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In this lecture you will learn about entryboxes and how to manipulate them.

I’m currently learning Python Programming to build a strong foundation in coding. This course is helping me understand how to write clean, efficient code and solve real-world problems using Python. I'm excited to apply these skills in web development, data analysis, and automation.

Thomas Wiecki - Probabilistic Programming in Python [EuroPython 2014] [24 July 2014] Probabilistic Programming allows flexible specification of statistical models to gain insight from data. The high interpretability and ease by which different sources can be combined has huge value for Data Science. PyMC3 features next generation sampling algorithms, an intuitive model specification syntax, and just-in-time compilation for speed, to allow estimation of large-scale probabilistic models. ----- Probabilistic Programming allows flexible specification of statistical models to gain insight from data. Estimation of best fitting parameter values, as well as uncertainty in these estimations, can be automated by sampling algorithms like Markov chain Monte Carlo (MCMC). The high interpretability and flexibility of this approach has lead to a huge paradigm shift in scientific fields ranging from Cognitive Science to Data Science and Quantitative Finance. PyMC3 is a new Python module that features next generation sampling algorithms and an intuitive model specification syntax. The whole code base is written in pure Python and Just-in-time compiled via Theano for speed. In this talk I will provide an intuitive introduction to Bayesian statistics and how probabilistic models can be specified and estimated using PyMC3.

Thomas Wiecki - Probabilistic Programming in Python [EuroPython 2014] [24 July 2014] Probabilistic Programming allows flexible specification of statistical models to gain insight from data. The high interpretability and ease by which different sources can be combined has huge value for Data Science. PyMC3 features next generation sampling algorithms, an intuitive model specification syntax, and just-in-time compilation for speed, to allow estimation of large-scale probabilistic models. ----- Probabilistic Programming allows flexible specification of statistical models to gain insight from data. Estimation of best fitting parameter values, as well as uncertainty in these estimations, can be automated by sampling algorithms like Markov chain Monte Carlo (MCMC). The high interpretability and flexibility of this approach has lead to a huge paradigm shift in scientific fields ranging from Cognitive Science to Data Science and Quantitative Finance. PyMC3 is a new Python module that features next generation sampling algorithms and an intuitive model specification syntax. The whole code base is written in pure Python and Just-in-time compiled via Theano for speed. In this talk I will provide an intuitive introduction to Bayesian statistics and how probabilistic models can be specified and estimated using PyMC3.

Thomas Wiecki - Probabilistic Programming in Python [EuroPython 2014] [24 July 2014] Probabilistic Programming allows flexible specification of statistical models to gain insight from data. The high interpretability and ease by which different sources can be combined has huge value for Data Science. PyMC3 features next generation sampling algorithms, an intuitive model specification syntax, and just-in-time compilation for speed, to allow estimation of large-scale probabilistic models. ----- Probabilistic Programming allows flexible specification of statistical models to gain insight from data. Estimation of best fitting parameter values, as well as uncertainty in these estimations, can be automated by sampling algorithms like Markov chain Monte Carlo (MCMC). The high interpretability and flexibility of this approach has lead to a huge paradigm shift in scientific fields ranging from Cognitive Science to Data Science and Quantitative Finance. PyMC3 is a new Python module that features next generation sampling algorithms and an intuitive model specification syntax. The whole code base is written in pure Python and Just-in-time compiled via Theano for speed. In this talk I will provide an intuitive introduction to Bayesian statistics and how probabilistic models can be specified and estimated using PyMC3.

Asynchronous Python Programming with Asyncio and Async_await - Learn to Write Robust and Modern Asynchronous Code

Asynchronous Python Programming with Asyncio and Async_await - Learn to Write Robust and Modern Asynchronous Code

We provide a brief description of the Python-DTU system, including the overall design, the tools and the algorithms that we plan to use in the agent contest.

Python is one of the powerful, high-level, easy to learn programming language that provides a huge number of applications. Some of its features, such as being object-oriented and open-source, having numerous IDE’s, etc. make it one of the most in-demand programming languages of the present IT industry.  According to TIOBE index, as of January 2020, Python is one of the popular programming languages. By looking at the popularity of this programming language, many IT professionals, both beginners as well as experienced alike, are willing to build their career as a Python developer.  Readers reviews: This is a VERY short introduction to Python and makes a number of mistakes throughout. For example, this is a piece of code: if True; print “True”; The first semicolon “;” should be a colon, and the second colon in the print line should not be there. The semicolon is used many times where it should not have appeared.

