates developers to increase the confidence and productivity about their developing software from development to deployment and maintenance. The benefits of making Python the perfect solution for machine learning and AI-driven projects include simplicity and consistency, flexibility, access to powerful AI and machine learning (ML) libraries and frameworks, platform independence, and large communities. These things increase the popularity of the language.

A great library ecosystem

A good selection of libraries is one of the main reasons why Python is AI’s most popular programming language. A library is a module or group of modules released from different sources (PyPi). It includes a pre-written code segment that allows a user to use a particular function or perform various operations. The Python library provides base-level items, so developers do not have to write code from scratch every time.

Machine learning requires continuous data processing, and Python libraries allow you to access, process, and transform your data. These are some of the most extensive libraries available for AI and ML.

• Scikit-learn to handle basic ML algorithms such as clustering, logistic and linear regression, regression, and classification.
• Pandas are used for advanced structure and data analysis. It allows you to merge and filter data and collect data from other external sources (such as Excel).
• Keras is used for deep learning. In addition to the computer’s CPU, it also uses the GPU, allowing rapid calculations and prototyping.
• TensorFlow is used to manipulate deep understanding by building, training, and using artificial neural networks using substantial data sets.

Platform independence

Python is easy to use, learn, and it is versatile too. It means that Python, which is used to develop machine learning, can run on all platforms, including Windows, Linux, Unix, macOS, and 21 others. To shift the process from one platform to another, developers implement some minor changes and modify a few lines of code to create executable code for the selected platform. Developers can use software packages such as PyInstaller to prepare code to run on different platforms. That saves time and money on testing across other platforms and makes the process easier and more convenient.

Simple and Consistent

Python code is easy to understand and read. ML and AI support complex algorithms and common workflows, but Python’s ease of use allows developers to create reliable systems. Developers do not need to spend energy and time on language technicalities but can find Machine Learning problems. Another reason that attracts developers to use Python is its simplicity and ease of learning. Python is written with simple code and can easily create models for machine learning.

For some programmers, the great advantage of Python is that it is more intuitive than other programming languages. Different features, various web frameworks, libraries, and Python functionalities that simplify applications are advantageous. Python seems to be an excellent place to collaborate when several developers participate in a project. It is a universal language that can perform many complex machine learning tasks. Developers can quickly develop a prototype and test their products for machine learning purposes.

Good visualization options

We have mentioned that Python comes with many libraries, some of which are great visualization tools. However, AI developers need to point out that it is vital to represent data in a human-readable format in AI, deep learning, and machine learning.

Libraries such as Matplotlib enable data scientists to create histograms, graphs, and plots to improve understanding, display, and data visualization. Different application programming interfaces simplify the visualization process and help make clear reports.

A low entry barrier

There is a shortage of programmers around the world. Python is easy to learn a language – the barriers to entry are very low. Multiple data scientists can learn Python quickly to participate in machine learning projects. Believe it or not, Python is so similar to English that it’s easy to understand. Thanks to the simple phrase structure, you can confidently use complex systems.

Massive Community Support

Python has a large user community worldwide, and these communities are always helpful when coding errors occur. In addition to a large group of supporters, it also has multiple communities, forums, and groups where programmers can post questions about language to help each other. Having an active developer community is very useful for solving coding errors. These groups and communities include Python.org, GitHub, and Stack Overflow.

Versatility

Python is easy to use and supports various libraries and frameworks, making the language more versatile. However, it works in two categories.

1. Web development
2. Machine learning

One could say that there are multiple other appliances where Python cannot stand. For instance, it may be tough to program hardware-level or operating systems applications in it, and it can be challenging to provide this language to the SPA front end. However, it works very well on the backend.

Readability

Python is easy to read and understand, so Python developers have no problem understanding, modifying, copying, or pasting peer code. There is no confusion, errors, or inconsistent paradigms when using Python. That facilitates the efficient exchange of algorithms, tools, and ideas between AI and machine learning professionals. Tools like IPython provide other features like testing, debugging, and tab completion to simplify your workflow. That is why Python’s machine learning portfolio is the future of programming.

Growing popularity

Python is becoming the most common programming language in the world. It is the choice of many well-known brands (such as Google, Amazon, Quora, Facebook, and Netflix) because of its simplicity, versatility, and ease of maintenance. They are usually used for some of the most exciting and innovative technologies, such as artificial intelligence, machine learning, and robotics.

Python is in high demand in universities, and it has become the most popular introductory language. It is learned by skilled developers who want to expand their skill set. More and more companies and people are using Python. More resources have been created around it to help developers complete complex tasks without encountering coding problems.

Conclusion

AI, DL, and ML have a massive impact on the world we live in, and new solutions emerge every day. Businesses know there is no better time to invest in these technologies. Therefore, learning Python takes hours of work to build applications and systems. Given all the advantages of Python over other programming languages. it is clear which programming language to choose for AI, DL, and ML.

