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Irikidzai Muchaneta https://2016.za.pycon.org/talks/6/ I will be talking about creating a computer vision classroom color poll sheet recognition project using Python, OpenCV and raspberry Pi. Specifically, looking at detecting color poll-sheets in a classroom of about 50 - 150 students. Much like how one can poll an audience of people using a clicker technology and show the results of the audience with regards to a question asked. Audience is anyone interested in computer vision, what computer vision can do and achieve, as well as the hard work required to make it all happen. After the talk the audience can have a glimpse into what it takes to create a computer vision project, as well as the typical process and algorithm one would follow in a simple task of creating a computer vision project using python, opencv and raspberry pi. A few aspects I will cover include: - Computer vision goals - Simplified overview of the algorithm used - Going into a bit of detail where the challenges arise - Example of the the project i worked on - Results achieved - A few global interesting projects around computer vision I will end with a look into the python code involved and talk through the results of the polling program, as well as any hard to detect areas and how the system detects these. :)

Irikidzai Muchaneta https://2016.za.pycon.org/talks/6/ I will be talking about creating a computer vision classroom color poll sheet recognition project using Python, OpenCV and raspberry Pi. Specifically, looking at detecting color poll-sheets in a classroom of about 50 - 150 students. Much like how one can poll an audience of people using a clicker technology and show the results of the audience with regards to a question asked. Audience is anyone interested in computer vision, what computer vision can do and achieve, as well as the hard work required to make it all happen. After the talk the audience can have a glimpse into what it takes to create a computer vision project, as well as the typical process and algorithm one would follow in a simple task of creating a computer vision project using python, opencv and raspberry pi. A few aspects I will cover include: - Computer vision goals - Simplified overview of the algorithm used - Going into a bit of detail where the challenges arise - Example of the the project i worked on - Results achieved - A few global interesting projects around computer vision I will end with a look into the python code involved and talk through the results of the polling program, as well as any hard to detect areas and how the system detects these. :)

The Complete Python Bootcamp From Zero to Hero in Python (19 - Final Capstone Python Project)

[ Project Brief] After Work Data Science Data Analysis With Python

After Work Data Science Ensemble Learning With Python Project

[ Project Brief] Data Pipelines With Python Project

[ Project Brief] After Work Data Science Data Analysis With Python

Udemy python course

After Work Data Science Clustering Analysis With Python Project ( 1)

Did you write a cool and useful Python script? Would you like to share it with the community, but you're not sure how to go about that? If so, then this talks is for you. We'll go over a list of simple steps which can turn your script into a fully fledged open-source project. The Python community has a rich set of tools which can help verify the quality of your code through automated code-review and linting. You can benefit by taking advantage of this ecosystem. Complete the steps in this checklist, and your project will be easier to maintain, you'll be ready to take contributions from the community and those contributions will be up to high standards. Your project will also keep up with other projects on PyPI and you will be alerted if any new release causes an incompatibility with your code. The same checklist can be used for non open-source projects as well. The project maturity checklist includes: Properly structure your code Use a setup.py file Add entry_points for your script command Create a requirements.txt file Use Black to format your code Create a tox.ini config and include code linters Set up a Git repo Refactor your code to be unit-testable and add tests Add missing docstrings Add type annotations and a MyPy verification step Upload to GitHub Add a continuous integration service (e.g. Travis) Add a requirements updater (e.g. pyup.bot) Add test coverage checker (e.g. coveralls) Add a Readme file and documentation Publish your project on PyPI Advertise your project Please see our speaker release agreement for details: https://ep2019.europython.eu/events/speaker-release-agreement/

[ Project Brief] After Work Data Science Recurrent Neural Networks With Python

[Project Brief] Innovating With GPT Models And Python

Django & Python Complete BUNDLE + Django Real World Project

Source code repositories for project python-exercises hosted on kenai.com. Original fetch URLs: https://svn.kenai.com/svn/python-exercises~subversion SVN exported with 'svnrdump' and compressed with lzip, git exported with 'git bundle', hg exported with 'hg bundle'.

The Complete Python Bootcamp From Zero to Hero in Python (7 - Milestone Project 1)

[ Project Brief] Python Basics For Data Science Dojo Project

After Work Data Science Hyperparameter Tuning With Python Project

After Work Data Science Hyperparameter Tuning With Python Project

Thursday Bram https://2018.northbaypython.org/schedule/presentation/15/ This talk covers key issues Python programmers run into when naming new projects. We'll go over the following: * Commonly used naming schemas in the Python community * Current and past project names (including those that many newcomers to Python struggle with) * Techniques to avoid similar confusion in the future (covering both name selection and documentation) We'll even talk about Monty Python and its long-term impact on the Python programming language. 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.