Description: This video is a complete beginner-friendly introduction to Python programming, created by Imarticus Learning . It is designed to help aspiring coders, students, and professionals take their first steps into the world of programming with one of the most popular and beginner-friendly languages in the tech industry. In This Video, You Will Learn: What is Python and why it's so widely used Applications of Python in web development, data science, artificial intelligence, and automation Key Python concepts: syntax, variables, data types, operators, control flow (if-else, loops), and functions Real-world use cases, automation tips, and beginner Python projects About Imarticus Learning: This video is part of the Postgraduate Program in Data Science and Analytics (PGPDSA) , a 6-month career-launching course for graduates and young professionals. The program includes: 300+ hours of training 25+ hands-on projects 10+ tools including Python, Tableau, and Power BI 100% job assurance via 2,000+ hiring partners Highest package of ₹22.5 LPA and average salary hikes of 52% Learn more about the program

Description: This video is a complete beginner-friendly introduction to Python programming, created by Imarticus Learning . It is designed to help aspiring coders, students, and professionals take their first steps into the world of programming with one of the most popular and beginner-friendly languages in the tech industry. In This Video, You Will Learn: What is Python and why it's so widely used Applications of Python in web development, data science, artificial intelligence, and automation Key Python concepts: syntax, variables, data types, operators, control flow (if-else, loops), and functions Real-world use cases, automation tips, and beginner Python projects About Imarticus Learning: This video is part of the Postgraduate Program in Data Science and Analytics (PGPDSA) , a 6-month career-launching course for graduates and young professionals. The program includes: 300+ hours of training 25+ hands-on projects 10+ tools including Python, Tableau, and Power BI 100% job assurance via 2,000+ hiring partners Highest package of ₹22.5 LPA and average salary hikes of 52% Learn more about the program

Description: This video is a complete beginner-friendly introduction to Python programming, created by Imarticus Learning . It is designed to help aspiring coders, students, and professionals take their first steps into the world of programming with one of the most popular and beginner-friendly languages in the tech industry. In This Video, You Will Learn: What is Python and why it's so widely used Applications of Python in web development, data science, artificial intelligence, and automation Key Python concepts: syntax, variables, data types, operators, control flow (if-else, loops), and functions Real-world use cases, automation tips, and beginner Python projects About Imarticus Learning: This video is part of the Postgraduate Program in Data Science and Analytics (PGPDSA) , a 6-month career-launching course for graduates and young professionals. The program includes: 300+ hours of training 25+ hands-on projects 10+ tools including Python, Tableau, and Power BI 100% job assurance via 2,000+ hiring partners Highest package of ₹22.5 LPA and average salary hikes of 52% Learn more about the program

python exercises

You'll learn about the tkinter library and what kinda widget we can create with them, we will go over how to create windows, buttons, listbox, scrollbars, etc in this lecture Have you ever wanted to know how your application is programmed on the computer. This lecture will show you how easy it is to program. Aimed at beginner programmers or people that has no programming experience. For you to watch this class, you must have python installed from python.org . Lecture 2 will show you how to install and run python. This lecture assumes that you already have python. The python course series is designed to be short, fun, and concise. They are 10 minutes each with fun examples and easy instruction. If you want to learn programming this is the way to go.

Your first class on how python functions work, how do you port functions from a different file into your code Have you ever wanted to know how your application is programmed on the computer. This lecture will show you how easy it is to program. Aimed at beginner programmers or people that has no programming experience. For you to watch this class, you must have python installed from python.org . Lecture 2 will show you how to install and run python. This lecture assumes that you already have python. The python course series is designed to be short, fun, and concise. They are 10 minutes each with fun examples and easy instruction. If you want to learn programming this is the way to go.

File ini diperuntukan bagi yang beli buku-nya dan CD dari buku tersebut hilang atau rusak.

Programming Computer Vision With Python

Programming Computer Vision with Python Tools and algorithms for analyzing images terjemahan Indonesia

Programming software

Python An Introduction To Programming ( Chapter 9 Multimedia)

Python programming language by Tutorials Point.