The post Why Python is Best for AI, ML, and Deep Learning appeared first on Artificial Intelligence.

Analytics Insight has selected the best laptops for Python programming.

Laptops for Python programming require a better battery life, speed, bigger screen size, powerful hard drive, a good keyboard and a bigger VRAM. Python is a vastly popular programming language and is widely used throughout the world. It’s easy to see why python is among the most used programs of today. It can understand the English language, which allows it to carry out simple commands with minimal fuss and effort. Python is used across a variety of platforms and systems worldwide, as well as with software applications and not every laptop is suitable for that. The following are some of the most suitable laptops for python programming.

MacBook Pro (2020)

The MacBook Pro, offers an all-round powerful processor, tons RAM, and a speed not comparable to most laptops on the market. Coming in at 16-inches, the computer still feels light and portable.This laptop is known for its top-notch display, powerful speakers, and speedy keyboard. This is a great laptop for all kinds of uses, including programming, design, art, and student life. Its CPU is 9th-generation Intel Core i7 – i9 and has AMD Radeon Pro 5300M – Radeon Pro 5500M for graphics along with a RAM of 16GB – 64GB. Its screen is a 16-inch Retina display with a storage of 512GB – 8TB SSD.

HP Spectre x360

The HP Spectre x360 2-in-1 laptop, is a great option for programmers looking for a new laptop. This version comes with 10th generation Intel Core processors and powerful Intel Iris Plus graphics. This model of the HP Spectre line takes the HP computer to the next level, especially in terms of battery life and sleek design. Its CPU is 10th-generation Intel Core i5 – i7 and has Intel Iris Plus for graphics, with a RAM of 8GB – 16GB. Its screen is 13.3 inches FHD, 1920 x 1080 resolution with a storage of 256GB – 2TB SSD.

Dell XPS 15

The Dell XPS 15 9500, is another great Dell product with a quality build. It offers a 10th generation i7 processor for extra speed. The trackpad and keyboard are flouted for its fast trackpad and intuitive navigation. On top of that, this robust laptop can be charged for more than 12 hours of battery life. It has a NVIDIA GTX 1650 Ti for graphics with a screen of Ultrasharp 4K Ultra HD display (3840 x 2160 resolution). It has 6GBs of DDR4 RAM.

Lenovo ThinkPad X1 Extreme

Lenovo’s ThinkPad X1, Extreme is one of the best laptops for programming, especially for those who like Lenovo. One perk of this laptop is that it’s far more configurable than most. It’s a solid laptop with a robust carbon-fiber and aluminum package. It has a CPU of 8th-generation Intel Core with Nvidia GeForce GTX 1050Ti with Max-Q for graphics. It has a RAM of 64GB along with a screen of 15.6” FHD (1920 x 1080 resolution). Its storage is 1TB SSD

Asus Chromebook Flip C436F/C436FA

Chromebooks are known for being a middle ground between a traditional laptop and a lightweight tablet. The Asus Chromebook Flip C436F is the best one out there, combining tons of power, premium build, and a cheaper price. It’s an ideal laptop for remote web developers who are on the move. Its CPU is 10th-generation Intel Core i3 – i5 and has Intel UHD for graphics. Its RAM is 8GB – 16GB along with 14” LED-backlit Full HD (1920 x 1080 resolution) for screen. It’s storage is 512GB, 256GB, or 128GB.

Microsoft Surface Laptop 3

The Microsoft Surface 3, is a great budget laptop for the 13.5-inch size. The powerful processor is capable of compiling complex programming languages. This laptop offers an overall great experience for programmers who like the Microsoft layout. Its CPU is 10th Gen Intel Core i3 along with Intel’s Iris Plus for graphics. Its RAM is 4GB – 16GB and a screen of 12.3” PixelSense Display. Its storage is 128GB – 1TB.

Acer Aspire E15 E5-576G-5762

Acer is a big name within the computing industry and this laptop offers great value for money. It’s ideal for python programming and comes with a reasonable budget too. The CPU is Intel Core i5 and offers speeds of 1.6 GHz. Acer Aspire E15 is able to handle programming software easily and comes with 8GB of RAM and 256GB of SSD memory. It also has a NVIDIA GEFORCE MX150 GPU. Since it has a NVIDIA GEFORCE GPU, it’s a strong contender for being one of the best laptops. Also, it has a backlit keyboard and a 15.6-inch HD display.

The post THE BEST LAPTOPS FOR PYTHON PROGRAMMING IN 2021 appeared first on Artificial Intelligence.

IBM’s Uncertainty Qualification 360 is an open source library of Python algorithms for quantifying, estimating, and communicating the uncertainty of machine learning models.

IBM has created an open source Python library, called Uncertainty Qualification 360 or UQ360, that provides developers and data scientists with algorithms to quantify the uncertainty of machine learning predictions, with the goal of improving the transparency of machine learning models and trust in AI.