After Work Data Science Classification Analysis With Python Project

A Materials Project based open-source Python tool, MPInterfaces, has been developed to automate the high-throughput computational screening and study of interfacial systems. The framework encompasses creation and manipulation of interface structures for solid/solid hetero-structures, solid/implicit solvents systems, nanoparticle/ligands systems; and the creation of simple system-agnostic workflows for in depth computational analysis using density-functional theory or empirical energy models. The package leverages existing open-source high-throughput tools and extends their capabilities towards the understanding of interfacial systems. We describe the various algorithms and methods implemented in the package. Using several test cases, we demonstrate how the package enables high-throughput computational screening of advanced materials, directly contributing to the Materials Genome Initiative (MGI), which aims to accelerate the discovery, development, and deployment of new materials.

Developers create new open-source projects every day. As the project becomes popular they have to invest more and more time into it's development and of course at some point a question arises: ""How can I make some money with my project ?"" In this talk we will try to answer this question. We will talk about different models of making money, their pros and cons. We will concentrate on Python Open-Source projects mostly and try to answer the following questions: What to sell? Where to sell? How to distribute? How to license? After this talk you will have a clear understanding of how you can make money with your project. What your next steps should be and how you can get the actual profit while still continuing making your customers happy. Please see our speaker release agreement for details: https://ep2017.europython.eu/en/speaker-release-agreement/

Goals: Describe the process I went though to return to and finish the code I wrote for my PhD modelling the geographic growth and decline of a Bulletin Board Systems (BBS) community called FidoNet. I had to leave the project for 2 major surgeries and finally returned to finish, validate the results and present a paper at the Sunbelt conference in Paris. Please see our speaker release agreement for details: https://ep2020.europython.eu/events/speaker-release-agreement/

Glyph https://pretalx.northbaypython.org/nbpy-2024/talk/RYQVSH Do you want to create a Python project? Do you want it to be good? There have been lots of talks and posts and books about how to improve your project by adopting this or that tool or technology, by adopting a best practice. We all want to improve. We all want our code to be more correct. Faster. Friendlier. Easier to use. Better documented. Better tested. To have better coverage. Easier to contribute to. Easier to work on. More welcoming as a community. More repeatable. Less flaky. More secure. More sustainable. Easier to install. Easier to deploy. Easier to manage. Easier to discover. Easier to discuss, to report issues, to investigate issues, to triage issues. What if we did all the things though? How could we make our project perfect? In this talk, I will explore a comprehensive review of all of the best practices available to Python projects; a few specific to open source, but most applicable to just about anything written in Python. In addition to learning about many, many best practices, their importance, and the tools available to facilitate them, I will also propose some solutions to the overwhelming sense of existential dread that one might feel when confronted with all of them at once. Produced by NDV: https://youtube.com/channel/UCQ7dFBzZGlBvtU2hCecsBBg?sub_confirmation=1 Sat Jun 29 10:25:00 2024 at Reis River Ranch

Allan Swanepoel https://2017.za.pycon.org/talks/77/ By: The authors of Tachyonic Intro: (10 mins) Meet Bob. Bob is a Network Administrator at a Service Provider. As a well seasoned network engineer, he's been building packet pushing networks since the days when IP shared bandwidth with IPX, Appletalk, DECnet etc. He is a well rounded network engineer with robust set of networking skills. He mastered making, shaping of networks with his tool set such as Spanning Tree, RAPS, Vlans, VXLAN and MPLS. However, Bob's world is changing. Networks are not what they used be. Today's networks are growing more rapidly than ever. This means there are more devices to manage and also more data produced by these devices to work with. Doing things manually just don't scale anmore. Bob has no other option to turn to automation. In the Devops world these issues have been and are being addressed. The Networking universe is lagging behind. Standard Bodies and Vendors have started coming up with solutions such as Openflow and NETCONF, but there are limits. Also, many Open source projects (most of them written in Python) and proprietary solutions have joined the scene, which we will talk about. So Bob has options, but… none of them has all the answers. Except for one! Introduction to Tachyonic Open Source Project (10 mins) Why Python? ** (you probably know some of the answer already) What is Tachyonic? ** Tachyonic framework is intended to control a large pool of services such as cloud, networking, dns, email managed through a single dashboard. The goal here is to have a "single pane of glass” view. Tachyonic scale is only dependent on the deployment method used and is designed to be massively horizontally scalable. It's all open-source and completely customizable. Modular - extensible All interfaces such as API endpoints and Dashboard views are fully extensible via plugins/modules. All you need to do is write the bit of python to meet your need, and plug it into the Tachyonic framework. Components and endpoints can run on the same Server, or different ones. API and UI ** Tachyonic comes with both a RESTful API, as well as a UI for those who wish to use it. In fact the UI simply uses the API in the back-end Multi-Tenancy ** With Tachyonic you get a framework that us Multi-Tenant, and Multi-Tiered. That means you can use it to add customers, and Customers can even be resellers that have their own customers. Tachyonic takes care of who is allowed access to what. What you get (10 mins) We have done all the hard work for you: User login and RBAC Separate Domains UI API * WSGI Framework What you need to code You may simply write the python you require, and plug it in! Single extendable portal, restapi framework does it all! Use cases Build customer portals to provision services in addition to integrated helpdesk and payment gateways. Build Billing and Telemetry services on top of Python using well-known open-source projects such as Pandas for time series data. Automate deployment and monitoring of network devices With knowledge of Python and Tachyonic, you may never have to look at a new system ever again! Where can i get more info? Visit our site: http://tachyonic.co.za/