Python programming language by Tutorials Point.

Python - Oriented Programming - Fastener Joint Design

Python - Oriented Programming - Fastener Joint Design

[EuroPython 2014] Benoit Chesneau - Concurrent programming with Python and my little experiment [EuroPython 2014] [25 July 2014] Concurrent programming in Python may be hard. A lot of solutions exists though. Most of them are based on an eventloop. This talk will present what I discovered and tested along the time and my little experiment in porting the Go concurrency model in Python. ----- Concurrent programming in Python may be hard. A lot of solutions exists though. Most of them are based on an eventloop. This talk will present what I discovered and tested along the time with code examples, from asyncore to asyncio, passing by gevent, eventlet, twisted and some new alternatives like evergreen or gruvi. It will also present my little experiment in porting the Go concurrency model in Python named [offset](http://github.com/benoitc/offset), how it progressed in 1 year and how it became a fully usable library . This presentation will be an update of the presentation I gave at the FOSDEM 2014. It will introduce to the concurrency concepts and how they are implemented in the different libraries.

random3, 'Andrew Park - Data Science for Beginners_ 4 Books in 1_ Python Programming, Data Analysis, Machine Learning. A Complete Overview to Master The Art of Data Science From Scratch Using Python for Busines'

This paper empirically enlists Python plagiarism attacks that have been found on Introductory Programming course assignments for undergraduate students. According to our observation toward 400 plagiarism-suspected cases, there are 35 plagiarism attacks that have been conducted by students. It starts with comment & whitespace modification as the most frequent attack and ends with replacing regular instruction with API-based instruction as the least frequent one. In addition to such primary finding, we have also found two additional findings. First, when classified based on Faidhi & Robinson's taxonomy, the occurrence trend of such attacks is not proportional to increasing plagiarism level due to the nature of Python programming language, course syllabus, and student preferences. Second, incorporated plagiarism attacks are proportional to student experience, even though such relation is, sometimes, mitigated by student understanding and assignment restrictions.

Gregory M. Kapfhammer https://pyohio.org/2018/schedule/presentation/32/ Real-world software engineering is collaborative, commonly involving the use of Git, GitHub, and continuous integration with Travis CI. Suitable for both full-time educators and members of industry who educate their peers, this presentation will explain how to use these technologies and platforms to teach interdisciplinary and introductory courses in computer programming and software engineering. The presentation will first show how to create a GitHub organization connected to a GitHub Classroom with unlimited private repositories that contain instructor solutions and starter kits and assignment submissions from both individual and team-based programming assignments. The talk will next explain how to connect GitHub repositories to continuous integration servers hosted by Travis CI, thus supporting the cloud-based execution of tests and checks. The presentation will subsequently introduce a Python program that supports the local and cloud-based checking of a student's source code and technical writing for a programming project. Ultimately, people who attend this talk will learn more about how to use open-source software and the Python programming language to effectively deliver instructional materials and assess assignment submissions. === https://pyohio.org A FREE annual conference for anyone interested in Python in and around Ohio, the entire Midwest, maybe even the whole world.

Learn how to Code:

Python. An Introduction To Programming ( Chapter 6 Classes)

Python. An Introduction To Programming ( Chapter 6 Classes)

python is a programming language. which is very helpfull to create website.

Kenneth and Wynn caught up with Glyph Lefkowitz from Twisted to talk about the project and evented programming in Python.

Kenneth and Wynn caught up with Glyph Lefkowitz from Twisted to talk about the project and evented programming in Python.

Summary: Xoke continues the Programming 101 series Series: Programming 101 Source: http://hackerpublicradio.org/eps.php?id=0282 print ("Hello World") print ("Hello \"World") x = 1 print (x) x = 1 x = x + 5 x = x * 7 print (x) x = 1 + 5 * 7 print (x) x = (1 + 5) * 7 print (x) # This is a comment x = (1 + 5) * 7 # print ("5") print (x) x = "Hello World" print (x) x = "Hello" y = "World" print (x+y) x = "Hello" y = "World" print (x + " " + y) sFirstName = "John" sSurname = "Smith" print ("Dear " + sFirstName + " " + sSurname) x = 13 sFirstName = "John" sSurname = "Smith" if x < 12: print ("Good Morning " + sFirstName + " " + sSurname) else: print ("Good Evening " + sFirstName + " " + sSurname) x = 11 sFirstName = "John" sSurname = "Smith" if x < 12: print ("Good Morning " + sFirstName + " " + sSurname) else: print ("Good Evening " + sFirstName + " " + sSurname) print ("When does this get printed?")

