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This #goodolddays box is #true - except things are MUCH BETTER NOW! But if you want to #learn #programming - well good luck. #Python #basic -erUr5W5beD1

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'

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

This open access book offers an initial introduction to programming for scientific and computational applications using the Python programming language. The presentation style is compact and example-based, making it suitable for students and researchers with little or no prior experience in programming. The book uses relevant examples from mathematics and the natural sciences to present programming as a practical toolbox that can quickly enable readers to write their own programs for data processing and mathematical modeling. These tools include file reading, plotting, simple text analysis, and using NumPy for numerical computations, which are fundamental building blocks of all programs in data science and computational science. At the same time, readers are introduced to the fundamental concepts of programming, including variables, functions, loops, classes, and object-oriented programming. Accordingly, the book provides a sound basis for further computer science and programming studies.

A computer program has been developed using PYTHON for the analysis of multi turn CLPHP made up of copper with capillary dimensions such as 2mm and 3.1 mm inner and outer diameters. The central difference finite difference equations are used to determine the temperatures at various sections by using numerical methods. The heat transfer equations for each cell solved iteratively, and the temperature values are updated at each time step until the solution converged. The performance of the heat pipe was analysed by varying the heat inputs and working fluid conditions. PYTHON code is developed for different fluids by using libraries such as NumPy, SciPy, and Matplotlib to obtain temperature matrix. Thermal resistance as a performance parameter is calculated with the help of temperatures. The obtained temperatures by running Python code are compared with the existing experimental results.

Model 2010 Bioinformatics Programming Using Python Covers Python 3( 4)

Python Programming Collection PDF Ebooks All You Need

Python Programming Collection PDF Ebooks All You Need

Python Programming Collection PDF Ebooks All You Need

GUI Tutorial 2-Python Tkinter -Quadratic Equation - Computer Programming Tutor

[ Project Brief] Python Programming Basics

[ Project Brief] Python Programming Basics

Python 3 Programming Specialization

Python 3 Programming Specialization

Python is a powerful, flexible programming language broadly used for web improvement, facts technology, and automation.

Python. An Introduction To Programming

@ https://programming-23.mooc.fi/ - Python Slideshow Pdf

@ https://programming-23.mooc.fi/ - Python Slideshow Pdf

@ https://programming-23.mooc.fi/ - Python Slideshow Pdf

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.

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.

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.

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.

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.

The Python DB-API 2.0 provides a direct interface to many popular database backends. It makes interaction with relational database very straight forward and allows tapping into the full set of features these databases provide. The talk will cover advanced database topics which are relevant in production environments such as locks, distributed transactions and transaction isolation. ---- The talk will give an in-depth discussion of advanced database programming topics based on the Python DB-API 2.0: locks and dead-locks, two-phase commits, transaction isolation, result set scrolling, schema introspection and handling multiple result sets. Talks slides are available on request.

The Python DB-API 2.0 provides a direct interface to many popular database backends. It makes interaction with relational database very straight forward and allows tapping into the full set of features these databases provide. The talk will cover advanced database topics which are relevant in production environments such as locks, distributed transactions and transaction isolation. ---- The talk will give an in-depth discussion of advanced database programming topics based on the Python DB-API 2.0: locks and dead-locks, two-phase commits, transaction isolation, result set scrolling, schema introspection and handling multiple result sets. Talks slides are available on request.

The Python DB-API 2.0 provides a direct interface to many popular database backends. It makes interaction with relational database very straight forward and allows tapping into the full set of features these databases provide. The talk will cover advanced database topics which are relevant in production environments such as locks, distributed transactions and transaction isolation. ---- The talk will give an in-depth discussion of advanced database programming topics based on the Python DB-API 2.0: locks and dead-locks, two-phase commits, transaction isolation, result set scrolling, schema introspection and handling multiple result sets. Talks slides are available on request.