Available from IBM Research, UQ360 aims to address problems that result when AI systems based on deep learning make overconfident predictions. With the Python toolkit, users are provided algorithms to streamline the process of quantifying, evaluating, improving, and communicating the uncertainty of predictive models. Currently, the UQ360 toolkit provides 11 algorithms to estimate different types of uncertainties, collected behind a common interface. IBM also provides guidance on choosing UQ algorithms and metrics.

IBM stressed that overconfident predictions of AI systems can have serious consequences. Examples cited included a chatbot being unsure of when a pharmacy closes, resulting in a patient not getting needed medication, and the life-or-death importance of reliable uncertainy estimates in the detection of sepsis. UQ exposes the limits and potential failure points of predictive models, enabling AI to express that it is unsure and increasing the safety of deployment.

Previous IBM efforts to advance trust in AI have included the AI Fairness 360 toolkit, which mitigates bias in machine learning models; the Adversarial Robustness Toolbox, which is a Python library for machine learning security; and the AI Explainability 360 toolkit, which helps users comprehend how machine learning models predict labels.

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Průzkumy RedMonk i Tiobe vycházejí z odlišných metodik a dávají rozdílné výsledky, v něčem se však shodují.

Aktuální přehled popularity programovacích jazyků, který pravidelně připravuje společnost Tiobe, ukazuje, že v v roce 2020 a na počátku roku 2021 se nejrychleji zvyšovala popularita Pythonu. V absolutním pořadí nejvíce lidí stále používá C, následuje Java, Python, C++ a C#. Růst obliby Pythonu podle Tiobe vyplývá ze všestrannosti, vysoké produktivity a relativně snadného učení tohoto jazyka; Python začínal jako konkurent Perlu pro psaní skriptů, ale mezitím se stal univerzálním prostředkem. Používá se v data science, pro psaní webových aplikací i aplikace strojového učení, v poslední době i pro mobilní aplikace nebo vestavěné systémy. Nicméně v univerzálnosti použití (zejména back end aplikace) ještě stále trochu zaostává za C a jeho variantami, respektive jazyky C mají náskok daný historicky. Obecně aplikace v C také nabízejí vyšší výkon.

Podle Tiobe by měl letos Python předstihnout Javu a stát se druhým nejoblíbenějším jazykem. Java tak o tuto pozici přijde poprvé za 20 let a v některých statistikách z konce roku 2020 se tak již stalo, zájem o Javu za poslední rok poklesl o 5 %. Ještě na počátku roku 2020 byla přitom Java populárnější i než C.

Žebříčky Tiobe jsou založeny především na zadávání názvů jednotlivých jazyků do vyhledávačů, tedy na poptávce; nevyplývá z nich například, v případě jakých jazyků profesionálové nejčastěji vystačí výhradně s jedinou znalostí. Zájem také nerovná se to, jak jsou pak vývojáři s daným jazykem (nebo konkrétními prostředími) spokojeni. Zde se uvádí, že při tvorbě webových i mobilních aplikací mají programátoři lepší zkušenost s JavaScriptem, respektive TypeScriptem než s Pythonem (JavaScript je v indexu Tiobe aktuálně na 7. příčce).

Jazyk R, používaný zejména ve statistice a datové vědě, se posunul z 18. na 9. místo. Tiobe dále zmiňuje, že svou pozici vylepšuje jazyk Julia. V roce 2018 se dostala mezi 50 nejoblíbenějších, nyní už míří top 20.

Pro srovnání, rovněž pravidelně zveřejňované statistiky RedMonk jsou shodné v tom, že také ukazují vzestup Pythonu a pokles Javy. Zde již Java opustila svoji pozici 2. nejoblíbenějšího jazyka v polovině roku 2020, a to také po velmi dlouhé době. RedMonk své žebříčky vytváří především na základě sledování kódů na GitHubu a diskusí na Stack Overflow. Čísla za konkrétní měsíc poměrně kolísají, větší smysl zde opět dávají dlouhodobé trendy. RedMonk uvádí, že Java se na svou pozici může kdykoliv vrátit. Kromě jazyků v první desítce se speciálně zmiňuje Rust (jazyk Mozilly aktuálně používá např. Apple, Amazon, Dropbox, Facebook, Google i Microsoft) a Kotlin (prosazovaný Googlem, od česko-ruské firmy JetBrains), jehož růst popularity se však v posledním cca roce zastavil.

Viz také: Roste obliba programovacího jazyka Rust

The post Index Tiobe: Python roste na úkor Javy appeared first on Artificial Intelligence.

Looking for powerful technology for your new project? The right framework makes application development simpler and faster. Your choice of tech stack can also influence the price and future possibilities of improving your business solutions.

You have heard about Python and you think it might be the best language to start your new app with? Here you’ll find some information about the most popular Python-based frameworks.