Summary: Xoke continues the Programming 101 series Series: Programming 101 Source: http://hackerpublicradio.org/eps.php?id=0302 for x in range(1, 10): print(x) for x in range(1, 11): print(x) y = 0 for x in range(1,101): y = y + x print(y) y = 0 x = 1 while x < 101: y = y + x x = x + 1 print (y) x = 0 y = 0 z = 1 while z < 100: x = y y = z z = x + y print (z) x = 0 y = 0 z = 1 while z < 100: print (z) x = y y = z z = x + y x = 0 y = 0 z = input('What number do we start from?') i = input('And up to which number should we calculate') while z < i: print (z) x = y y = z z = x + y

Bethany Poulin https://www.pyohio.org/2019/presentations/139 ## What is Linear Programming Linear programming is an optimization method with broad utility. Sadly the study of linear programming is often overlooked in favor of sexier machine learning algorithms by both practitioners and data science educators (both bootcamps and graduate programs). However, it is used extensively in many of the fields from which data science evolved and there are useful libraries in most of the major analytical languages. We will spend 5-10 minutes understanding linear programming in general before moving on to code ## Linear Programming in Python In Python, the most common library is Pulp which we will use to look at two separate optimization problems, one to minimize and one to maximize a desired outcome variable. We will go through: System requirements Package Installation Computational Complexity (floating values & discrete number calculations) ## Real World Examples with Code We will be setting up and completing the two problems. In doing so we will learn how to simplify a complex system into a series of linear equations. We will learn about: * Decision Variables - the outcomes we choose to optimize * Objective Function - the series of relationships between decision variables which affect the optimization * Constraints - the limits on our decision variables which allocate resources * How to visualize the problem (it could be a graph or simple illustration) * How to set up the equations defining the problem * How to solve the series of equations * How to interpret the results * How to know when you can, should or should not use linear programming to solve a problem The presentation will be assume a base understanding of python algebra and linear equations (y=mx+b) but it is not going to be presented in a mathematically demanding way. We will look at real-world resource allocation problems and use simple python code to solve them. ### Resources There will be a fully fleshed out GITHUB repository with: * The slides * The code * Brief instructional README.md with links to other useful online sources Linear programming is a useful computational technique for finding minima or maxima of a complex system by breaking it into a series of linear equations which describe the systems. It has many practical applications in industry, medicine, agriculture and in retail environments. Together we will explore simple linear programming problems using Python and the module Pulp. === https://pyohio.org A FREE annual conference for anyone interested in Python in and around Ohio, the entire Midwest, maybe even the whole world. Produced by NDV: https://youtube.com/channel/UCQ7dFBzZGlBvtU2hCecsBBg?sub_confirmation=1 Sat Jul 27 14:00:00 2019 at Cartoon 2

Bethany Poulin https://www.pyohio.org/2019/presentations/139 ## What is Linear Programming Linear programming is an optimization method with broad utility. Sadly the study of linear programming is often overlooked in favor of sexier machine learning algorithms by both practitioners and data science educators (both bootcamps and graduate programs). However, it is used extensively in many of the fields from which data science evolved and there are useful libraries in most of the major analytical languages. We will spend 5-10 minutes understanding linear programming in general before moving on to code ## Linear Programming in Python In Python, the most common library is Pulp which we will use to look at two separate optimization problems, one to minimize and one to maximize a desired outcome variable. We will go through: System requirements Package Installation Computational Complexity (floating values & discrete number calculations) ## Real World Examples with Code We will be setting up and completing the two problems. In doing so we will learn how to simplify a complex system into a series of linear equations. We will learn about: * Decision Variables - the outcomes we choose to optimize * Objective Function - the series of relationships between decision variables which affect the optimization * Constraints - the limits on our decision variables which allocate resources * How to visualize the problem (it could be a graph or simple illustration) * How to set up the equations defining the problem * How to solve the series of equations * How to interpret the results * How to know when you can, should or should not use linear programming to solve a problem The presentation will be assume a base understanding of python algebra and linear equations (y=mx+b) but it is not going to be presented in a mathematically demanding way. We will look at real-world resource allocation problems and use simple python code to solve them. ### Resources There will be a fully fleshed out GITHUB repository with: * The slides * The code * Brief instructional README.md with links to other useful online sources Linear programming is a useful computational technique for finding minima or maxima of a complex system by breaking it into a series of linear equations which describe the systems. It has many practical applications in industry, medicine, agriculture and in retail environments. Together we will explore simple linear programming problems using Python and the module Pulp. === https://pyohio.org A FREE annual conference for anyone interested in Python in and around Ohio, the entire Midwest, maybe even the whole world. Produced by NDV: https://youtube.com/channel/UCQ7dFBzZGlBvtU2hCecsBBg?sub_confirmation=1 Sat Jul 27 14:00:00 2019 at Cartoon 2

