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Python Resources for Beginners
 
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Install or Use Python Includes all modules and Integrated Development Environment (IDE): Jupyter Notebook in a Web-Browser: https://try.jupyter.org Anaconda (Jupyter Notebook, Spyder, IPython): https://www.continuum.io/downloads PyCharm: https://www.jetbrains.com/pycharm/download Install each module through pip or conda (for Anaconda only): Python.org: https://www.python.org Numpy: https://sourceforge.net/projects/numpy/ Scipy: https://www.scipy.org/scipylib/download.html Matplotlib: http://matplotlib.org/users/installing.html Getting started: http://apmonitor.com/che263/index.php/Main/PythonIntroduction Courses BYU ChE263 Course Material: http://apmonitor.com/che263/ MIT EdX Python Course: https://www.edx.org/course/introduction-computer-science-mitx-6-00-1x8 Coursera (20 courses): https://www.coursera.org/courses/?query=PYTHON Code Academy: https://www.codecademy.com/learn/python Grok Learning (first 2 modules free): https://groklearning.com   Enthought Python Training: https://training.enthought.com Learn Python Programming in One Video: https://youtu.be/N4mEzFDjqtA Udemy (150 courses, 10 free): https://www.udemy.com/courses/search/?q=python MIT OpenCourseWare: YouTube Playlist - https://goo.gl/PaiQhQ Reference Material Pyplot Tutorial: http://matplotlib.org/users/pyplot_tutorial.html Numpy for MATLAB Users: https://docs.scipy.org/doc/numpy-dev/user/numpy-for-matlab-users.html Scipy and Numpy Reference Manual: https://docs.scipy.org/doc/ StackExchange / StackOverflow: http://stackoverflow.com/questions/tagged/python Python Tutorial: http://www.tutorialspoint.com/python/ Python String Format Cookbook: https://mkaz.tech/python-string-format.html Commonly Used If Statements: if (condition), elif (condition), else Loops:             for, while Range of integers:     range(start,end) Functions:         def function_name (inputs), return values Convert variable types:    int, float, str Create list:        x = [1, 2, ‘car’, ‘house’] First list element:    x[0] = 1 Last list element:    x[-1] = ‘house’ Slice part of list:        x[1:3]  = [2, ‘car’] Create tuple        y = (1,2,3) Numpy import numpy as np np.linspace(start,end,elements) np.empty(size) np.zeros(size) np.ones(size) np.arange(start,end,interval) np.concatenate np.vstack, np.hstack np.sum, np.max, np.min, np.median Matplotlib.pyplot import matplotlib.pyplot as plt %matplotlib inline  # This will show your plots in ipynb plt.plot(x,y) plt.pie(data) plt.bar(data) plt.xlabel(‘string’) plt.ylabel(‘string’) plt.legend([‘string1’,’string2’]) Random import random   random.randint(start,end) - random integer between start and end random.random() - random floating point number between 0 and 1
Views: 3056 APMonitor.com
Python Tutorial: Context Managers - Efficiently Managing Resources
 
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In this Python Programming Tutorial, we will be learning how to use context managers to properly manage resources. Context Managers are great for when we need to setup or teardown some resources during use. So these can be used for: open and closing files, opening and closing database connections, acquiring and releasing locks, and much much more. Let's get started... The code from this video can be found at: https://github.com/CoreyMSchafer/code_snippets/tree/master/Python-Context-Managers Python Object-Oriented Series: https://goo.gl/ZSqx6y If you enjoy these videos and would like to support my channel, I would greatly appreciate any assistance through my Patreon account: https://www.patreon.com/coreyms Or a one-time contribution through PayPal: https://goo.gl/649HFY If you would like to see additional ways in which you can support the channel, you can check out my support page: http://coreyms.com/support/ Equipment I use and books I recommend: https://www.amazon.com/shop/coreyschafer You can find me on: My website - http://coreyms.com/ Facebook - https://www.facebook.com/CoreyMSchafer Twitter - https://twitter.com/CoreyMSchafer Google Plus - https://plus.google.com/+CoreySchafer44/posts Instagram - https://www.instagram.com/coreymschafer/ #Python
Views: 20284 Corey Schafer
psutil module in python
 
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psutil (process and system utilities) is a cross-platform library for retrieving information on running processes and system utilization (CPU, memory, disks, network) in Python. It is useful mainly for system monitoring, profiling and limiting process resources and management of running processes. It implements many functionalities offered by command line tools such as: ps, top, lsof, netstat, ifconfig, who, df, kill, free, nice, ionice, iostat, iotop, uptime, pidof, tty, taskset, pmap. It currently supports Linux, Windows, OSX, Sun Solaris, FreeBSD, OpenBSD and NetBSD, both 32-bit and 64-bit architectures, with Python versions from 2.6 to 3.5 (users of Python 2.4 and 2.5 may use 2.1.3 version). PyPy is also known to work. For more information open the link https://pypi.python.org/pypi/psutil
Views: 4844 rajat shukla
[ROS Q&A] How to import python modules from different ROS packages
 
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More ROS Learning Resources: https://goo.gl/DuTPtK Learn how to import python modules from other packages and make them available for all the system. Also learn how to correctly install python scripts and modules. ----------Want to advance your ROS learning and master the latest Robotics topics?----------- ::Visit Robot Ignite Academy, try the platform for free: https://goo.gl/LBT7EN Robot Ignite Academy is an integrated ROS learning platform which contains a series of online ROS tutorials tied to online simulations, giving you the tools and knowledge to understand and create any ROS based robotics development. ------------You are ROS expert and want to develop your next ROS project?----------- ::Visit ROS Development Studio, try the platform for free: https://goo.gl/EtFqmE In ROS Development Studio, you will be able to: -develop ROS programs for robots in a faster and more effective way -test the programs in real time on the provided simulated robots -use graphical ROS tools which are included in the RDS -test what you have developed in the real robot all of these are using ONLY a web browser without any installation and not limited by any device.
Views: 1889 The Construct
Python 3 Programming Tutorial - urllib module
 
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The urllib module in Python 3 allows you access websites via your program. This opens up as many doors for your programs as the internet opens up for you. urllib in Python 3 is slightly different than urllib2 in Python 2, but they are mostly the same. Through urllib, you can access websites, download data, parse data, modify your headers, and do any GET and POST requests you might need to do. Sample code for this basics series: http://pythonprogramming.net/beginner-python-programming-tutorials/ Python 3 Programming tutorial Playlist: http://www.youtube.com/watch?v=oVp1vrfL_w4&feature=share&list=PLQVvvaa0QuDe8XSftW-RAxdo6OmaeL85M http://seaofbtc.com http://sentdex.com http://hkinsley.com https://twitter.com/sentdex Bitcoin donations: 1GV7srgR4NJx4vrk7avCmmVQQrqmv87ty6
Views: 190420 sentdex
How to Learn Python - Best Courses, Best Websites, Best YouTube Channels
 