Sewagodimo Matlapeng https://2018.za.pycon.org/talks/41-from-zero-to-kind-of-a-hero-getting-your-python-side-project-ready-for-deployment/ One evening, my little sister asked me for help with her homework at 9pm. She had already asked her classmates on a WhatsApp group, but nobody was able to help as all her peers had the same limited resources available to them. This gave me the idea for Buza (Zulu for "ask”), a platform for high school learners to ask questions that could be answered by volunteer university students in their free time. Buza is the first side project of mine that I've ever truly wanted to deploy. After months of coding and adding loads of features, I finally reached out to a senior developer to help me deploy my truck of features. The first thing she said was "A lot of this code will have to be replaced with production-ready code”. These are words you never want to hear. University Computer Science equipped me with the ability to write code to solve problems, but in industry extra skills were required to build production-ready software. This talk will share the valuable lessons I learned from getting this Django web app production-ready. This will start with how to c

GRR Rapid Response (https://github.com/google/grr) is an incident response framework focused on remote live forensics. It consists of a Python client (agent) that is installed on target systems, and Python server infrastructure that can manage and talk to clients. The goal of GRR is to support forensics and investigations in a fast, scalable manner to allow analysts to quickly triage attacks and perform analysis remotely. GRR was started at Google in 2009 as a simple Python helper script used by Incident Response engineers. Eventually a little Python script got a little server component, was adapted to run on multiple systems (Mac, Linux, Windows), then a little UI was added and a few nice features were introduced (large-scale hunts, collection of predefined artifacts, memory analysis). A helper script has eventually evolved into a sophisticated framework with 180,000 lines of Python code. In the presentation we’ll talk about the process of evolving a small prototype-like Python project into a production-ready system, using GRR as an example. The topics that we’ll cover are: * Taking shortcuts - both in terms of design and implementation. Reasons for taking them and their eventual costs. * Relying on Python’s power features (i.e. meta-classes, mixins)? Long-term consequences on maintainability and readability. * Organising the project into separate PyPI packages - benefits of doing that. * Continuous integration, testing and automated builds for various platforms - implementation costs and maintainability effects. Please see our speaker release agreement for details: https://ep2019.europython.eu/events/speaker-release-agreement/

GRR Rapid Response (https://github.com/google/grr) is an incident response framework focused on remote live forensics. It consists of a Python client (agent) that is installed on target systems, and Python server infrastructure that can manage and talk to clients. The goal of GRR is to support forensics and investigations in a fast, scalable manner to allow analysts to quickly triage attacks and perform analysis remotely. GRR was started at Google in 2009 as a simple Python helper script used by Incident Response engineers. Eventually a little Python script got a little server component, was adapted to run on multiple systems (Mac, Linux, Windows), then a little UI was added and a few nice features were introduced (large-scale hunts, collection of predefined artifacts, memory analysis). A helper script has eventually evolved into a sophisticated framework with 180,000 lines of Python code. In the presentation we’ll talk about the process of evolving a small prototype-like Python project into a production-ready system, using GRR as an example. The topics that we’ll cover are: * Taking shortcuts - both in terms of design and implementation. Reasons for taking them and their eventual costs. * Relying on Python’s power features (i.e. meta-classes, mixins)? Long-term consequences on maintainability and readability. * Organising the project into separate PyPI packages - benefits of doing that. * Continuous integration, testing and automated builds for various platforms - implementation costs and maintainability effects. Please see our speaker release agreement for details: https://ep2019.europython.eu/events/speaker-release-agreement/

