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Learn how to create a picture display. This lecture will mainly concentrate on the idea of variables. The different types of variables includes integer, float, string and boolean. We will also take a look at the concept of global and local variables in python.

Learn how to create a picture display. This lecture will mainly concentrate on the idea of variables. The different types of variables includes integer, float, string and boolean. We will also take a look at the concept of global and local variables in python.

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.

On the Auspicious Occasion of the 150th Year of Celebrating Gandhi Jayanti. A Small Contribution to  archive.org . Dated: 02.10.2019 This book aims to cover Socket Programming using Python. AJIT SINGH  Patna Women's College, INDIA https://archive.org/details/@ajit-singh  

Programacion en Python

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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.

Learn more about the placement of widgets within the window, using pack, place, grid commands. We also are introduced to the concept of bindings and have a chart which you have reference to the different bindings. We basically finally have this project finished. You can get all the binding from http://ourmedia.org/node/17406

Catherine Moroney https://2017.northbaypython.org/schedule/presentation/24/ Python is an incredibly powerful and versatile language, but it is not practical for lots of deeply nested loops because it's not compiled, and a lot of the big scientific libraries only have C interfaces. But, if you bring Fortran (via f2py) and C (Cython) into the mix then you can use the best features of all three languages and shrink your development/maintenance time and resulting code size by almost an order of magnitude. My satellite has a lot of legacy code in a Perl/C/Fortran mixture that is incredibly difficult to read and maintain, but our new software is in a Python/C/Fortran mixture (with Python being the bulk of the code) that is a fraction of the size of the old code, much much easier to develop and maintain, yet it has the speed and stability to be used in our production system. I use Python for the upper and middle layers, Fortran for the few places where I need to do lots of looping that cannot be transformed into whole-array operations, and then C to interface with the various 3rd party libraries. I can wrap Fortran and C using f2py and Cython respectively, call them directly from the Python, and pass numpy arrays back and forth seamlessly. I also have layers of scripts: a Python code to run a single orbit's worth of data through a single stage of the processing pipeline, a second Python script to run multiple orbits through a single stage, and then a third script that runs the entire processing chain through many orbits, thereby using Python's numerous scripting capabilities. By using all three languages as described above, I truly get the best of all worlds: dramatically reduced development time, a code base that is a fraction of the size of the old legacy system, yet all the speed and stability I need to process huge amounts of data. About North Bay Python A single-track conference north of the Golden Gate, focused on community, collaboration, and all things Python. North Bay Python is a two-day, single-track Python conference held at the Mystic Theatre in Historic Downtown Petaluma, California, over the weekend of December 2 & 3, 2017. We're a nonprofit conference for professionals, enthusiasts and students alike. We're focused on inclusion, accessibility, diversity, and affordability. Most importantly, we're planning a great lineup of talks from all over the Python ecosystem, with plenty of time to meet new people and develop new ideas. Our venue, the Mystic Theatre in Downtown Petaluma, is a beautiful example of an early 1900s Vaudeville theatre. You can find over 50 different food and drink options a short walk away, and the nearest hotel is only a block away. A Python conference north of the Golden Gate North Bay Python is a single-track conference with a carefully curated set of talks representing the diverse Python community and their different areas of interest. If a topic is less to your interest, or you've met some people you really want to sit down and chat with, we'll have plenty of areas away from the main theatre to catch up and chat. Our goal is to keep prices as low as possible. That means we won't be catering lunch. Instead, you can look forward to extra-long lunch breaks you can use to explore all of the great food options around the venue.

Nicolas Lara - Python and Async programming [EuroPython 2016] [18 July 2016 / 2016-07-18] [Bilbao, Euskadi, Spain] (https://ep2016.europython.eu//conference/talks/python-and-async-programming) How does the experienced python programmer fair when faced with python's "new" way of doing async programming for the first time? This talk details the different ways python provides for attacking the problem of asynchronous programming and focuses on the best practices for the future (as of python 3.4 and 3.5) ----- How does the experienced python programmer fair when faced with python's "new" way of doing async programing in for the first time? Do we all know how and when to use Futures, yield from, asyncio, coroutines, the async and await keywords, eventloops, and others? A lot has changed in recent versions of Python when it comes to async programming, concurrency, and parallelism. We still have very different ways of approaching the problem in each version, but they are finally (as of python 3.4/3.5) converging to a standard. This talk explores, from the perspective of an experienced python programmer with little to no experience in async programming, what the "one obvious way" to do async programming in Python is supposed to be. It does so but analysing examples of different categories of async problems we may want to solve and what the correct way to solve them with the latest versions of Python would be (along with the trade offs of different approaches). The examples include generic CPU-bound problems, IO-bound problems, and "both-bound" problems; along with common tasks as building a simple server, scraping, deferring a web response, and traversing graphs. When useful, I compare the solutions with the approach we would take in languages that have been design for- and are known to be good at async programming like Javascript and Go.