How to choose the right framework for your project

Surely, you know by now that there are plenty of technologies that could be used for web and mobile app development, creating advanced solutions for your company management and leveraging the newest methods of running a business. Python is a high-level general-purpose programming language, and it has many frameworks designed to overcome different development problems. It can be used in various projects.

But how do you decide which framework is the best for your company? Before you meet with consultants and developers from a software house, you should answer the following questions:

1. How big and complex will your desired business solution be?
2. Does your in-house team have a lot of experience in IT projects, and you’d like to choose your application structure and libraries, or would you rather the developers select it for you?
3. What quality of performance do you expect?
4. What is your preferred time-to-market?
5. What kind of special features do you expect your application will have?

You don’t have to be an expert on IT solutions for business and choose the only framework by yourself. Idego Groupconsultants have many years of experience in analyzing the business needs of small, medium, and large companies and suggesting the right solutions. All you have to do is to tell them what you require and ask about some tech stack that interests you – keep reading to learn about popular Python-based frameworks.

Flask

This is among the most popular Python-based frameworks in 2020. It provides a core set of functionalities: request handling, routing, WSGI compliance, templating, and flexibility. Using it, developers can make their own choice for matters like storage, database interaction, authentication, authorization, security solutions, and many others – as it allows for easy integration of your chosen solutions. The possibility to do so lets you keep your app free of libraries and pieces of code representing functionalities that your business solution doesn’t use at all. Apart from flexibility, you’ll benefit from scalability as Flask enables developers to scale applications easily.

Which projects should it be considered for?

• Web application development
• Building microservices
• MVP development in a short time
• Medium and large scale projects

Pyramid

This full-stack framework provides routing, renderers, and command-line tools for bootstrapping a project and enables the user to choose the libraries, database layer, and templating system. Developers can use a nice, large set of useful plugins for their projects. This may be a good choice if you expect to integrate your application with systems that would not be integrated easily with frameworks such as Django. By developing in Pyramid, developers can code with a minimalistic approach (it is similar to Flask in that regard). There is no need to deal with components that are not of high business value for your company. This results in better performance.

Which projects should it be considered for?

• Large applications
• For exposing existing Python code as a REST API
• Providing the core for web projects to be developed further

Django

Another full-stack, this Python-based framework is known as a great technology for building complex web applications. Often called “the web framework for perfectionists with deadlines”, Django is great for building advanced business products which need to be created in the shortest possible time. It comes with an automatically generated admin interface that makes quick development easier. Furthermore, it offers its object-relational mapper (ORM) for handling databases and standard solutions for authentication and authorization.

Which projects should it be considered for?

• Rapid development
• Small and large projects
• For web applications that will have to deal with high traffic
• For solutions that will contain a lot of content
• For developing API-only backend applications

Choosing the best technology is not as easy as it might seem. Always remember to analyze all your business needs carefully before making a choice and consult with experts.

The post Which Python-based framework will be the best for your next project? appeared first on Artificial Intelligence.

According to a recent study, almost 27% of the advertised jobs require Python as a core skill, up from 18.5% at the beginning of the year.

Python is one of the TIOBE Index programming languages of the year. It has become the go-to language for developers working on data science and machine learning for a few years now. 

What sets Python apart is its intuitive features like libraries, high productivity, ease of learning etc. According to a recent study, almost 27% of the advertised jobs require Python as a core skill — up from 18.5% at the beginning of the year.

Last October, Python released version 3.9. The updates included improvements in Python internals, performance boosts, dictionary union operators, handy new string functions, new type operations, consistent and stable internal APIs and more.

What To Expect From 3.10

Python started work on the upcoming version, Python 3.10 pre-alpha, in May 2020. Python version 3.10 is set to be released on 4 October 2021. 

However, with the pre-alpha, the company has unveiled an early developer preview of Python 3.10.

The current alpha version of 3.10 is 3.10.0a6, which is the sixth of seven planned alpha releases. During the alpha phase, features may be added up until the start of the beta phase and, if necessary, may be modified or deleted until the release candidate phase. 

The significant new features of the Python 3.10 version are mentioned below:

Structural Pattern Matching

The pattern matching feature has been added in the form of a match statement and case statements of patterns with associated actions. Patterns usually consist of sequences, primitive data types mappings, as well as class instances. Pattern matching will help developers extract information from various complex data types, branch on the structure of data, and apply specific actions based on different data forms.

Better Error Messages In The Parser

When parsing code that contains unclosed parentheses or brackets the interpreter now includes the location of the unclosed bracket of parentheses instead of displaying SyntaxError: unexpected EOF while parsing or pointing to some incorrect location. Previous versions of the interpreter reported confusing places like the location of the syntax error but in Python3.10 a more informative error is emitted.

Parameter Specification Variables

Parameter Specification Variables feature supports forwarding the parameter types of one callable over to another callable, making it difficult to annotate function decorators. This feature proposes typing.ParamSpec and typing.Concatenate that help in specifying the type of a callable. 