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I show you how to learn python. There are so many python learning resources, how do you know which are the best? To save you time, I have collated what I think are the best python learning resources available. These are my favourite python courses, websites, books and YouTube channels. I hope you like them too. If this has been useful, then consider giving your support by buying me a coffee https://ko-fi.com/pythonprogrammer Links: Udemy The Python Mega Course - https://www.udemy.com/the-python-mega-course/ The Complete Python Bootcamp - https://www.udemy.com/complete-python-bootcamp/ The Modern Python 3 Bootcamp - https://www.udemy.com/the-modern-python3-bootcamp/ Automate the Boring Stuff Video Course - https://www.udemy.com/automate/ Udacity Introduction to Python - http://bit.ly/2GuhXzU Programming Foundations With Python - http://bit.ly/2GrjZB4 Coursera Python for Everybody - https://www.coursera.org/specializations/python Python Websites Github - http://bit.ly/2GoUuQN Python 3 Module of the Week - http://bit.ly/2IpZIle Introduction to Python - Quantitative Economics - http://bit.ly/2wQ5fw2 Books (affiliate links) Automate the Boring Stuff with Python - https://amzn.to/2wRjGjd Learn Python the Hard Way - https://amzn.to/2wPYWIz Python Programming - https://amzn.to/2rPYANo Python Crash Course - https://amzn.to/2IRXWZD YouTube Channels Sirar Raval - http://bit.ly/2rOYWDX MIT Python Course (Introduction to Computer Science and Programming) - http://bit.ly/2k4TbOb My Python Course - http://bit.ly/2rWq9nB Music by http://www.bensound.com #LearnPython #Python #HowtolearnPython
Views: 10876 Python Programmer
Python tutorial: HTTP requests to import data from the web
 
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Learn how to perform HTTP requests to import web data with Python: https://www.datacamp.com/courses/importing-data-in-python-part-2 Congrats on importing your first web data! In order to import files from the web, we used the urlretrieve function from urllib.requests. Lets now unpack this a bit and, in the process, understand a few things about how the internet works: URL stands for Uniform or Universal Resource Locator and all they really are are references to web resources. The vast majority of URLs are web addresses, but they can refer to a few other things, such as file transfer protocols (FTP) and database access. We'll currently focus on those URLs that are web addresses OR the locations of websites. Such a URL consists of 2 parts: A protocol identifier http ot https and A resource name such as datacamp.com The combination of protocol identifier and resource name uniquely specifies the web address! To explain URLs, I have introduced yet another acronym http, which itself stands for HyperText Transfer Protocol. Wikipedia provides a great description of HTTP: The Hypertext Transfer Protocol (HTTP) is an application protocol for distributed, collaborative, hypermedia information systems. HTTP is the foundation of data communication for the World Wide Web. Note that HTTPS is a more secure form of HTTP. Each time you go to a website, you are actually sending an HTTP request to a server. This request is known as a GET request, by far the most common type of HTTP request. We are actually performing a GET request when using the function urlretrieve. The ingenuity of urlretrieve also lies in fact that it not only makes a GET request but also saves the relevant data locally. In the following, you'll learn how to make more GET requests to store web data in your environment. In particular, you'll figure out how to get the HTML data from a webpage. HTML stands for Hypertext Markup Language and is the standard markup language for the web. To extract the html from the wikipedia home page, you Import the necessary functions; Specify the URL; Package the GET request using the function Request; Send the request and catch the response using the function urlopen; This returns an HTTPResponse object, which has an associated read method; You then apply this read method to the response, which returns the HTML as a string, which you store in the variable html. You remember to be polite and close the response! Now we are going to do the same, however here we'll use the requests package, which provides a wonderful API for making requests. According to the requests package website: Requests allows you to send organic, grass-fed HTTP/1.1 requests, without the need for manual labor. and the following organizations claim to use requests internally: Her Majesty's Government, Amazon, Google, Twilio, NPR, Obama for America, Twitter, Sony, and Federal U.S. Institutions that prefer to be unnamed Moreover, Requests is one of the most downloaded Python packages of all time, pulling in over 7,000,000 downloads every month. All the cool kids are doing it! Lets now see requests at work: Here you Import the package requests; Specify the URL; Package the request, send the request and catch the response with a single function requests.get(); Apply the text method to the response which returns the HTML as a string; That's enough out of me for the time being: let's get you hacking away at pulling down some HTML from the web using GET requests! GET coding!
Views: 38999 DataCamp
Teaching geometry using Logo/Python turtle module, or how to sneak programming into maths class
 
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Vivian Li https://2016.pycon-au.org/schedule/117/view_talk With the new national curriculum for Digital Technologies, there is a fantastic opportunity (and in some schools, need) to teach programming in conjunction with other learning areas. Python is an ideal first language, and there is a natural and deep relationship between mathematics and computer science. We used the Python’s Turtle module (an implementation of Logo) to create resources that integrate the Year 7 Geometry topic and the basics of Python programming in a way that’s highly engaging for students. Learn about how we integrated the two subject areas and the results of our pilot run with a cohort of Year 7 students in NSW.
Views: 1637 PyCon Australia
Python for Data Science - Resources for Learning Basic Python Programming
 
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This video presents a handful of useful resources for learning basic Python programming. The list is designed to quickly (in an hour or so) get new Python users familiar with the components of Python that are useful for general scripting in data science. Pandas, Scikit-learn, and similar tools are covered in detail in other videos so they are not addressed here. This video is for new Python users. If you're already familiar with basic Python programming concepts you can skip this one. Notes: General overviews: Python for Beginners - https://www.python.org/about/gettingstarted/ Beginner's Guide - https://wiki.python.org/moin/BeginnersGuide/Programmers Learn Python in 10 Minutes - https://www.stavros.io/tutorials/python/ Installing packages: You'll usually use pip and PyPi, so start here: https://packaging.python.org/tutorials/installing-packages/#installing-from-pypi When you need something more, i.e., installing from source or installing from Github read the rest: https://packaging.python.org/tutorials/installing-packages/ Importing packages: https://www.tutorialspoint.com/python/python_modules.htm Data types: E.g., booleans, numerics, strings, lists, sets, and dictionaries https://en.wikibooks.org/wiki/Python_Programming/Data_Types Control flow: E.g., for loops, if/elif/else, continue, break, next https://docs.python.org/3/tutorial/controlflow.html Defining functions: E.g., fizz_buzz() http://anh.cs.luc.edu/python/hands-on/3.1/handsonHtml/functions.html List also available at: https://www.bryancshepherd.com/programming/resources-learning-basic-python-programming/
MDAnalysis: A Python Package for the Rapid Analysis of Molecular Dynamics Simulations | SciPy 2016
 
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MDAnalysis (http://mdanalysis.org) is an object-oriented library for structural and temporal analysis of molecular dynamics (MD) simulation trajectories and individual protein structures. MD simulations of biological molecules have become an important tool to elucidate the relationship between molecular structure and physiological function. Simulations are performed with highly optimized software packages on HPC resources but most codes generate output trajectories in their own formats so that the development of new trajectory analysis algorithms is confined to specific user communities and widespread adoption and further development is delayed. The MDAnalysis library addresses this problem by abstracting access to the raw simulation data and presenting a uniform object-oriented Python interface to the user. It thus enables users to rapidly write code that is portable and immediately usable in virtually all biomolecular simulation communities. The user interface and modular design work equally well in complex scripted workflows, as foundations for other packages, and for interactive and rapid prototyping work in IPython/Jupyter notebooks, especially together with molecular visualization provided by nglview [1] and time series analysis with pandas [2]. MDAnalysis is written in Python and Cython and uses NumPy arrays for easy interoperability with the wider scientific Python ecosystem. It is widely used and forms the foundation for more specialized biomolecular simulation tools. MDAnalysis is available under the GNU General Public License v2. [1] https://github.com/arose/nglview [2] http://pandas.pydata.org/ Slides for this talk are available here: https://github.com/MDAnalysis/scipy-2016 See the complete SciPy 2016 Conference talk & tutorial playlist here: https://www.youtube.com/playlist?list=PLYx7XA2nY5Gf37zYZMw6OqGFRPjB1jCy6
Views: 6418 Enthought
The Top 10 Books To Learn Python
 