Python

Python

Python programming language by Tutorials Point.

Python programming language by Tutorials Point.

Python An Introduction To Programming ( Chapter 9 Multimedia)

Python Programming

a text book for learning python.

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.

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

Programming Python 4th Edition

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

Juan Nunez-Iglesias https://linux.conf.au/schedule/30358/view_talk In my brief experience people rarely take this [streaming] route. They use single-threaded in-memory Python until it breaks, and then seek out Big Data Infrastructure like Hadoop/Spark at relatively high productivity overhead. ~ Matt Rocklin That quote succinctly summarises my computational life, right up until recent months. In "traditional" programming, you load a dataset into memory, process it in some way, and output the result. This is simple to understand. But in streaming programs, a function processes some of the data, yields the processed chunk, then downstream functions deal with that chunk, then the original function receives a bit more, and so on... All these things are going on at the same time! How can one keep them straight? This talk will introduce Matt Rocklin's Toolz library which makes functional programming easy in Python and provides a framework to write elegant, concise code to analyse bigger-than-memory data, and fn.py, which has even more FP constructs. I'll present streaming data analysis using FP from the ground up, from a simple "hello-world" example to image illumination correction and streaming extensions to scikit-learn classifiers, and analysing a genome in a few minutes.

100+ Python challenging programming exercises Python-programming-exercises 100+ Python challenging programming exercises. Python Comics Hey guys I just created a comic for learning Python if you like you could see it from here: https://aicodeplayer.comFor now I just use Chinese if you like I could use English as well. To restore the repository download the bundle wget https://archive.org/download/github.com-zhiwehu-Python-programming-exercises_-_2020-10-16_12-24-34/zhiwehu-Python-programming-exercises_-_2020-10-16_12-24-34.bundle and run: git clone zhiwehu-Python-programming-exercises_-_2020-10-16_12-24-34.bundle Source: https://github.com/zhiwehu/Python-programming-exercises Uploader: zhiwehu Upload date: 2020-10-16

This video will teach you everything you need as a beginner to write Python programs using PyCharm on any Linux distro and Windows 11. 00:00 Intro 00:18 How to open PyCharm on Linux 00:37 How to open PyCharm on Windows 11 00:14 How to exit project in PyCharm 01:20 How to open projects in PyCharm 01:35 How to create new projects in PyCharm 03:10 How to maximize the PyCharm window 03:19 How to change the PyCharm theme 03:46 Introduction to PyCharm project tool window 03:54 How to hide items in the folder in PyCharm project tool window 04:00 How to view items in the folder in PyCharm project tool window 04:05 How to create a new folder using the PyCharm project tool window 04:37 How to create a new Python script using the PyCharm project tool window 05:24 How to close Python Scripts in the PyCharm editor panel 05:31 How to open Python Scripts in the PyCharm editor panel using PyCharn project tool window 05:39 PyCharm project conclusion 05:51 How to minimize the “project tool window” 06:00 How to make the “Project tool window” show up 06:14 Introduction to PyCharm editor panel 06:20 How to edit Python script using PyCharm editor panel 07:00 How to use the "cd" command to move around the file path in PyCharm terminal 07:49 How to use the "ls" command to see a list of all the folders and files contained inside your current folder in the PyCharm terminal 08:00 Example of using the "cd" command in the PyCharm terminal 08:34 How to create folders using the PyCharm terminal using the "mkdir" command 09:31 How to create Python files using the PyCharm terminal using the "touch" command 10:31 How to run Python script using the PyCharm terminal 12:07 How to hide and unhide the PyCharn terminal 12:27 PyCharm terminal conclusion 12:31 How you can run a Python script in PyCharm without using the terminal 12:59 How to close the PyCharm app 13:11 What to do if you need help or want to improve using PyCharm. 13:41 Outro

Intro to the string library, with find and strip commands. Learn how to find out all the tools inside a library yourself.