Precise Line Numbers for Debugging and Other Tools

According to its developers, Python should guarantee that when tracing is turned on, “line” the tracing events are generated for all lines of code executed and only for lines of code that are executed. A side effect of ensuring correct line numbers is that some bytecodes will need to be marked as artificial and to assist such tools, a new co_lines attribute will be added that describes the mapping from bytecode to source.

Deprecate Distutils Module

Distutils is an undocumented and unmaintained collection of utilities for packaging and distributing Python packages, including the compilation of native extension modules. This feature defines a configuration format that describes a Python distribution and provides the tools to convert a directory of source code into a source distribution and binary distribution forms.

In both the version of Python 3.10 and 3.11, distutils will be formally marked as deprecated. Meaning, all the known issues will be closed at this time, and import distutils will raise a deprecation warning. During Python 3.10 and 3.11, uses of distutils within the standard library may change to use alternative APIs. However, in Python 3.12, distutils will no longer be installed by make install or any of the first-party distributions. 

The post Python 3.10: What To Expect appeared first on Artificial Intelligence.

Nástrojů pro zpracování vstupů existuje v linuxových distribucích celá hromada. Chtěli byste pro tuhle činnost používat syntaxi Pythonu? Představíme vám užitečnou utilitu pz, jako Pythonize.

Chtěl bych vám představit utilitu pz, jako Pythonize, určenou pro vás, pokud jste uživatelem příkazové řádky se znalostí Pythonu. Současné linuxové distribuce mají k dispozici mnoho efektivních nástrojů pro zpracování vstupu. Přáli jste si někdy, abyste místo nich mohli použít syntaxi Pythonu? Listujete často manuálem, jak že se chovají ty přepínače na zpracovávání vstupu? Připadá vám zdrojový kód Bashe obtížně čitelný? Právě pro vás je tento článek. Dozvíte se, jak si napsat maličký program a jak je vyhodnocován, které proměnné v něm máte k dispozici, pár slov k auto-importu, přepínačům a ukážeme si některé příklady použití.

Příkazová řádka je vynikající rozhraní, jež se vyznačuje ohromujícím globálním… když ne přímo nadšením, pak dosahem. Do terminálu se lze připojit málem už na poslední pračce. Někteří uživatelé se příkazové řádky obávají, nicméně doposud neexistují dokonalé grafické aplikace umožňující nám vyrovnat se s šíří úkolů, jimž člověk před klávesnicí čelí. Možná jste guru GNU coreutils a znáte všechny přepínače programů tr a cut, možná děláte jazykovou korekturu v  sed u místo ve Wordu a nákupní seznamy píšete v awk, pak utilitu pz nepotřebujete. Pokud jste ale strávili tři hodiny hledáním zapomenuté mezery v podmínce Bash skriptu a umíte si představit lepší využití pro své odpoledne, možná váš život bude už večer jednodušší. Netřeba již více umět rozličné syntaxe, umíte-li syntaxi Python.

Proč ale nepoužít přímo Python na příkazové řádce, copak nelze použít přepínač -c a vyhodnotit kód?

python -c "print(1)" # 1

Ano, samozřejmě lze. Ale zkuste si zpracovat složitější úkol a brzy utonete při zpracovávání vstupu a výstupu. Přestože je výpočet na jednu řádku, už jenom import knihoven na zacházení s rourami způsobí pořádné bolení hlavy. Zvlášť když přihlédneme k tomu, že Python nevhodný na jednořádkové příkazy, které tu působí monstrózně; na ty je tu například PERL. Utilita pz umožňuje zanedbat všechny ty řádky jako shebang #!/usr/bin/env python3, loggování, konverzi mezi byty a stringem a tak dále; všechno to, co byste kvůli jednořádkové funkcionalitě psali stále dokola.

Při navrhování rozhraní jsme se pokusili použít pravidlo intuice – pokud by existoval ideální program, jak přesně by ho uživatel chtěl použít? Základem jsou proměnné, přepínače a klauzule. Některé proměnné se automaticky doplňují, některé jsou předpřipraveny pro vás, abyste je nemuseli inicializovat a další řídí výstup. Klauzule obsahují váš příkaz nebo jejich sekvence a vyhodnocují se před, během a po zpracování vstupu a přepínače mění jejich chování.

Nechme tedy Python dělat ty věci, pro které nám přirostl k srdci, jako vykrajování podřetězců pomocí hranatých závorek [start:stop:step] nebo generátorová notace. Utilita pz vám přesně toto umožňuje. K dispozici máte proměnnou s, která vždy obsahuje právě zpracovávaný řádek. Změníte-li ji, změní se výstup. Pošleme nyní na vstup slovíčko ‚ahoj‘ a nechme vyříznout podřetězec mezi 1. a 3. znak, tedy chtějme dostat ‚ho‘. (Za mřížkou značím návratovou hodnotu programu.)