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👉🏻 Check Out The NEW Simple Programmer YouTube Channel With NEW Programming Videos: https://simpleprogrammer.com/yt/spnewytchannel 💻 SUBSCRIBE TO THIS CHANNEL: vid.io/xokz Learn Python The Hard Way: http://simpleprogrammer.com/learnpythonhard Think Python: How to Think Like a Computer Scientist: http://simpleprogrammer.com/thinkpython Dive Into Python 3: http://simpleprogrammer.com/diveintopython Core Python Programming: http://simpleprogrammer.com/corepython The Quick Python Book: http://simpleprogrammer.com/thequickpythonbook Beginning Python: From Novice to Professional: http://simpleprogrammer.com/beginningpython Hello World!: Computer Programming for Kids and Other Beginners: http://simpleprogrammer.com/programmingforkids Python Programming for the Absolute Beginner: http://simpleprogrammer.com/pythonabsolutebeginner Python Essential Reference: http://simpleprogrammer.com/pythonessentialreference Python Cookbook: https://simpleprogrammer.com/pythoncookbook Violent Python: http://simpleprogrammer.com/violentpython Top Programming Books Playlist: https://www.youtube.com/playlist?list=PLjwWT1Xy3c4XoA9VdooMPPiDFsckl1d_2 Trust The Process T-Shirt: https://simpleprogrammer.com/spstore The Top 10 Books To Learn Python Have you ever wondered what are the best books to learn Python? "Python is an interpreted, object-oriented, high-level programming language with dynamic semantics. Its high-level built in data structures, combined with dynamic typing and dynamic binding, make it very attractive for Rapid Application Development, as well as for use as a scripting or glue language to connect existing components together. Python's simple, easy to learn syntax emphasizes readability and therefore reduces the cost of program maintenance. Python supports modules and packages, which encourages program modularity and code reuse. The Python interpreter and the extensive standard library are available in source or binary form without charge for all major platforms, and can be freely distributed. Often, programmers fall in love with Python because of the increased productivity it provides. Since there is no compilation step, the edit-test-debug cycle is incredibly fast. Debugging Python programs is easy: a bug or bad input will never cause a segmentation fault. Instead, when the interpreter discovers an error, it raises an exception. When the program doesn't catch the exception, the interpreter prints a stack trace. A source level debugger allows inspection of local and global variables, evaluation of arbitrary expressions, setting breakpoints, stepping through the code a line at a time, and so on. The debugger is written in Python itself, testifying to Python's introspective power. On the other hand, often the quickest way to debug a program is to add a few print statements to the source: the fast edit-test-debug cycle makes this simple approach very effective." (Source: https://www.python.org/doc/essays/blurb/) I've programmed in Python before and I must say I love Python! In this video I'll list the top 10 books if you're serious about learning python, whether you're a beginner and or advanced (even a kid! lol) If you have a question, email me at [email protected] If you liked this video, share, like and, of course, subscribe! Subscribe To My YouTube Channel: http://bit.ly/1zPTNLT Visit Simple Programmer Website: http://simpleprogrammer.com/ Connect with me on social media: Facebook: https://www.facebook.com/SimpleProgrammer Twitter: https://twitter.com/jsonmez Other Links: Sign up for the Simple Programmer Newsletter: http://simpleprogrammer.com/email Simple Programmer blog: http://simpleprogrammer.com/blog Learn how to learn anything quickly: http://10stepstolearn.com Boost your career now: http://devcareerboost.com #python #pythonbooks #learnpython #programming #coding #pythontutorial
Views: 64687 Bulldog Mindset
HOW TO MAKE A JARVIS LIKE AI IN PYTHON 2(REQUIREMENTS)
 
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PLEASE SUPPORT ME ON PATREON : https://www.patreon.com/satyamshivhare In this video i have listed all the required modules nd resources i have used in this program. well the pyaudio module is required i you are using microphone as an input hardware whether it is internal or external.
Views: 6487 LEARN ANYTHING
Top 5: 11 Python resources, Linux system monitoring, and more
 
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Full article: https://opensource.com/life/16/4/top-5-april-22 Top 5 articles of the week 5. What sets Krita apart from other open source digital painting tools https://opensource.com/life/16/4/nick-hamilton-linuxfest-northwest-2016-krita 4. Free, high-quality education resources from the National Science Digital Library https://opensource.com/education/16/4/national-science-digital-library 3. Decoding DevOps, Docker, and Git https://opensource.com/business/16/4/linuxfest-northwest-interview-corey-quinn 2. Finding the signal in the noise of Linux system monitoring https://opensource.com/business/16/4/linuxfest-northwest-interview-ilan-rabinovitch 1. 11 resources for teaching and learning Python https://opensource.com/education/16/4/teaching-python-and-more-with-oer
Views: 672 Opensource.com
Python Tutorial: Working with JSON Data using the json Module
 
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In this Python Programming Tutorial, we will be learning how to work with JSON data. We will learn how to load JSON into Python objects from strings and how to convert Python objects into JSON strings. We will also see how to load JSON from a file and save those Python objects back to files. Let's get started... The code from this video can be found at: https://github.com/CoreyMSchafer/code_snippets/tree/master/Python-JSON Python File Objects: https://youtu.be/Uh2ebFW8OYM If you enjoy these videos and would like to support my channel, I would greatly appreciate any assistance through my Patreon account: https://www.patreon.com/coreyms Or a one-time contribution through PayPal: https://goo.gl/649HFY If you would like to see additional ways in which you can support the channel, you can check out my support page: http://coreyms.com/support/ Equipment I use and books I recommend: https://www.amazon.com/shop/coreyschafer You can find me on: My website - http://coreyms.com/ Facebook - https://www.facebook.com/CoreyMSchafer Twitter - https://twitter.com/CoreyMSchafer Google Plus - https://plus.google.com/+CoreySchafer44/posts Instagram - https://www.instagram.com/coreymschafer/ #Python
Views: 106540 Corey Schafer
How to fix no lapack/blas resources found - scipy python machine learning
 
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How to fix no lapack/blas resources found while installing scipy packge in python machine learning URL:http://www.lfd.uci.edu/~gohlke/pythonlibs/#scipy Command : pip install downloadedfile
Views: 4217 dython
Python Session | Tutorial (Beginner) Part 4: Function, Modules, Packages and List comprehension
 
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In this tutorial | session you will learn Conditions, Function, Modules, Packages and List comprehension. Platform: Any Code Editor: Python 3.6 | Any one of your choice Links to the resources: In-Class materials (Github): http://bit.ly/2g6V2jI Content: 1. Conditions: https://youtu.be/E7M7Or02lIM?t=12 2. Function: https://youtu.be/E7M7Or02lIM?t=254 3. Modules: https://youtu.be/E7M7Or02lIM?t=523 4. Packages: https://youtu.be/E7M7Or02lIM?t=662 5. List comprehension: https://youtu.be/E7M7Or02lIM?t=738 I strongly suggest you to practice, as there is no other short cut to master python, Happy learning :) Feel free to add your questions/comments/suggestions/area to improve in the comment section below. Connect with me: email: [email protected]
Views: 72 dalonlobo
Python Flask Tutorial: Full-Featured Web App Part 5 - Package Structure
 