[ Reading] After Work Introduction To Python Programming

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

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

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

Certified Associate in Python Programming (PCAP)

GUI Tutorial 0-Python Tkinter -Tutorial for Beginners-Computer Programming Tutor

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

Gregory M. Kapfhammer, Madelyn M. Kapfhammer https://www.pyohio.org/2019/presentations/120 This presentation will explore the different features of text editors for Python programming. By comparing the capabilities of VS Code and Vim, audiences of all skill levels will receive the necessary information to make an informed decision about which text editor fits their programming preferences. Using the illustrative example of a Python programmer who is implementing a Python program, the talk will introduce and compare features including fuzzy file finding and code navigation, auto-completion, source code highlighting, linting, testing, virtual environments, and snippets. For VS Code and Vim, these selected features showcase what is often important to a Python programmer, highlighting the trade-offs and benefits of both text editors. Here are some topics that we will cover in this presentation: - **Fuzzy File Finding**: Rapidly search for files in your project with names that match a pattern. - **Source Code Highlighting**: Bring clarity by applying colors and fonts to your source code and technical writing. - **Autocompletion**: Save time by quickly substituting partial code and text segments with the desired content. - **Linting and Code Formatting**: Check and reformat source code and writing to ensure adherence to well-established style guides. - **Virtual Environments and Packages**: Maintain project isolation by installing and managing packages in separate development and execution environments. - **Automated Testing and Debugging**: Establish a confidence in program correctness by running test suites and finding and fixing bugs. - **Code Snippets**: Save time when programming and testing by inserting full segments based on easy-to-complete keywords. Ultimately, this presentation will demonstrate that both VS Code and Vim are outstanding text editors for Python, with features that can assist in many everyday programming tasks. In different ways, and possibly with different disadvantages or benefits, these text editors improve a programmer's efficiency and effectiveness, becoming an indispensable part of an everyday workflow. With the knowledge of the features that VS Code and Vim offer, the audience will be able to choose which editor is best for them, emerging with the know-how to configure it to their preferences for Python programming. Both beginners and experts alike will be capable of finding their "feature fit" for a text editor that supports Python programming! What is important to you when it comes to text editors? To find out, join us in comparing VS Code and Vim. From version control integration to source code highlighting, with auto-completion, testing, virtual environments, snippets, code navigation and linting in between, learn how VS Code and Vim handle each feature and decide for yourself what fits your programming preferences when using Python. === 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 15:45:00 2019 at Hays Cape

Gregory M. Kapfhammer, Madelyn M. Kapfhammer https://www.pyohio.org/2019/presentations/120 This presentation will explore the different features of text editors for Python programming. By comparing the capabilities of VS Code and Vim, audiences of all skill levels will receive the necessary information to make an informed decision about which text editor fits their programming preferences. Using the illustrative example of a Python programmer who is implementing a Python program, the talk will introduce and compare features including fuzzy file finding and code navigation, auto-completion, source code highlighting, linting, testing, virtual environments, and snippets. For VS Code and Vim, these selected features showcase what is often important to a Python programmer, highlighting the trade-offs and benefits of both text editors. Here are some topics that we will cover in this presentation: - **Fuzzy File Finding**: Rapidly search for files in your project with names that match a pattern. - **Source Code Highlighting**: Bring clarity by applying colors and fonts to your source code and technical writing. - **Autocompletion**: Save time by quickly substituting partial code and text segments with the desired content. - **Linting and Code Formatting**: Check and reformat source code and writing to ensure adherence to well-established style guides. - **Virtual Environments and Packages**: Maintain project isolation by installing and managing packages in separate development and execution environments. - **Automated Testing and Debugging**: Establish a confidence in program correctness by running test suites and finding and fixing bugs. - **Code Snippets**: Save time when programming and testing by inserting full segments based on easy-to-complete keywords. Ultimately, this presentation will demonstrate that both VS Code and Vim are outstanding text editors for Python, with features that can assist in many everyday programming tasks. In different ways, and possibly with different disadvantages or benefits, these text editors improve a programmer's efficiency and effectiveness, becoming an indispensable part of an everyday workflow. With the knowledge of the features that VS Code and Vim offer, the audience will be able to choose which editor is best for them, emerging with the know-how to configure it to their preferences for Python programming. Both beginners and experts alike will be capable of finding their "feature fit" for a text editor that supports Python programming! What is important to you when it comes to text editors? To find out, join us in comparing VS Code and Vim. From version control integration to source code highlighting, with auto-completion, testing, virtual environments, snippets, code navigation and linting in between, learn how VS Code and Vim handle each feature and decide for yourself what fits your programming preferences when using Python. === 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 15:45:00 2019 at Hays Cape