[ Project Brief] After Work Data Science Text Classification With Python

[ Project Brief] Data Pipelines With Python And Mongo DB

Python And Hacking Made Simple Full Beginners Bundle To Master Python Hacking By Project Syntax (z Lib.org)

After Work Data Science Hyperparameter Tuning With Python Project

Felix Wick/Florian Wilhelm - How to Setup a new Python Project [EuroPython 2014] [23 July 2014] Setting up a new Python project from scratch can be quite hard. How to structure your files and directories. Where should my packages, modules, documentation and unit tests go? How do I configure setup.py, Sphinx and so on? We provide proven answers! ----- Whenever a Python beginner starts with its own project he or she is confronted with the same technical questions. Questions about a well thought out directory structure to hold all the files. How setup.py needs to be configured and even what it is capable of like specifying entry_points and other goodies. We show from the experience of our yearslong work with Python how to structure your Python project in terms of folders, files, modules and packages. How to configure setup.py to specify your requirements, to use it with nosetests, with Sphinx and so on. We also elaborate on the usage of Git and Versioneer (https://github.com/warner/python-versioneer) to help you version your package.

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[ Project Brief] Web Scraping With Python Project

Data Pipelines With Python Project Solution

ClickMyProject, widely recognized as the premier project center in Madurai, delivers a broad range of project solutions for aspirants in various fields. We specialize in the provision of Python machine learning projects ,  online projects, readymade projects, and bulk projects in a multitude of domains, including Python, Java, engineering, and computer science. Whether you seek complex engineering tasks or unique computer science undertakings, ClickMyProject stands out as a reliable hub for various project needs. Website | Map  Fb | Insta | Linkedin | x | Pinterest  Contact detail : 96777 48277

[ Project Brief] After Work Data Science Data Wrangling With Python

[ Project Brief] Python Programming Basics

The Complete Python Bootcamp From Zero to Hero in Python (11 - Milestone Project 2)