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In this Python Flask Tutorial, we will be learning how to restructure our application into a package rather than running from a single module. This has major benefits in terms of importing modules across our application. Let's get started... The code for this series can be found at: https://github.com/CoreyMSchafer/code_snippets/tree/master/Python/Flask_Blog If you enjoy these videos and would like to support my channel, I would greatly appreciate any assistance through my Patreon account: https://www.patreon.com/coreyms Or a one-time contribution through PayPal: https://goo.gl/649HFY If you would like to see additional ways in which you can support the channel, you can check out my support page: http://coreyms.com/support/ Equipment I use and books I recommend: https://www.amazon.com/shop/coreyschafer You can find me on: My website - http://coreyms.com/ Facebook - https://www.facebook.com/CoreyMSchafer Twitter - https://twitter.com/CoreyMSchafer Google Plus - https://plus.google.com/+CoreySchafer44/posts Instagram - https://www.instagram.com/coreymschafer/ #Python #Flask
Views: 19927 Corey Schafer
Python Tutorial: pip - An in-depth look at the package management system
 
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In this video, we will take an in-depth look at Python's package management system, pip. We'll walk through how to install, uninstall, list, and upgrade packages. We will also dive into how we can output our dependencies and install a list of dependencies. An in-depth knowledge of pip can be a great addition to your Python tool-belt. If you enjoy these videos and would like to support my channel, I would greatly appreciate any assistance through my Patreon account: https://www.patreon.com/coreyms Or a one-time contribution through PayPal: https://goo.gl/649HFY If you would like to see additional ways in which you can support the channel, you can check out my support page: http://coreyms.com/support/ Equipment I use and books I recommend: https://www.amazon.com/shop/coreyschafer You can find me on: My website - http://coreyms.com/ Facebook - https://www.facebook.com/CoreyMSchafer Twitter - https://twitter.com/CoreyMSchafer Google Plus - https://plus.google.com/+CoreySchafer44/posts Instagram - https://www.instagram.com/coreymschafer/ #Python
Views: 58710 Corey Schafer
Python Tutorial: Unit Testing Your Code with the unittest Module
 
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In this Python Programming Tutorial, we will be learning how to unit-test our code using the unittest module. Unit testing will allow you to be more comfortable with refactoring and knowing whether or not your updates broke any of your existing code. Unit testing is a must on any large projects and is used by all major companies. Not only that, but it will greatly improve your personal code as well. Let's get started. The code from this video can be found at: https://github.com/CoreyMSchafer/code_snippets/tree/master/Python-Unit-Testing Unittest assert methods: https://docs.python.org/3/library/unittest.html#unittest.TestCase.debug if __name__ == '__main__' video: https://www.youtube.com/watch?v=sugvnHA7ElY OOP Series: https://www.youtube.com/playlist?list=PL-osiE80TeTsqhIuOqKhwlXsIBIdSeYtc If you enjoy these videos and would like to support my channel, I would greatly appreciate any assistance through my Patreon account: https://www.patreon.com/coreyms Or a one-time contribution through PayPal: https://goo.gl/649HFY If you would like to see additional ways in which you can support the channel, you can check out my support page: http://coreyms.com/support/ Equipment I use and books I recommend: https://www.amazon.com/shop/coreyschafer You can find me on: My website - http://coreyms.com/ Facebook - https://www.facebook.com/CoreyMSchafer Twitter - https://twitter.com/CoreyMSchafer Google Plus - https://plus.google.com/+CoreySchafer44/posts Instagram - https://www.instagram.com/coreymschafer/ #Python
Views: 126692 Corey Schafer
Which Python Package Manager Should You Use? (AI Adventures)
 
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In this episode of AI Adventures, Yufeng discusses some of the options available when it comes to managing your Python environment for machine learning and data science, and helps you make an informed decision based on your needs. Associated Medium post "Which Python package manager should you use?": https://goo.gl/uGwXiA Resources: Pip: https://goo.gl/YwDW8 Virtualenv: https://goo.gl/5ix9f Anaconda: https://goo.gl/thHWnF Pyenv: https://goo.gl/RU0Swa Watch more episodes of AI Adventures: https://goo.gl/UC5usG Subscribe to be notified of new episodes: https://goo.gl/S0AS51
Views: 20500 Google Cloud Platform
Useful Python Libraries for Network Engineers
 
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Season 1, Talk 1 of NetDevOps Live! explores several useful Python on libraries for network engineers. Full details at http://bit.ly/2MDnuam Presenter: Hank Preston https://twitter.com/hfpreston Topics Covered - Time Links - Introduction - Libraries to Work with Data - 1:52 - xmltodict - 3:30 - json - 8:14 - PyYAML - 11:21 - csv - 14:27 - pyang - 18:05 - API Libraries - 23:42 - requests & RESTCONF - 24:20 - ncclient & NETCONF - 32:56 - netmiko & CLI - 39:21 - pysnmp & SNMP - 44:03 - Configuration Management Tools - 46:26 - NAPALM - 47:52 - Ansible - 48:58 - Other Cool Python Tools - 49:51 - virlutils - 49:53 - pyATS - 51:45 - Summary - 52:59 - Webinar Resources - 54:03 - Code Exchange Challenge - 54:39 Episode Description: Python has quickly become THE language for network automation and programmability due to its combined simplicity and power. Add to that the robust assortment of tools, libraries and modules related to networking available to 'import' and I doubt another language will take over the title anytime soon. In this session we'll explore some of the most useful libraries for network engineers and developers looking to interact with the network from a configuration and operational perspective. Plenty of code will be shown, and all examples will be available to take away and leverage in your own environments. Through the session you'll learn how to leverage the tried and true interfaces of CLI and SNMP to manage your network before we jump up to newer options like NETCONF, RESTCONF and REST APIs. We'll even explore full configuration management solutions and discuss when and how they should fit into your overall automation strategy. NetDevOps Live! is produced by Cisco DevNet. Details can be found at https://developer.cisco.com/netdevops/live and follow NetDevOps Live! on Twitter at https://twitter.com/netdevopslive
Views: 4134 Cisco DevNet
Programming with Python - 24 - PyPI - the Python Package Index
 
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Additional Python Resources
Views: 20 #RandomStuff
Python for Beginners: Reading & Manipulating CSV Files
 
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A quick tutorial designed for anyone interested in Python and learning what basic programming skills can do for you. More Python training & resources at: https://newcircle.com
Views: 196047 InfoQ
31.Python для Начинающих - Конвертирование .py в Linux bin
 
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31.Python для Начинающих - Конвертирование .py в Linux bin 1.Устанавливаем и обновляем библиотеки Пайтона: sudo apt-get install --reinstall python-pkg-resources sudo apt-get install build-essential python-dev 2.Качаем PyInstaller: wget https://github.com/pyinstaller/pyinstaller/releases/download/v3.2/PyInstaller-3.2.tar.gz 3.Раcпаковываем PyInstaller: tar -xvf PyInstaller-3.2.tar.gz 4.Заходим в распакованный PyInstaller: cd PyInstaller-3.2 5.Устанавливаем PyInstaller: ./pyinstaller.py setup.py 6.Конвертим ваш .py файл: ./pyinstaller.py myscript.py Ваш бинарный байл будет в /PyInstaller-3.2/myscript/dist Буду рад паре баксов, можно даже Канадских :) https://www.paypal.me/DenisAstahov
Views: 2432 ADV-IT
vigneshwer dhinakaran - Pumping up Python modules using Rust - PyCon 2018
 
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Speaker: vigneshwer dhinakaran If you’ve spent much time writing (or debugging) Python performance problems, you’ve probably had a hard time managing memory with its limited language support. In this talk, we venture deep into the belly of the Rust Language to uncover the secret incantations for building high performance and memory safe Python extensions using Rust. Rust has a lot to offer in terms of safety and performance for high-level programming languages such Python, Ruby, Js and more with its easy Foreign Function Interface capabilities which enables developers to easily develop bindings for foreign code. Slides can be found at: https://speakerdeck.com/pycon2018 and https://github.com/PyCon/2018-slides
Views: 1961 PyCon 2018
AWS - Lambda | Clean your AWS account with ONE Lambda Function | PYTHON boto3
 