Gregory M. Kapfhammer, Madelyn M. Kapfhammer https://www.pyohio.org/2019/presentations/120 This presentation will explore the different features of text editors for Python programming. By comparing the capabilities of VS Code and Vim, audiences of all skill levels will receive the necessary information to make an informed decision about which text editor fits their programming preferences. Using the illustrative example of a Python programmer who is implementing a Python program, the talk will introduce and compare features including fuzzy file finding and code navigation, auto-completion, source code highlighting, linting, testing, virtual environments, and snippets. For VS Code and Vim, these selected features showcase what is often important to a Python programmer, highlighting the trade-offs and benefits of both text editors. Here are some topics that we will cover in this presentation: - **Fuzzy File Finding**: Rapidly search for files in your project with names that match a pattern. - **Source Code Highlighting**: Bring clarity by applying colors and fonts to your source code and technical writing. - **Autocompletion**: Save time by quickly substituting partial code and text segments with the desired content. - **Linting and Code Formatting**: Check and reformat source code and writing to ensure adherence to well-established style guides. - **Virtual Environments and Packages**: Maintain project isolation by installing and managing packages in separate development and execution environments. - **Automated Testing and Debugging**: Establish a confidence in program correctness by running test suites and finding and fixing bugs. - **Code Snippets**: Save time when programming and testing by inserting full segments based on easy-to-complete keywords. Ultimately, this presentation will demonstrate that both VS Code and Vim are outstanding text editors for Python, with features that can assist in many everyday programming tasks. In different ways, and possibly with different disadvantages or benefits, these text editors improve a programmer's efficiency and effectiveness, becoming an indispensable part of an everyday workflow. With the knowledge of the features that VS Code and Vim offer, the audience will be able to choose which editor is best for them, emerging with the know-how to configure it to their preferences for Python programming. Both beginners and experts alike will be capable of finding their "feature fit" for a text editor that supports Python programming! What is important to you when it comes to text editors? To find out, join us in comparing VS Code and Vim. From version control integration to source code highlighting, with auto-completion, testing, virtual environments, snippets, code navigation and linting in between, learn how VS Code and Vim handle each feature and decide for yourself what fits your programming preferences when using Python. === 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 15:45:00 2019 at Hays Cape

xii, 270 p. ; 24 cm

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.

A new concept based on learning programming using the Python API of Blender makes it very easy to get visible objects created. [Learning](http://pymove3d.pysv.org/python_course/stations/blender-basics/b_find_information/ba_search_api.html) of object oriented programming is much easier that way. Objects you have created are visualized. By methods you can interact with them and you get in time results shown. Backing to the contest we provide [course material](http://pymove3d.pysv.org/coursematerial). The talk overall gives an overview what experience we got by these ideas and how we want to continue.

A new concept based on learning programming using the Python API of Blender makes it very easy to get visible objects created. [Learning](http://pymove3d.pysv.org/python_course/stations/blender-basics/b_find_information/ba_search_api.html) of object oriented programming is much easier that way. Objects you have created are visualized. By methods you can interact with them and you get in time results shown. Backing to the contest we provide [course material](http://pymove3d.pysv.org/coursematerial). The talk overall gives an overview what experience we got by these ideas and how we want to continue.

Perma.cc archive of https://en.wikipedia.org/wiki/Python_(programming_language) created on 2022-07-01 10:28:04.890697+00:00.

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'

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.

Learn Python 3 programming  Become job ready using Pycharm

Learn Python 3 programming  Become job ready using Pycharm

Python. An Introduction To Programming ( Chapter 10 Basic Algorithms)

Python. An Introduction To Programming ( Chapter 9 Multimedia)