# vykrojíme řetězec
echo "ahoj" | pz s[1:3] # "ho"

Kdybychom použili přepínač --verbose, k zobrazení detailních informací, zjistili bychom, že utilita na pozadí vyhodnotila, že se proměnná s při vykonávání příkazu nijak nemění. A upravila tedy výraz o zpětné přiřazením do proměnné s tímto způsobem: s = s[1:3]. Proto není třeba do s zpátky explicitně přiřazovat. Mezi další automaticky doplňované proměnné patří n, která obsahuje aktuální řádku konvertovanou na číslo (lze-li). V následujícím příkladu přičteme k prvku na indexu 1 sedmičku. Zároveň je vidět, že volání utility lze libovolně řetězit.

# rozdělíme na čárce a přičteme sedmičku
echo "ahoj,5" | pz 's.split(",")[1]' | pz n+7 # 12

K dalším automaticky doplňovaným proměnným patří counter (číslo aktuální řádky), text (celý text v jediném řetězci), lines (celý text v listu řádků), numbers (celý text jako list, přičemž každý řádek je změněn na číslo). Utilita myslí na přetékání (automatické doplňování proměnných lze vypnout) i průtok – proměnná text je k dispozici implicitně až po zpracování celého textu, abychom bezpečně mohli zpracovávat nekonečný vstup libovolně dlouho. Podívejme se například na proměnnou numbers. Na vstupu dostáváme postupně čtyři čísla. Naše klauzule určuje, že přes proměnnou s se na výstup dostane entice ve formátu: číslo aktuální řádky, aktuální řádka a aritmetický průměr.

$ echo -e "20\n40\n25\n28" | pz 's = counter, s, sum(numbers)/counter'
1, 20, 20.0
2, 40, 30.0
3, 25, 28.333333333333332
4, 28, 28.25

Zatím jsme viděli pouze klauzuli main, která se spouští pro každou řádku vstupu. Existují i klauzule --setup, která se vyhodnocuje úplně na začátku (pro případ, že je třeba iniciovat některé proměnné) a zrcadlově k tomu --end, která se spustí jedinkrát na závěr. Pro jednoduchý příklad chtějme spočítat délku celého textu.

echo -e "hello\nworld" | pz --end 'len(text)' # 11

Filtrování lze snadno realizovat pomocí přepínače --filter  – řádka se pošle dále podle booleanu, na který se přetypuje výsledek klauzule main. Obdobně pracuje proměnná skip, kterou stačí nastavit pravdivostní hodnotu. Pošleme dál například jen čísla, která jsou větší než tři.

$ echo -e "1\n2\n3\n4\n5" | pz "skip = not n > 3"
4
5

Pro užití regulární výrazů máme speciální přepínače. Není tedy třeba importovat z modulu re dané funkce a ztratit se v lese uvozovek a apostrofů, stačí využít --search, --match, --findall či --sub. Pokud se vám do textu zatoulala URL, přičemž na jedné řádce jich může být i více, prožeňte každou řádku přepínačem findall. Klauzule main se pak automaticky doplní na  s = re.findall(výraz, vstup).

echo "Lorem http://example.com ipsum http://example.cz dolor" |  pz --findall "(https?://[^\s]+)"
http://example.com
http://example.cz

Když se zaměříte na výstup, zjistíte, že pro jednu řádku vstupu (tu jedinou), se nám vrátilo více řádek. Ano, i to je možné. Výstup se upraví podle toho, co v proměnné s zbude po procesování řádku. Je-li to n-tice či generátor, vypíše se jako jeden řádek oddělený čárkami. Zbude-li list, dostaneme řádků více. Najdeme-li volatelný výraz, zavoláme ho. To je důvod, proč můžeme napsat pouhé s.lower bez závorek, abychom snížili text.

echo "HEllO" | pz s.lower # s = s.lower() → "hello"
echo "HEllO" | pz len # s = len(s) → "5"
echo "25" | pz sqrt | pz round # s = math.sqrt(n) → s = round(n) → "5"

Jak pracuje automatický import? Bylo by velmi nepraktické, kdyby uživatel musel používat klauzuli --setup používat i pro import těch nejobyčejnějších funkcí. Zároveň načítat všechny dostupné moduly naráz by utilitu zpomalilo na nesnesitelnou úroveň, nemluvím-li ani o problémech, které by vyvstaly při duplicitním jménu funkce u různých modulů. Utilita přistupuje na kompromis: Část funkcí je importována od začátku, například všechny symboly z knihovny math, aby uživatel mohl sečíst všechna čísla na vstupu pouhým pz --end sum (které se interně přeloží jako pz --end "s=sum(numbers)". Další množství funkcí je připraveno v záloze, aby se importovaly, jakmile zpracovávání vstupu selže kvůli jejich nepřítomnosti. Například modul requests, aby uživatel mohl načíst webovou stránku pouhým  echo "http://example.com" | pz 'requests.get(s).content'.