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In this practical video, we will write a Lambda Function in PYTHON which investigates your AWS account and deletes the resources which are costing you money. You will also learn how to use boto3 Python library. A detailed interactive video with DEMO of every step. boto3 Documentation -- https://boto3.readthedocs.io/en/latest/reference/services/ec2.html PYTHON Code --- https://aws-tutorials.blogspot.com/2018/06/aws-lambda-clean-your-aws-account-with-python.html ---------------------------------------------------------------------- I would request to look at our playlists to learn systematically for AWS Certifications --- Solutions Architect - https://www.youtube.com/watch?v=ywHFXfuJoSU&list=PLTyrc6mz8dg8YE7OpHYoaRILnf1RQM8bz &&& SysOps Administrator - https://www.youtube.com/watch?v=UFSH-KuDGj8&list=PLTyrc6mz8dg8grU1HEhVw4zoCyvAl5MBO ++++++++++++++++++++++++++++++++++++++++ SUBSCRIBE to our youtube channel - youtube.com/knowledgeindia I have answered lot of AWS Interview questions in LIVE sessions here -- https://www.youtube.com/playlist?list=PLTyrc6mz8dg_tEexS22k_gmssDmkWkEMd Connect with me on LinkedIn to read interesting AWS updates & Practical Scenario Questions --- https://www.linkedin.com/in/knowledgeindia Don't miss any updates, please follow my FB page http://fb.me/AWStutorials & Twitter - http://twitter.com/#!/knowledge_india And for AWS exercises & case-studies, you can refer our blog -- https://aws-tutorials.blogspot.com/ ++++++++++++++++++++++++++++++++++++++++
Views: 1524 Knowledge India
Learning Python Web Penetration Testing | 04 Resources Discovery
 
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===== ▼♪♫Welcome♫♪ ▼ ===== ►Subscribe : http://goo.gl/Xc3FFT Stop using automated testing tools. Customize and write your own tests with Python! While there are an increasing number of sophisticated ready-made tools to scan systems for vulnerabilities, Python allows testers to write system-specific scripts—or alter and extend existing testing tools—to find, exploit, and record as many security weaknesses as possible. This course will give you the necessary skills to write custom tools for different scenarios and modify existing Python tools to suit your application's needs. ▬▬▬▬▬▬▬▬▬▬ஜ۩۞۩ஜ▬▬▬▬▬▬▬▬▬▬▬▬▬▬ ►►►►► Archivos Base ◄◄◄◄◄ ►Sublime Text : https://www.sublimetext.com/ ► Brackets : http://brackets.io/ ► Xammp : https://www.apachefriends.org/es/index.html ► Jquery : https://jquery.com/ ► Python :https://www.python.org/ ►Eclipse : https://eclipse.org/downloads/ ► Netbeans : https://netbeans.org/ ► Angular JS : https://angularjs.org/ ▬▬▬▬▬▬▬▬▬▬ஜ۩۞۩ஜ▬▬▬▬▬▬▬▬▬▬▬▬▬▬ ======== Redes Sociales====== Twitter : http://goo.gl/UndqDt Recuerda darle like y suscríbete =D ░░░░░░░░░░░░▄▄ ░░░░░░░░░░░█░░█ ░░░░░░░░░░░█░░█ ░░░░░░░░░░█░░░█ ░░░░░░░░░█░░░░█ ███████▄▄█░░░░░██████▄ ▓▓▓▓▓▓█░░░░░░░░░░░░░░█ ▓▓▓▓▓▓█░░░░░░░░░░░░░░█ ▓▓▓▓▓▓█░░░░░░░░░░░░░░█ ▓▓▓▓▓▓█░░░░░░░░░░░░░░█ ▓▓▓▓▓▓█░░░░░░░░░░░░░░█ ▓▓▓▓▓▓█████░░░░░░░░░█ ██████▀░░░░▀▀██████ LIKE !!! ╔═╦╗╔╦═╦═╦╦╦╦╗╔═╦══╦═╗ ║╚╣║║║╚╣╔╣╔╣║╚╣═╬╗╔╣═╣ ╠╗║╚╝╠╗║╚╣║║║║║═╣║║║═╣ ╚═╩══╩═╩═╩╝╚╩═╩═╝╚╝╚═╝
Views: 2188 Hugo NEPT
Natural Language Processing in Python: Part 4 -- WordNet
 
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In this video, we consider the WordNet resource and look at how to make use of this resource within NLTK. Each video in this series will have a companion blog post, which covers the content of the video in greater detail, as well as a Github link to the Python code used. Both of these links are provided below: Blog Post: http://vprusso.github.io/blog/2018/natural-language-processing-python-4/ This video is part of a series on Natural Language Processing in Python. The link to the playlist may be accessed here: http://bit.ly/lp_nlp Python Code: https://github.com/vprusso/youtube_tutorials/blob/master/natural_language_processing/nlp_4.py If I've helped you, feel free to buy me a beer :) Bitcoin: 1CPDk4Hp4Fnh7tjeMdZBudmYAkCCcLqimT PayPal: https://www.paypal.me/VincentRusso1 Do you like the development environment I'm using in this video? It's a customized version of vim that's enhanced for Python development. If you want to see how I set up my vim, I have a series on this here: http://bit.ly/lp_vim If you've found this video helpful and want to stay up-to-date with the latest videos posted on this channel, please subscribe: http://bit.ly/lp_subscribe
Views: 932 LucidProgramming
GIS Python: Count shape files and Select by Location (2 Real world ArcPy examples)
 
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In this video I show how to start scripting with Python to GIS. This video lesson is focusing on Python GIS beginners who just starting work on ArcPy Python module. So I show how to find example GIS data in shape file format in Internet, Add this geographical data to GIS and apply Python code to this. In order to correctly run scripts you must to import ArcPy module to Python code. The content of ''GIS Python for beginners with real world ArcPy examples'' video lesson is: 0:05 - Step 1: Download GIS data from Internet 1:47 - Using PyCharm and IDLE ArcGIS Scripting with Python (Download and start using PyCharm Python Framework) 6:06 - Result of Example 1: get list of all Shape files in project directory. 2:48 - Example 1: Simple script - Find GIS feature list (shape files in project directory) with Python 6:29 - Example 2: Select by Location with Python in GIS. Here I explain how to use where_clause in MakeFeatureLayer_management command. 12:20 - Result of Example 2: Select and Show all airports WITHIN Mexico country polygon. Used commands: MakeFeatureLayer_management (http://resources.arcgis.com/en/help/main/10.1/index.html#//00170000006p000000) SelectLayerByLocation (http://resources.arcgis.com/en/help/main/10.1/index.html#//001700000072000000) SelectClassToFeatureClass_conversion - Write new GIS shape file to Output directory as a result. Download PyCharm: https://www.jetbrains.com/pycharm/download/#section=windows Thank you for watching! Please subscribe me for get more interest Python, GIS, Machine Learning videos in short future! Vytautas.
Views: 4333 Vytautas Bielinskas
Reproducible Data Analysis in Jupyter, Part 5/10: Creating a Python Package
 
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Jupyter notebooks provide a useful environment for interactive exploration of data. A common question I get, though, is how you can progress from this nonlinear, interactive, trial-and-error style of exploration to a more linear and reproducible analysis based on organized, packaged, and tested code. This series of videos presents a case study in how I personally approach reproducible data analysis within the Jupyter notebook. For more information and resources relating to these videos, see http://jakevdp.github.io/blog/2017/03/03/reproducible-data-analysis-in-jupyter/ In this fifth video, we move some of our generally-useful routines into a separate Python package.
Views: 6181 Jake Vanderplas
Overloading Operators in Python
 
