Podcasts about sympy

Python is used for a wide variety of software projects. One area it's really gained a huge amount of momentum is in the computational space (including data science). On this episode we welcome back Allen Downey to dive into a particular slice of this space: simulation problems and Python in Physics and Engineering in general. Links from the show Allen's web page: allendowney.com Allen's blog (Probably Overthinking It): allendowney.com/blog Allen on Twitter: @allendowney Allen on Mastodon: @allendowney@fosstodon.org Modeling and Simulation in Python book: allendowney.github.io Programming as a Way of Thinking: blogs.scientificamerican.com Think Python book: greenteapress.com Think OS book: greenteapress.com Pint package: pint.readthedocs.io Free version of the book and Jupyter notebooks: allendowney.github.io Published version: nostarch.com Elm programming language: elm-lang.org SymPy examples: docs.sympy.org Guinness World Record won for bungee 'dunk' into cup of tea: youtube.com Watch this episode on YouTube: youtube.com Episode transcripts: talkpython.fm --- Stay in touch with us --- Subscribe to us on YouTube: youtube.com Follow Talk Python on Mastodon: talkpython Follow Michael on Mastodon: mkennedy Sponsors influxdata Pybites PDM Talk Python Training

Right now, plenty of people are building businesses on social media platforms, on streaming platforms, and on market platforms that they don't control. That platform can make the rules in any way they want and remove access at any time. That means founders are potentially one step away from losing their livelihood. The same goes for consumers buying from these platforms: if you lose access to your account, there goes all your purchases. As it turns out, you were licensing everything, not buying it. On this sponsored episode of the podcast, we talk with Ripple CTO David Schwartz about the promise that decentralized trust and distributed consensus has for software development — and for more transparency in ownership. Episode notes:Cross-border payments, while they might not be the sexiest app, are one of the best product-market fits for blockchains. Learn more about Ripple at their home page. Check out the documentation to learn more about building on the XRP Ledger. Congrats to Lifeboat badge winner, asmeurer, for their answer to What does `S` signify in SymPy? 

Often when we learn about or work with Math, it's done so in a very detached style. You might learn the rules and techniques for differentiation, for example. But how often do you get to apply them to meaningful and interesting problems? In this episode, we have Vince Knight and Geraint Palmer on to discuss solving a wide variety of applied and approachable math problems using Python. Whether you're deeply into math or not so much, I think there is a lot to enjoy from this episode. Links from the show Applied Mathematics with Open-Source Software: taylorfrancis.com Book source files: ithub.com Vince on Twitter: @drvinceknight Geraint on Twitter: @geraintpalmer Traces Package: traces.readthedocs.io A Beautiful Mind: wikipedia.org Nashpy: github.com e: The Story of a Number: amazon.com SymPy episode: talkpython.fm 8451: 8451.com Stack Overflow Trends: stackoverflow.com PYCON UK 2017: Python for conducting operational research in healthcare: youtube.com Ciw package: github.com Python ternary: github.com Michael's in-person FastAPI course: maven.com Reimbursement templates for our courses Expense a Course at Talk Python: zoho.com Expense Course Bundle at Talk Python: zoho.com Expense Cohort Course at Talk Python: zoho.com Watch this episode on YouTube: youtube.com Episode transcripts: talkpython.fm --- Stay in touch with us --- Subscribe to us on YouTube: youtube.com Follow Talk Python on Twitter: @talkpython Follow Michael on Twitter: @mkennedy Sponsors RedHat Python at Scale AssemblyAI Talk Python Training