Bethany Poulin https://www.pyohio.org/2019/presentations/139 ## What is Linear Programming Linear programming is an optimization method with broad utility. Sadly the study of linear programming is often overlooked in favor of sexier machine learning algorithms by both practitioners and data science educators (both bootcamps and graduate programs). However, it is used extensively in many of the fields from which data science evolved and there are useful libraries in most of the major analytical languages. We will spend 5-10 minutes understanding linear programming in general before moving on to code ## Linear Programming in Python In Python, the most common library is Pulp which we will use to look at two separate optimization problems, one to minimize and one to maximize a desired outcome variable. We will go through: System requirements Package Installation Computational Complexity (floating values & discrete number calculations) ## Real World Examples with Code We will be setting up and completing the two problems. In doing so we will learn how to simplify a complex system into a series of linear equations. We will learn about: * Decision Variables - the outcomes we choose to optimize * Objective Function - the series of relationships between decision variables which affect the optimization * Constraints - the limits on our decision variables which allocate resources * How to visualize the problem (it could be a graph or simple illustration) * How to set up the equations defining the problem * How to solve the series of equations * How to interpret the results * How to know when you can, should or should not use linear programming to solve a problem The presentation will be assume a base understanding of python algebra and linear equations (y=mx+b) but it is not going to be presented in a mathematically demanding way. We will look at real-world resource allocation problems and use simple python code to solve them. ### Resources There will be a fully fleshed out GITHUB repository with: * The slides * The code * Brief instructional README.md with links to other useful online sources Linear programming is a useful computational technique for finding minima or maxima of a complex system by breaking it into a series of linear equations which describe the systems. It has many practical applications in industry, medicine, agriculture and in retail environments. Together we will explore simple linear programming problems using Python and the module Pulp. === https://pyohio.org A FREE annual conference for anyone interested in Python in and around Ohio, the entire Midwest, maybe even the whole world. Produced by NDV: https://youtube.com/channel/UCQ7dFBzZGlBvtU2hCecsBBg?sub_confirmation=1 Sat Jul 27 14:00:00 2019 at Cartoon 2