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We explore the world of overloading operators in python - creating animal objects and having them add and multiply! CODE: https://github.com/chatasweetie/creating-animals Blog Post: https://chatasweetie.com/2016/08/11/overloading-operators-in-python/ RESOURCES: Bpython: http://www.bpython-interpreter.org/ Random module: https://docs.python.org/2/library/random.html Names module: https://pypi.python.org/pypi/names/ Emoji module: https://pypi.python.org/pypi/emoji/0.3.9 FOLLOW US DevelopHerDevelopHim http://twitter.com/DevelopHH Jessica http://twitter.com/chatasweetie James http://twitter.com/theJamesCha http://www.develophh.com/ James's Podcast http://www.devmunity.com/ Jessica's Blog http://chatasweetie.com
Views: 1211 DevelopHerDevelopHim
Starfish: image based transcriptomics for the human cell atlas
 
19:44
Deep Ganguli — Chan Zuckerberg Initiative https://bids.berkeley.edu/resources/videos/starfish-python-library-image-based-transcriptomics This talk was presented as part of the third annual 2018 ImageXD Workshop (bids.berkeley.edu/news/2018-imagexd-workshop–-using-images-cross-science-boundaries-and-domains) held at BIDS on May 16-18, 2018.
Blender/Python 16.3 Exercise - 3D Mandelbrot by Matrix
 
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-------------------Python/Blender 16.3 - Mandelbrot set by Matrix------------- Create your own 3D Mandelbrot set in Blender, a free online 3D software which I particularly love. Open source and free to download in link below, Visualise fractals in the 3D environment using the familiar Mandelbrot set. By taking advantage of 3D graphing, we can hopefully enjoy ourselves coding in python but also learn along the way. Using Blender for 3D Interface and built in Python interface. Loading inbuilt 'Math utilities' library for easy matrix manipulation. --------------------DOWNLOAD RESOURCES----------------------------------- You can download EVERYTHING that was used to record this video and feel free to use all resources for any educational purposes. Please just thank us if practical; during presentation or in description. File Download - http://www.mediafire.com/?7lcgyoda3s1ja Blender Download -https://www.blender.org/ or http://graphicall.org/ Blender 0.0 - All Resources - https://www.mediafire.com/folder/80mh78khy1bdm/Blender_0.0_-_Getting_Started_-_Downloading_and_installing ---------------------MORE-------------------------------------------- Everything You Need to Know have dedicated their time to free education. EYN2K is a registered Australian business. Each video takes many hours to plan, film, edit and upload. We do this all for free, please support us by subscribing or visit us at Patreon Patreon : https://www.patreon.com/EYN2K Google+ : https://plus.google.com/u/0/115589514656500200774/posts Facebook : https://www.facebook.com/EYN2K?ref=hl website : http://www.eyn2k.com.au/ Twitter : #eyntok SPECIAL THANKS TO : Numberphile - https://www.youtube.com/watch?v=NGMRB4O922I Blender 3D Python Gimp ----------------------MUSIC--------------------------------------------------- RW Smith - Turn On - https://www.youtube.com/watch?v=EzIBvLO8V9s A-Himitsu -Adventures - https://soundcloud.com/a-himitsu/adventures Jimmy Square - Like Apollo - https://soundcloud.com/jimmysquare/z6q0jhxtdsjr ------------------------------------------------------------------------------------ CREDIT LIST : Coffee -Ahhhh, that sweet coffee
Python Tutorial: Image Manipulation with Pillow
 
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In this video we will learn how to modify and manipulate images using the Python Pillow Library. Pillow is a fork of the Python Imaging Library (PIL). It will allow us to do many different things to our images such as: changing their file extension, resizing, cropping, changing colors, blurring, and much more. Pillow is extremely useful when you have multiple images you wish to process at once. For example, you can use Pillow to automatically create different sized thumbnails of images you upload to your web server. Let's get started. If you enjoy these videos and would like to support my channel, I would greatly appreciate any assistance through my Patreon account: https://www.patreon.com/coreyms Or a one-time contribution through PayPal: https://goo.gl/649HFY If you would like to see additional ways in which you can support the channel, you can check out my support page: http://coreyms.com/support/ Equipment I use and books I recommend: https://www.amazon.com/shop/coreyschafer You can find me on: My website - http://coreyms.com/ Facebook - https://www.facebook.com/CoreyMSchafer Twitter - https://twitter.com/CoreyMSchafer Google Plus - https://plus.google.com/+CoreySchafer44/posts Instagram - https://www.instagram.com/coreymschafer/ #Python
Views: 88313 Corey Schafer
Python Editor IDLE
 
11:09
More Python resources (including example code and video) by Dr Anne Dawson can be found at: www.annedawson.net. How to get started using Python's IDLE editor to write programs in the Python language. This movie is meant for absolute beginners and does NOT include a discussion of the range of features provided with IDLE. In this demonstration, Python 3 is used, but the same instructions can be used for Python 2 (including the example program). The demonstration is done on a Linux Ubuntu 10.04 laptop, but the IDLE editor runs in exactly the same way on Windows and Mac machines. More Python resources by Dr Anne Dawson can be found at: www.annedawson.net.
Views: 135092 Anne Dawson
Effective Python package management (Devon Bernard)
 
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This talk showcases various example scenarios around the ins/outs of managing Python packages. Scenarios range from intra-package problems of how to build a package, inter-package problems of how to connect packages, to general usage/environment/setup problems that developers have a hard time debugging. For each of these scenarios, I'll highlight the pros/cons, best practices, and how to overcome the issues developers commonly face. Some examples: - installing packages from public, local file system, git (ssh + https), and how this affects deployment procedures - handling systems with multiple Python versions or package managers - how app structures and imports can affect testability - relative vs absolute imports - virtual environments - setup scripts - package versioning (why, when, how) - how new code changes could not be taking effect (old .pyc files, or updated import not catching) - handling sub-packages/dependencies that utilize parent configuration files - working with PYTHONPATH - executing python as modules or scripts Presentation page -- https://2017.pycon.ca/schedule/2/
Views: 234 PyCon Canada
Python Tutorial: Web Scraping with BeautifulSoup and Requests
 
45:48
In this Python Programming Tutorial, we will be learning how to scrape websites using the BeautifulSoup library. BeautifulSoup is an excellent tool for parsing HTML code and grabbing exactly the information you need. So whether you're pulling down headlines from news sites, scores from sports websites, or prices from an online store... BeautifulSoup and Python will help you get this done quickly and easily. Let's get started... The code from this video can be found at: https://github.com/CoreyMSchafer/code_snippets/tree/master/BeautifulSoup Difference Between Parsers: https://goo.gl/zdy9br Python File Objects: https://youtu.be/Uh2ebFW8OYM Python Strings: https://youtu.be/k9TUPpGqYTo Python Try/Except: https://youtu.be/NIWwJbo-9_8 Python CSV Files: https://youtu.be/q5uM4VKywbA If you enjoy these videos and would like to support my channel, I would greatly appreciate any assistance through my Patreon account: https://www.patreon.com/coreyms Or a one-time contribution through PayPal: https://goo.gl/649HFY If you would like to see additional ways in which you can support the channel, you can check out my support page: http://coreyms.com/support/ Equipment I use and books I recommend: https://www.amazon.com/shop/coreyschafer You can find me on: My website - http://coreyms.com/ Facebook - https://www.facebook.com/CoreyMSchafer Twitter - https://twitter.com/CoreyMSchafer Google Plus - https://plus.google.com/+CoreySchafer44/posts Instagram - https://www.instagram.com/coreymschafer/ #Python
Views: 109135 Corey Schafer
Quickstart for Installing Python cx_Oracle 6 for Oracle Database on Linux
 