Jisté množství symbolů je importováno, funkce sleep z modulu time, funkce datetime z modulu datetime, dříve viděný randint z modulu random nebo celý modul random, aby uživatel mohl snadno použít náhodný výběr přes funkce  random.choice.

Na závěr si předložme ještě jednu komplikovanější výzvu. Stojíme před úkolem, kdy chceme ověřit, k čemu budou tíhnout náhodná čísla, generovaná funkcí random.randint. Nezajímá nás celý průběh, ale dejme tomu každá desetitisící hodnota. Jak na to? Za prvé zde nepotřebujeme žádný vstup. Použijeme přepínač --generate s hodnotou nula, která zajistí nekonečný vstup. Zahodíme všechny řádky, jež nejsou dělitelné deseti tisíci, zbylé vypišme na konzoli ve formátu číslo aktuálního řádku a aktuální průměr hodnot. Hodnoty se drží okolo padesátky.

pz "i+=randint(1,100); s = (counter,i/counter) if not counter % 10000 else None" --generate 0
10000, 50.4153
20000, 50.35645

Utilita nemá žádné závislosti, pouze standardní Python ve verzi alespoň 3.6 (která je s námi už čtyři roky). Nainstaluje se balíčkovačem pip, případně stačí stáhnout a spustit jediný soubor. Její kód je hostován jako GPLv3 na adrese GitHub.com/CZ-NIC/pz , kde také naleznete kompletní dokumentaci a množství příkladů.

The post Pythonize (pz): když chcete na řádce Python místo Bashe appeared first on Artificial Intelligence.

Arrow is a flexible Python library designed to create, format, manipulate, and convert dates, time, and timestamps in a sensible and human-friendly manner. It provides an intelligent module API that allows dealing with dates and times with a few code lines and imports. The library is named after the “time’s arrow” concept suggesting the one-directional progress of time. It is inspired by Requests (an HTTP library) and Moment.js (a JavaScript date library).

Why Arrow?

Standard Python library and low-level conventional modules are available for the various date and time functionalities but have the following limitations from a user’s point of view:

• Too many modules available such as time, datetime, dateutil, calendar and pytz
• A large number of data types to choose from such as datetime, date, time, timedelta, tzinfo and so on
• Users may lack experience in handling different time zones
• Inefficient ways of timestamps and timezone conversions
• Gaps in functionalities such as humanization (e.g. converting a date into human-readable format) and ISO 8601 parsing

Highlighting Features of Arrow

• Supports Python 3.6+ versions
• A fully-implemented, efficient alternative to datetime standard library
• Aware of timezones (uses UTC (Coordinated Universal Time) by default) and enables easy timezone conversion.
• Automatic formatting and parsing of strings
• Widely supports ISO 8601 standard date and time format. 
• Supports pytz, ZoneInfo and dateutil objects
• Can generate floors, ceilings, spans and ranges for time frames ranging from microseconds to years
• Can be extended to users’ own Arrow-derived datatypes
• Supports PEP 484-style type hints

Installing Arrow

Install Arrow using pip command as follows:

pip install -U arrow

Practical Implementation

Here’s a demonstration of performing various operations using Arrow. The experiments have been done in Google colab with Python version3.7.10. Explanation of each operation along with its output is as follows:

First, import the Arrow library

import arrow as arw

1. Represent current time in the given timezone

arw.now()

With no timezone specified, an Arrow object representing current UTC time will be created. Same output results on executing

arw.utcnow()

Output: <Arrow [2021-03-16T10:39:26.746385+00:00]>

To get current time in say US/Pacific timezone:

arw.now(‘US/Pacific’)

Output: <Arrow [2021-03-16T03:48:10.893440-07:00]>

2. Convert a specified integer or float number into a floating-point UTC timestamp

arw.get(1567900664)

Output: <Arrow [2019-09-07T23:57:44+00:00]>

 #Try with a floating point input
 arw.get(17900664.5463877) 

Output: <Arrow [1970-07-27T04:24:24.546388+00:00]>

Verify the converted output here.

3. String parsing

arw.get('12-03-2020 22:30:25', 'MM-DD-YYYY HH:mm:ss')

Specified date and time will be represented as MM-DD-YYYY HH:mm:ss 

format

Output: <Arrow [2020-12-03T22:30:25+00:00]>

4. Extract date from a string

arw.get('I was born on March 12 1990','MMMM DD YYYY')

‘MMMM DD YYYY’ formatted date will be searched in the string

Output: <Arrow [1990-03-12T00:00:00+00:00]>

5. Instantiate Arrow object by directly providing a datetime argument

arw.Arrow(2020,12,26)

Output: <Arrow [2020-12-26T00:00:00+00:00]>

6. Get a datetime representation an Arrow object

x = arw.now()