Lara Kattan https://www.pyohio.org/2019/presentations/116 Let's build up our knowledge of probabilistic programming and Bayesian inference! All you need to start is basic knowledge of linear regression; familiarity with running a model of any type in Python is helpful. By the end of this presentation, you'll know the following: - What probabilistic programming is and why it's necessary for Bayesian inference - What Bayesian inference is, how it's different from classical frequentist inference, and why it's becoming so relevant for applied data science in the real world - How to write your own Bayesian models in the Python library PyMC3, including metrics for judging how well the model is performing - How to go about learning more about the topic of Bayesian inference and how to bring it to your current data science job We'll meet our objectives by answering three questions: 1. What is probabilistic programming? * PP is the idea that we can use computer code to build probability distributions * Theory of the primitives in probabilistic programming and how we can build models out of distributions 2. What is Bayesian inference and why should I add it to my toolbox on top of classical ML models? * Classically, we had simulations, but they run in only one direction: get data input and move it according to assumptions of parameters and get a prediction * Bayesian inference adds another direction: use the data to go back and pick one of many possible parameters as the most likely to have created the data (posterior distributions) * Use Bayes' theorem to find the most likely values of the model parameters 3. What is PyMC3 and how can I start building and interpreting models using it? * **We'll work through actual examples of models using PyMC3, including hierarchical models** * Solving Bayes' theorem in practice requires taking integrals * If we don't want to do integrals by hand, we need to use numerical solution methods * From the package authors: "[PyMC3 is an ]open source probabilistic programming framework written in Python that uses Theano to compute gradients via automatic differentiation as well as compile probabilistic programs on-the-fly to C for increased speed" The intention is to get hands-on experience building PyMC3 models to demystify probabilistic programming / Bayesian inference for those more well versed in traditional ML, and, most importantly, to understand how these models can be relevant in our daily work as data scientists in business. If you can write a model in sklearn, you can make the leap to Bayesian inference with PyMC3, a user-friendly intro to probabilistic programming (PP) in Python. PP just means building models where the building blocks are probability distributions! And we can use PP to do Bayesian inference easily. Bayesian inference allows us to solve problems that aren't otherwise tractable with classical methods. === https://pyohio.org A FREE annual conference for anyone interested in Python in and around Ohio, the entire Midwest, maybe even the whole world. Produced by NDV: https://youtube.com/channel/UCQ7dFBzZGlBvtU2hCecsBBg?sub_confirmation=1 Sun Jul 28 13:15:00 2019 at Suzanne Scharer

Lara Kattan https://www.pyohio.org/2019/presentations/116 Let's build up our knowledge of probabilistic programming and Bayesian inference! All you need to start is basic knowledge of linear regression; familiarity with running a model of any type in Python is helpful. By the end of this presentation, you'll know the following: - What probabilistic programming is and why it's necessary for Bayesian inference - What Bayesian inference is, how it's different from classical frequentist inference, and why it's becoming so relevant for applied data science in the real world - How to write your own Bayesian models in the Python library PyMC3, including metrics for judging how well the model is performing - How to go about learning more about the topic of Bayesian inference and how to bring it to your current data science job We'll meet our objectives by answering three questions: 1. What is probabilistic programming? * PP is the idea that we can use computer code to build probability distributions * Theory of the primitives in probabilistic programming and how we can build models out of distributions 2. What is Bayesian inference and why should I add it to my toolbox on top of classical ML models? * Classically, we had simulations, but they run in only one direction: get data input and move it according to assumptions of parameters and get a prediction * Bayesian inference adds another direction: use the data to go back and pick one of many possible parameters as the most likely to have created the data (posterior distributions) * Use Bayes' theorem to find the most likely values of the model parameters 3. What is PyMC3 and how can I start building and interpreting models using it? * **We'll work through actual examples of models using PyMC3, including hierarchical models** * Solving Bayes' theorem in practice requires taking integrals * If we don't want to do integrals by hand, we need to use numerical solution methods * From the package authors: "[PyMC3 is an ]open source probabilistic programming framework written in Python that uses Theano to compute gradients via automatic differentiation as well as compile probabilistic programs on-the-fly to C for increased speed" The intention is to get hands-on experience building PyMC3 models to demystify probabilistic programming / Bayesian inference for those more well versed in traditional ML, and, most importantly, to understand how these models can be relevant in our daily work as data scientists in business. If you can write a model in sklearn, you can make the leap to Bayesian inference with PyMC3, a user-friendly intro to probabilistic programming (PP) in Python. PP just means building models where the building blocks are probability distributions! And we can use PP to do Bayesian inference easily. Bayesian inference allows us to solve problems that aren't otherwise tractable with classical methods. === https://pyohio.org A FREE annual conference for anyone interested in Python in and around Ohio, the entire Midwest, maybe even the whole world. Produced by NDV: https://youtube.com/channel/UCQ7dFBzZGlBvtU2hCecsBBg?sub_confirmation=1 Sun Jul 28 13:15:00 2019 at Suzanne Scharer

random2, 'Andrew Park - Data Science for Beginners_ 4 Books in 1_ Python Programming, Data Analysis, Machine Learning. A Complete Overview to Master The Art of Data Science From Scratch Using Python for Busines'