03:03
This quickstart video shows the simple steps for installing and running a Python application on Linux that accesses Oracle Database using the cx_Oracle interface. Ask questions at https://github.com/oracle/python-cx_Oracle/issues Useful Resources: Home page: https://oracle.github.io/python-cx_Oracle/index.html Installation instructions: http://cx-oracle.readthedocs.io/en/latest/installation.html Documentation: http://cx-oracle.readthedocs.io/en/latest/ Examples: https://github.com/oracle/python-cx_Oracle/tree/master/samples Oracle Linux: http://yum.oracle.com/ Oracle Instant Client: http://www.oracle.com/technetwork/database/features/instant-client Blog: https://blogs.oracle.com/opal/ Twitter: https://twitter.com/ghrd https://twitter.com/AnthonyTuininga
Arcpy Getting Started (Python in ArcGIS)
 
18:45
Introduction to python (Arcpy) in ArcGIS. For noobs getting started with arcpy module and python. Video demonstrates how to use geoprocessing tools to generate python code for use within and outside of ArcMap.
Views: 12442 Jido Herox
Learn Python - Full Course for Beginners
 
04:26:52
This course will give you a full introduction into all of the core concepts in python. Follow along with the videos and you'll be a python programmer in no time! ⭐️ Contents ⭐ ⌨️ (0:00) Introduction ⌨️ (1:45) Installing Python & PyCharm ⌨️ (6:40) Setup & Hello World ⌨️ (10:23) Drawing a Shape ⌨️ (15:06) Variables & Data Types ⌨️ (27:03) Working With Strings ⌨️ (38:18) Working With Numbers ⌨️ (48:26) Getting Input From Users ⌨️ (52:37) Building a Basic Calculator ⌨️ (58:27) Mad Libs Game ⌨️ (1:03:10) Lists ⌨️ (1:10:44) List Functions ⌨️ (1:18:57) Tuples ⌨️ (1:24:15) Functions ⌨️ (1:34:11) Return Statement ⌨️ (1:40:06) If Statements ⌨️ (1:54:07) If Statements & Comparisons ⌨️ (2:00:37) Building a better Calculator ⌨️ (2:07:17) Dictionaries ⌨️ (2:14:13) While Loop ⌨️ (2:20:21) Building a Guessing Game ⌨️ (2:32:44) For Loops ⌨️ (2:41:20) Exponent Function ⌨️ (2:47:13) 2D Lists & Nested Loops ⌨️ (2:52:41) Building a Translator ⌨️ (3:00:18) Comments ⌨️ (3:04:17) Try / Except ⌨️ (3:12:41) Reading Files ⌨️ (3:21:26) Writing to Files ⌨️ (3:28:13) Modules & Pip ⌨️ (3:43:56) Classes & Objects ⌨️ (3:57:37) Building a Multiple Choice Quiz ⌨️ (4:08:28) Object Functions ⌨️ (4:12:37) Inheritance ⌨️ (4:20:43) Python Interpreter Course developed by Mike Dane. Check out his YouTube channel for more great programming courses: https://www.youtube.com/channel/UCvmINlrza7JHB1zkIOuXEbw 🐦Follow Mike on Twitter - https://twitter.com/mike_dane 🔗The Mike's website: https://www.mikedane.com/ ⭐️Other full courses by Mike Dane on our channel ⭐️ 💻C: https://youtu.be/KJgsSFOSQv0 💻C++: https://youtu.be/vLnPwxZdW4Y 💻SQL: https://youtu.be/HXV3zeQKqGY 💻Ruby: https://youtu.be/t_ispmWmdjY 💻PHP: https://youtu.be/OK_JCtrrv-c 💻C#: https://youtu.be/GhQdlIFylQ8 -- Learn to code for free and get a developer job: https://www.freecodecamp.org Read hundreds of articles on programming: https://medium.freecodecamp.org And subscribe for new videos on technology every day: https://youtube.com/subscription_center?add_user=freecodecamp
Views: 2129754 freeCodeCamp.org
Python Programming with the Rhinoscript Library: Introduction, Welcome and Resources Available
 
03:47
View full course here: https://www.kadenze.com/courses/computing-form-and-shape-python-programming-with-the-rhinoscript-library/info Lesson 1 of Computing Form and Shape: Python Programming with the Rhinoscript Library, taught by Carl Lostritto
Views: 60 Kadenze
Python 2 vs 3 - Which should you learn?
 
03:42
Should you learn python 2 or python 3. What's the difference between python 2 and python 3. Take a look at my facebook page https://www.facebook.com/LearnPythonRapidly/ This channel is focussed on learning python, here are some python learning resources (a mixture of affiliate and non affiliate links) If this has been useful, then consider giving your support by buying me a coffee https://ko-fi.com/pythonprogrammer Udacity Introduction to Python - http://bit.ly/2GuhXzU Programming Foundations With Python - http://bit.ly/2GrjZB4 Python Websites Github - http://bit.ly/2GoUuQN Python 3 Module of the Week - http://bit.ly/2IpZIle Introduction to Python - Quantitative Economics - http://bit.ly/2wQ5fw2 Books (affiliate links) Automate the Boring Stuff with Python - https://amzn.to/2wRjGjd Learn Python the Hard Way - https://amzn.to/2wPYWIz Python Programming - https://amzn.to/2rPYANo Python Crash Course - https://amzn.to/2IRXWZD YouTube Channels Sirar Raval - http://bit.ly/2rOYWDX MIT Python Course (Introduction to Computer Science and Programming) - http://bit.ly/2k4TbOb My Python Course - http://bit.ly/2rWq9nB Music by bensound.com
Views: 511 Python Programmer
Overview of Online Learning Resources
 
23:59
There's a vast amount of websites and resources online where you can go to learn about programming, web development, web design, software engineering, or anything else you could want. A lot of these online resources are either free or cost very little money. Let me show you some of my favorites so that you can begin learning or refreshing your knowledge on a wide variety of topics. If you enjoy these videos and would like to support my channel, I would greatly appreciate any assistance through my Patreon account: https://www.patreon.com/coreyms Or a one-time contribution through PayPal: https://goo.gl/649HFY If you would like to see additional ways in which you can support the channel, you can check out my support page: http://coreyms.com/support/ Equipment I use and books I recommend: https://www.amazon.com/shop/coreyschafer You can find me on: My website - http://coreyms.com/ Facebook - https://www.facebook.com/CoreyMSchafer Twitter - https://twitter.com/CoreyMSchafer Google Plus - https://plus.google.com/+CoreySchafer44/posts Instagram - https://www.instagram.com/coreymschafer/
Views: 8634 Corey Schafer
Cloudknot: A Python Library to Run your Existing Code on AWS Batch | SciPy 2018 | Richie-Halford
 
28:05
In this presentation, we introduce Cloudknot, a software library that simplifies cloud-based distributed computing by programmatically executing user-defined functions (UDFs) in AWS Batch. It takes as input a Python function, packages it as a container, creates all the necessary AWS constituent resources to submit jobs, monitors their execution and gathers the results, all from within the Python environment. Cloudknot overcomes limitations of previous similar libraries, such as pywren, that runs UDFs on AWS Lambda, because most data science workloads exceed the AWS Lambda limits on execution time, RAM, and local storage. See the full SciPy 2018 playlist at https://www.youtube.com/playlist?list=PLYx7XA2nY5Gd-tNhm79CNMe_qvi35PgUR
Views: 458 Enthought
5 Ideas to Help you Think Like a Programmer in Python!
 