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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.

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.

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

Python. An Introduction To Programming ( Chapter 7 Graphics)

Python. An Introduction To Programming ( Chapter 7 Graphics)

xxxiv, 372 p. : 24 cm. +

you'll learn how to get the text from the entry and put it into a list, learn how to add padding between widgets

The Complete Python Bootcamp From Zero to Hero in Python (8 - Object Oriented Programming)

The Complete Python Bootcamp From Zero to Hero in Python (8 - Object Oriented Programming)

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'

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

you will learn the Tkinter library, how to draw buttons, windows, textfields, etc, a good starting point to learn GUI programming. Watching previous lectures are recommended to understand 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.

Moshe Goldstein/david dayan - Implementing Parallel Programming Design Patterns using EFL for Python [EuroPython 2016] [19 July 2016 / 2016-07-19] [Bilbao, Euskadi, Spain] (https://ep2016.europython.eu//conference/talks/implementing-parallel-programming-design-patterns-using-efl-for-python) EFL (Embedded Flexible Language), a deterministic parallel programming tool, may be embedded in any host language. Two versions of the EFL pre-compiler for Python were implemented. One translates EFL blocks into Python's Multiprocessing code, and the other one into DTM/MPI4PY code. EFL implementations of Parallel Programming Design Patterns will be shown, generated parallel code compared, and differences discussed. Visit flexcomp.jct.ac.il for further information. ----- Multi-core CPUs are abundant and utilizing them effectively requires programmers to parallelize CPU-intensive code. To facilitate this, we have developed EFL (Embedded Flexible Language), a deterministic parallel programming tool. The parallel parts of a program are written as EFL-blocks, which are embedded into a sequential host language program. The sequential parts of the program are written in the host language, outside the EFL blocks. EFL may be embedded in any host language by writing an appropriate EFL pre-compiler. At the moment, we implemented two versions of the EFL pre-compiler. Both pre-compilers translate EFL blocks into parallel Python code - one of them generates parallel code based on Python's Multiprocessing module, and the other one generates parallel code based on the DTM/MPI4PY Python module. We will present the principles upon which EFL is built. We will show the implementation of Parallel Programming Design Patterns using EFL's parallel programming constructs (such as parallel assignments, parallel for-loops, etc.). Using our two EFL pre-compilers we will show their translation to Python parallel code according to the Multiprocessing module as well as the DTM/MPI4PY module. The differences between code versions produced by the EFL pre-compilers will be discussed. For further information about the EFL project and our Flexible Computation Research Laboratory, visit http://flexcomp.jct.ac.il

Hi, & Welcome to this new how to series of student video tutorial. My name is Bijay, and i’ll be teaching you complete series of Python Programming for Hackers. This is first part, Part 1 – Introduction to course outline & setup process.

Advanced Perspective of Classes and Object-Oriented Programming in Python

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.

A simulation is needed to observe and indicate how much preventive measures influence the pandemic flow, controlling and stopping it. This study succeeded in making a stochastic susceptible infected recovered deceased (SIRD) simulation using Python programming language to determine the effectiveness of prevention methods such as masks policy, social distancing, vaccination, quarantine, and lockdown. Every preventive measure is modeled based on an equivalent actual event and every essential aspect that affects the course of the pandemic. A person is represented as a circle moving freely in two-dimensional space, and disease spreads through person-to-person contact. This simulator then tested using parameters to simulate COVID-19 and found significant results between communities that implement preventive measures and those that do not. We found that within 106 days, 284 people were infected, but when five preventive methods are applied for a total of 33 days, only 31 people were infected. Adequate to simulate epidemic events and their prevention measures, this simulator can also be used as a learning tool with factors in epidemic events such as population density, mobility, infection rate, disease mortality, and every effect of each preventive measure. Users can change and influence the simulation course using interactive and straightforward software tools.

This is a continuation from the previous lecture. In this lecture we learn how built an image viewer that displays our cool pictures. In this lecture we learn ::: Learn how to display gif pictures with a canvas widget. How to create a photo image object. How do you delete an image from the canvas.

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