Suppose, x is <Arrow [2021-03-16T11:49:17.908895+00:00]>

x.datetime

Output: datetime.datetime(2021, 3, 16, 11, 49, 17, 908895, tzinfo=tzlocal())

The components can then be separated as:

x.month    

Output: 3

x.year

Output: 2021

x.day

Output: 16

x.hour

Output: 11

Datetime functions can be called to get properties of the Arrow object

x.time()

Output: datetime.time(11, 49, 17, 908895)

7. Replace one or more components of the Arrow object

a = arw.now()

Suppose, ‘a’ is <Arrow [2021-03-16T12:00:13.500164+00:00]>

a.replace(year=2019, second=45, month=11)

will give the output: <Arrow [2019-11-16T12:00:45.500164+00:00]>

8. One or more Arrow object attributes can be shifted forward or backward

present = arw.now()

Suppose, ‘present’ object is <Arrow [2021-03-16T12:08:34.530495+00:00]>

Go forward in time by 2 years

present.shift(years=+2)

Output: <Arrow [2023-03-16T12:08:34.530495+00:00]>

Go backward by 4 hours

present.shift(hours=-4)

Output: <Arrow [2021-03-16T08:08:34.530495+00:00]>

9. Represent date and time in the required format

arw.now().format('HH:MM:SS MM:DD:YYYY')

Output: 12:03:00 03:16:2021

(i.e. 12 hrs 3 mins 00 sec, 16 March 2021)

10. Convert default UTC to specified timezone 

utc = arw.utcnow()

Suppose, utc is <Arrow [2021-03-16T12:20:43.301159+00:00]>

It can be converted to US/Pacific time zone as follows:

utc.to('US/Pacific')

Output: <Arrow [2021-03-16T05:20:43.301159-07:00]>

11. Humanization examples

Humanize relative to current time:

 future = arw.now().shift(minutes=+2)  #advance by 2 mins
 future.humanize() 

Output: ‘in 2 minutes’

Humanize relative to another datetime or Arrow object

 present = arw.now()  #current time
 future = present.shift(days=-3)  #go back by 3 days
 #how far is present from future
 future.humanize(present)  

Output: ‘3 days ago’

 #how far is future from present
 present.humanize(future)  

Output: ‘in 3 days’

To get only the distance:

future.humanize(present, only_distance=True) 

Output: ‘3 days’

Indicate time difference in terms of one or more specific time granularities like hours,  

 minutes etc.

future.humanize(present, granularity="minute")

Output: ‘4320 minutes ago’  #3 days equals 4320 mins

Multiple granularities can be specified as:

future.humanize(present, granularity=["minute","second"])

Output: ‘in 4320 minutes and 0 seconds’

12. Get the span of any time unit

arw.now().span(‘minute’)  

Output:

 (<Arrow [2021-03-16T12:58:00+00:00]>,
  <Arrow [2021-03-16T12:58:59.999999+00:00]>) 

arw.now().span('day')  #get the span of time for a whole day

Output:

 (<Arrow [2021-03-16T00:00:00+00:00]>,
  <Arrow [2021-03-16T23:59:59.999999+00:00]>) 

13. Get floor and ceiling values of a specific time component

arw.now().floor('minute')

Output: <Arrow [2021-03-16T13:04:00+00:00]>

arw.now().ceil('minute')

Output: <Arrow [2021-03-16T13:04:59.999999+00:00]>

14. Get the range of specified timespans

 import datetime
 begin = datetime.datetime(2020, 6, 5, 2, 30) #start time
 end = datetime.datetime(2020, 6, 5, 4, 50)  #end time
 for r in arw.Arrow.range('hour', begin, end):  #iterate in terms of hour
     print(r) 

We want time from 2:30 to 4:50 at the interval of an hour so output will contain 2:30, 3:30 and 4:30 times.

Output:

 2020-06-05T02:30:00+00:00
 2020-06-05T03:30:00+00:00
 2020-06-05T04:30:00+00:00 

15. FACTORIES can be used for utilizing Arrow’s module API to create a custom Arrow-derive type.

Suppose, we need to find difference in terms of days between today and the Christmas day. We can define a custom class as:

 class Custom(arw.Arrow):  #pass an Arrow object
      def till_christmas(self):  #define a function for computation
   #store christmas day of given year
          christmas = arw.Arrow(self.year, 12, 25)
 “””
 If given date falls comes after Christmas of that year, compute its difference w.r.t. Christmas of the next year
 “””
          if self > christmas:
              christmas = christmas.shift(years=1)
          return (christmas - self).days  #return the difference 

 f = arw.ArrowFactory(Custom) #Create a factory
 x = f.now()  #current date and time 

Suppose, x is <Custom [2021-03-16T13:21:09.741500+00:00]>

Call the till_christmas() method to compute the difference 

x.till_christmas()

Output: 283

i.e. Christmas of the year 2021 is 283 days away from 16 March 2021.

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