05:53
How to think like a programmer in python. The are several strands to learning python. You have to learn the syntax of python and the basic structure of python and the important methods and functions in python. But the most important aspect of learning python or any programming language is to think like a programmer. In this video I talk about why learning to think like a programmer in python is essential to your python journey. I also give some very useful resource to help you do this. The websites are:- If this has been useful, then consider giving your support by buying me a coffee https://ko-fi.com/pythonprogrammer http://www.projecteuler.net http://www.pythonchallenge.com http://www.hackerrank.com http://www.practice python.org Python Playground https://amzn.to/2AmqYgX If you want to learn python, check out these resources:- Udacity Introduction to Python - http://bit.ly/2GuhXzU Programming Foundations With Python - http://bit.ly/2GrjZB4 Python 3 Module of the Week - http://bit.ly/2IpZIle Introduction to Python - Quantitative Economics - http://bit.ly/2wQ5fw2 Books (affiliate links) Automate the Boring Stuff with Python - https://amzn.to/2wRjGjd Learn Python the Hard Way - https://amzn.to/2wPYWIz Python Programming - https://amzn.to/2rPYANo Python Crash Course - https://amzn.to/2IRXWZD YouTube Channels Sirar Raval - http://bit.ly/2rOYWDX MIT Python Course (Introduction to Computer Science and Programming) - http://bit.ly/2k4TbOb My Python Course - http://bit.ly/2rWq9nB Music by http://www.bensound.com
Views: 1210 Python Programmer
Recommendation Systems - Learn Python for Data Science #3
 
06:57
In this video, we build our own recommendation system that suggests movies a user would like in 40 lines of Python using the LightFM recommendation library. I start off by talking about why we need recommendation systems, then we dive straight into installing our dependencies and writing our script. The coding challenge for this video is here: https://github.com/llSourcell/recommender_system_challenge The winner of last weeks coding challenge (Rohan Verma): https://twitter-sentiment-csv.herokuapp.com/ https://t.co/4eg8UdlaSB The runner up (Arnaud Delauney): https://github.com/arnauddelaunay/twitter_sentiment_challenge I created a Slack channel for us, sign up here: https://wizards.herokuapp.com/ The LightFM Python Library: https://github.com/lyst/lightfm/tree/master/lightfm Some great learning resources on recommender systems: http://blogs.gartner.com/martin-kihn/how-to-build-a-recommender-system-in-python/ https://www.analyticsvidhya.com/blog/2015/08/beginners-guide-learn-content-based-recommender-systems/ http://www.quuxlabs.com/blog/2010/09/matrix-factorization-a-simple-tutorial-and-implementation-in-python/ http://blog.manugarri.com/a-short-introduction-to-recommendation-systems/ Best book to become a Python God: https://learnpythonthehardway.org/ Please share this video, like, comment and subscribe! That's what keeps me going. Please support me on Patreon!: https://www.patreon.com/user?u=3191693 Follow me: Twitter: https://twitter.com/sirajraval Facebook: https://www.facebook.com/sirajology Instagram: https://www.instagram.com/sirajraval/ Instagram: https://www.instagram.com/sirajraval/ Signup for my newsletter for exciting updates in the field of AI: https://goo.gl/FZzJ5w
Views: 103017 Siraj Raval
Keras Explained
 
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Whats the best way to get started with deep learning? Keras! It's a high level deep learning library that makes it really easy to write deep neural network models of all sorts. It can use several popular backends like Tensorflow and CNTK. I'll show you how it works and explain how it compares to the other deep learning libraries. Code for this video: https://github.com/llSourcell/keras_explained Alberto's Winning Code: https://github.com/alberduris/Reinforcement_Learning_AI_Video_Games/tree/master/Week%206 Sven's Runner-up Code: https://github.com/EmbersArc/PPO Please Subscribe! And like. And comment. That's what keeps me going. Connect with me: Twitter: https://twitter.com/sirajraval Facebook: https://www.facebook.com/sirajology Instagram: https://www.instagram.com/sirajraval More learning resource: https://elitedatascience.com/keras-tutorial-deep-learning-in-python https://keras.io/ https://machinelearningmastery.com/tutorial-first-neural-network-python-keras/ https://github.com/fchollet/keras-resources https://www.datacamp.com/community/tutorials/deep-learning-python https://dashee87.github.io/data%20science/deep%20learning/python/another-keras-tutorial-for-neural-network-beginners/ Join us in the Wizards Slack channel: http://wizards.herokuapp.com/ And please support me on Patreon: https://www.patreon.com/sirajraval Signup for my newsletter for exciting updates in the field of AI: https://goo.gl/FZzJ5w
Views: 108994 Siraj Raval
Hardy Cross Pipe Networks in Python (part 2 of 2)
 
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Solving Example 2.11 from Chin Water Resources Engineering, 3rd edition, using Python programming and the numpy library. Part 2 of 2. Made for CVEN 5423, Fall 2017, University of Colorado Boulder. Part 1 of this series: https://www.youtube.com/watch?v=xkxp6TwSX3E (Recorded with http://screencast-o-matic.com)
Views: 253 jwharding5
Python Tutorial: Variable Scope - Understanding the LEGB rule and global/nonlocal statements
 
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In this Python Tutorial, we will be going over variable scope in Python. Scope is important because we need to understand it in just about every program we write. It allows us to understand where our variables can be seen from within our program and also what values these variables hold. It also helps with debugging, because scope is a common problem when errors are thrown. Let's get started. The code from this video can be found at: https://github.com/CoreyMSchafer/code_snippets/tree/master/Scope If you enjoy these videos and would like to support my channel, I would greatly appreciate any assistance through my Patreon account: https://www.patreon.com/coreyms Or a one-time contribution through PayPal: https://goo.gl/649HFY If you would like to see additional ways in which you can support the channel, you can check out my support page: http://coreyms.com/support/ Equipment I use and books I recommend: https://www.amazon.com/shop/coreyschafer You can find me on: My website - http://coreyms.com/ Facebook - https://www.facebook.com/CoreyMSchafer Twitter - https://twitter.com/CoreyMSchafer Google Plus - https://plus.google.com/+CoreySchafer44/posts Instagram - https://www.instagram.com/coreymschafer/ #Python
Views: 56547 Corey Schafer
Python NETCONF/YANG library part 1 – network programmability stream 27
 
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This is the recording of the 27th network programmability stream which occurred on 2018/10/14 During this stream I started working on my new open-source project – Python NETCONF/YANG library. During this stream, we partially completed dict to XML converter. Timecode: 0:00 – Introduction 2:55 – Announcements/News 9:25 – What we did last time 13:48 – Idea behind Python NETCONF/YANG library 32:25 – Convert Python dictionary to XML: search for an existing solution 1:18:58 – Writing my own solution to convert Python dictionary to XML 2:45:00 – Stream summary and wrap-up Resources: Talk Python to Me #181: 30 amazing Python projects – https://talkpython.fm/episodes/show/181/30-amazing-python-projects Please note that this content is stream-first and it is slow-paced by design. I recommend increasing playback speed in the player settings. Don't miss my upcoming streams at https://twitch.tv/dmfigol The code is on my GitHub: https://github.com/dmfigol/network-programmability-stream Twitter: https://twitter.com/dmfigol Blog: https://dmfigol.me Background music (royalty-free): https://www.pretzel.rocks/
Views: 146 Dmitry Figol

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