by Mariano Anaya At: FOSDEM 2019 https://video.fosdem.org/2019/UD2.120/python_demystifying_coroutines.webm In this talk we will explore how coroutines work and how they are implemented in Python. We will learn that generators lie at the heart of coroutines, and how this long-standing feature of Python evolved over time to support the new capabilities of asynchronous programming. It’s a time travelling tale that explores the milestones in Python that relate to generators, coroutines, and asynchronous programming. The talk is intended to explore coroutines and asynchronous programming in Python, in full detail. It provides a historical revision of all the milestones in Python that led to the current implementation of coroutines, asynchronous programming, and event loops (asyncio) as we know them today. Starting from generators, it reviews their original idea and rationale, and then it explores how they became the pythonic solution for supporting coroutines, by extending their interface with new methods (PEP-342). Later on, generators (and coroutines) were extended once again in PEP-380, to support even more advanced functionality (like the "yield from" syntax). This was a major milestone that actually allowed programmers to start working with non-blocking I/O asynchronously fashion, and the idioms used for that resemble modern asynchronous programming with Python. Understanding what's behind those idioms helps to explain much of the current asynchronous frameworks. The dynamic nature of Python made the syntax prone to mixing iterators with coroutines, so the community (wisely) agreed on the fact that the language had to evolve, and new syntax needed to be added. New specific types were added for coroutines in Python 3.5, and Python 3.6 incorporated asynchronous generators. Finally in Python 3.7, "async" and "await" were promoted to keywords, and there were several enhancements to the asyncio library, marking the current state of the art of asynchronous programming in Python. The talk concludes by providing food for thought on what asynchronous programming actually means in Python, and what to expect for the future (Python 3.8 and beyond). Room: UD2.120 (Chavanne) Scheduled start: 2019-02-03 11:00:00+01 Source: https://www.youtube.com/watch?v=7AoANOGIDuM Uploader: FOSDEM

#fastestgrowinglanguages #python #spanish #arabic #french #fastestgrowing #language #languages #programming #google #softwaregore Source: https://www.tiktok.com/@7106641030017270830/video/7227106643975195950 Uploader: newlinersseries

Model 2010 Bioinformatics Programming Using Python Covers Python 3( 3)

GUI Programming with Python

Web allows a wide extent of realities and information source set up by people. However, it will comprise of a gigantic arrangement of divergent and debilitated coordinated information. In metropolitan urban areas of India, there are different institution that measures climate such as The Energy Resource and Institute, Indian Institute of Tropical Meteorology and many more. There are also different sites that also provide accurate weather data of cities in India. For this research, I am using web scraping method to collect current weather report for a specific city in India from two different sources. The fundamental target of Web Scraping is to extract data from one or numerous sites and cycle it into basic structures such as JSON, text, CSV files. This incorporates web crawler and data extractor. Web crawler slithers all the connections present in a site page and stores them in an information base though data extractor extricates information from these put away connections. The information gathered by this web scraping procedure will shape a data set or information distribution centre that can be utilized for additional exploration on information digging for climate determining in metropolitan urban areas. These data stored in the database can be utilize to enhance the application and also meteorologist can study the weather pattern from the data and alert the people of the states from upcoming natural disasters. H. Sumesh Singha | Dr. Bhuvana J "Abrasion of Current Weather of a City Using Variant Python Libraries and Weather Application Programming Interface (API)" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-5 | Issue-1 , December 2020, URL: https://www.ijtsrd.com/papers/ijtsrd38037.pdf Paper URL : https://www.ijtsrd.com/computer-science/other/38037/abrasion-of-current-weather-of-a-city-using-variant-python-libraries-and-weather-application-programming-interface-api/h-sumesh-singha

Reimar Bauer - pymove3D - Python moves the world - Attractive programming for young people [EuroPython 2014] [22 July 2014] The second time a contest for schoolar students is organized by the Python Software Verband e.V.. It is about to write a Python program that is executable in Blender using its 3D capabilities. The talk overall gives an overview what experience we got by these ideas and how we want to continue.

Reimar Bauer - pymove3D - Python moves the world - Attractive programming for young people [EuroPython 2014] [22 July 2014] The second time a contest for schoolar students is organized by the Python Software Verband e.V.. It is about to write a Python program that is executable in Blender using its 3D capabilities. The talk overall gives an overview what experience we got by these ideas and how we want to continue.

Programacion en Python