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Code: # MODULES import pygame, sys import numpy as np # initializes pygame pygame.init() WIDTH = 600 HEIGHT = 600 LINE_WIDTH = 15 WIN_LINE_WIDTH = 15 BOARD_ROWS = 3 BOARD_COLS = 3 SQUARE_SIZE = 200 CIRCLE_RADIUS = 60 CIRCLE_WIDTH = 15 CROSS_WIDTH = 25 SPACE = 55 RED = (255, 0, 0) BG_COLOR = (28, 170, 136) LINE_COLOR = (23, 130, 135) CIRCLE_COLOR = (239, 231, 200) CROSS_COLOR = (66, 66, 66) screen = pygame.display.set_mode( (WIDTH, HEIGHT) ) pygame.display.set_caption( 'TIC TAC TOE' ) screen.fill( BG_COLOR ) board = np.zeros( (BOARD_ROWS, BOARD_COLS) ) def draw_lines(): # 1 horizontal pygame.draw.line( screen, LINE_COLOR, (0, SQUARE_SIZE), (WIDTH, SQUARE_SIZE), LINE_WIDTH ) # 2 horizontal pygame.draw.line( screen, LINE_COLOR, (0, 2 * SQUARE_SIZE), (WIDTH, 2 * SQUARE_SIZE), LINE_WIDTH ) # 1 vertical pygame.draw.line( screen, LINE_COLOR, (SQUARE_SIZE, 0), (SQUARE_SIZE, HEIGHT), LINE_WIDTH ) # 2 vertical pygame.draw.line( screen, LINE_COLOR, (2 * SQUARE_SIZE, 0), (2 * SQUARE_SIZE, HEIGHT), LINE_WIDTH ) def draw_figures(): for row in range(BOARD_ROWS): for col in range(BOARD_COLS): if board[row][col] == 1: pygame.draw.circle( screen, CIRCLE_COLOR, (int( col * SQUARE_SIZE + SQUARE_SIZE//2 ), int( row * SQUARE_SIZE + SQUARE_SIZE//2 )), CIRCLE_RADIUS, CIRCLE_WIDTH ) elif board[row][col] == 2: pygame.draw.line( screen, CROSS_COLOR, (col * SQUARE_SIZE + SPACE, row * SQUARE_SIZE + SQUARE_SIZE - SPACE), (col * SQUARE_SIZE + SQUARE_SIZE - SPACE, row * SQUARE_SIZE + SPACE), CROSS_WIDTH ) pygame.draw.line( screen, CROSS_COLOR, (col * SQUARE_SIZE + SPACE, row * SQUARE_SIZE + SPACE), (col * SQUARE_SIZE + SQUARE_SIZE - SPACE, row * SQUARE_SIZE + SQUARE_SIZE - SPACE), CROSS_WIDTH ) def mark_square(row, col, player): board[row][col] = player def available_square(row, col): return board[row][col] == 0 def is_board_full(): for row in range(BOARD_ROWS): for col in range(BOARD_COLS): if board[row][col] == 0: return False return True def check_win(player): # vertical win check for col in range(BOARD_COLS): if board[0][col] == player and board[1][col] == player and board[2][col] == player: draw_vertical_winning_line(col, player) return True # horizontal win check for row in range(BOARD_ROWS): if board[row][0] == player and board[row][1] == player and board[row][2] == player: draw_horizontal_winning_line(row, player) return True # asc diagonal win check if board[2][0] == player and board[1][1] == player and board[0][2] == player: draw_asc_diagonal(player) return True # desc diagonal win chek if board[0][0] == player and board[1][1] == player and board[2][2] == player: draw_desc_diagonal(player) return True return False def draw_vertical_winning_line(col, player): posX = col * SQUARE_SIZE + SQUARE_SIZE//2 if player == 1: color = CIRCLE_COLOR elif player == 2: color = CROSS_COLOR pygame.draw.line( screen, color, (posX, 15), (posX, HEIGHT - 15), LINE_WIDTH ) def draw_horizontal_winning_line(row, player): posY = row * SQUARE_SIZE + SQUARE_SIZE//2 if player == 1: color = CIRCLE_COLOR elif player == 2: color = CROSS_COLOR pygame.draw.line( screen, color, (15, posY), (WIDTH - 15, posY), WIN_LINE_WIDTH ) def draw_asc_diagonal(player): if player == 1: color = CIRCLE_COLOR elif player == 2: color = CROSS_COLOR pygame.draw.line( screen, color, (15, HEIGHT - 15), (WIDTH - 15, 15), WIN_LINE_WIDTH ) def draw_desc_diagonal(player): if player == 1: color = CIRCLE_COLOR elif player == 2: color = CROSS_COLOR pygame.draw.line( screen, color, (15, 15), (WIDTH - 15, HEIGHT - 15), WIN_LINE_WIDTH ) def restart(): screen.fill( BG_COLOR ) draw_lines() for row in range(BOARD_ROWS): for col in range(BOARD_COLS): board[row][col] = 0 draw_lines() player = 1 game_over = False while True: for event in pygame.event.get(): if event.type == pygame.QUIT: sys.exit() if event.type == pygame.MOUSEBUTTONDOWN and not game_over: mouseX = event.pos[0] # x mouseY = event.pos[1] # y clicked_row = int(mouseY // SQUARE_SIZE) clicked_col = int(mouseX // SQUARE_SIZE) if available_square( clicked_row, clicked_col ): mark_square( clicked_row, clicked_col, player ) if check_win( player ): game_over = True player = player % 2 + 1 draw_figures() if event.type == pygame.KEYDOWN: if event.key == pygame.K_r: restart() player = 1 game_over = False pygame.display.update() Source: https://www.youtube.com/watch?v=6pd43R1dErc Uploader: TheFiveSeven

Source code repositories for project python-web-server hosted on kenai.com. Original fetch URLs: https://svn.kenai.com/svn/python-web-server~subversion SVN exported with 'svnrdump' and compressed with lzip, git exported with 'git bundle', hg exported with 'hg bundle'.

After Work Data Science Clustering Analysis With Python Project

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Python tutorial for beginners in hindi and urdu: In this Tutorial you will learn how to connect mysql database with pythonn django project.

Computer and computer based activities have pervaded our teaching/learning process as well as research so much so that the 21st century academic activities in science and engineering (S & E) cannot be fully engaged without some level of computation. An important choice of a programming language especially in a developing country is the ease to learn it and its accessibility. Python's unique features and the possibility that any user connected to the internet can download the entire packages into any platform, install it and immediately begin to use it makes it a preferred environment for introducing students and new beginners to elegant and all purpose programming. Therefore in Africa, the Python African Tour project has been launched and I am coordinating its use in computational Science and Engineering (S & E) code-named Python African Computational Science and Engineering Tour (PACSET).

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