We're all familiar with the data science tools like numpy, pandas, and others. These are numerical tools working with floating point numbers, often to represent real-world systems. But what if you exactly specify the equations, symbolically like many of us did back in Calculus and Differential Equations courses? With SymPy, you can do exactly that. Create equations, integrate, differentiate, and solve them. Then you can convert those solutions into Python (or even C++ and Fortran code). We're here with two of the core maintainer: Ondřej Čertík and Aaron Meurer to learn all about SymPy. Links from the show Ondrej Certik: @OndrejCertik Aaron Meurer: @asmeurer SymPy: sympy.org SymPy Docs: docs.sympy.org/dev Tutorials: docs.sympy.org The SymPy/HackerRank DMCA Incident: asmeurer.com SymEngine: github.com SymPy Gamma: gamma.sympy.org Sovled derivative problem - wait for derivative steps to appear: gamma.sympy.org Github Takedown Repo: github.com e: The Story of a Number book: amazon.com Watch this episode on YouTube: youtube.com Episode transcripts: talkpython.fm --- Stay in touch with us --- Subscribe on YouTube: youtube.com Follow Talk Python on Twitter: @talkpython Follow Michael on Twitter: @mkennedy Sponsors Microsoft Sentry Error Monitoring, Code TALKPYTHON AssemblyAI Talk Python Training

In this Stories in AI podcast, Dr. Andy Terrel, VP of Data and Algorithms at Xometry, gave me valuable perspectives on how corporations, product managers, and data scientists should view artificial intelligence. If you are looking to advance your career as a data scientist or just in AI, this is a must listen episode. Andy's Bio: Dr. Andy R. Terrel is the VP, Data, and Algorithms of Xometry, Inc. where he is bringing his experience building smart scalable data systems to the manufacturing industry. You will also find him leading the infrastructure committee of the NumFOCUS foundation. As a passionate advocate for open source scientific codes Andy has been involved in the wider scientific Python community since 2006, contributing to numerous projects in the scientific stack. Dr. Andy R. Terrel was previously a Research Associate Scientist for the Computational Hydraulics Group at the Institute for Computational Engineering and Science which is at the University of Texas at Austin and High-Performance Computing researcher at the Texas Advanced Computing Center. Andy's research included utilizing supercomputers with Python and studying methods for speeding up computational fluid dynamics. He graduated from the University of Chicago with a Ph.D. in Computer Science in 2010 and has been programming in Python for the last decade. Andy has contributed to numerous open-source projects notably the FEniCS Project and Sympy. Reach Andy at:  http://andy.terrel.us https://www.linkedin.com/in/aterrel/ https://twitter.com/aterrel A note about our sponsors: Experian is the world's leading global information services company. We empower our clients to manage their data with confidence and build trusted relationships with consumers, using advanced analytics, decisioning technology and fraud prevention tools. We help businesses to make smarter decisions and thrive, lend more responsibly, and prevent fraud and financial crime. As the world's leading repository of consumer credit data, Experian is transforming data into solutions that facilitate transactions, ensure financial safety and improve the financial lives of millions of consumers around the world. Learn more at https://Experian.com.

Sponsored by Datadog: pythonbytes.fm/datadog Brian #1: D-Tale suggested by @davidouglasmit via twitter “D-Tale is the combination of a Flask back-end and a React front-end to bring you an easy way to view & analyze Pandas data structures. It integrates seamlessly with ipython notebooks & python/ipython terminals. Currently this tool supports such Pandas objects as DataFrame, Series, MultiIndex, DatetimeIndex & RangeIndex.” way cool UI for visualizing data Live Demo shows Describe shows column statistics, graph, and top 100 values filter, correlations, charts, heat map Michael #2: Carnets by Nicolas Holzschuch A standalone Jupyter notebooks implementation for iOS. The power of Jupyter notebooks. In your pocket. Anywhere. Everything runs on your device. No need to setup a server, no need for an internet connection. Standard packages like Numpy, Matplotlib, Sympy and Pandas are already installed. You're ready to edit notebooks. Carnets uses iOS 11 filesharing ability. You can store your notebooks in iCloud, access them using other apps, share them. Extended keyboard on iPads, you get an extended toolbar with basic actions on your keyboard. Install more packages: Add more Python packages with %pip (if they are pure Python). OpenSource: Carnets is entirely OpenSource, and released under the FreeBSD license. Brian #3: BeeWare Podium suggested by Katie McLaughlin, @glasnt on twitter NOT a pip install, download a binary from https://github.com/beeware/podium/releases Linux and macOS Still early, so you gotta do the open and trust from the apps directory thing for running stuff not from the app store. But Oh man is it worth it. HTML5 based presentation frameworks are cool. run a presentation right in your browser. My favorite has been remark.js presenter mode, notes are especially useful while practicing a talk running timer super helpful while giving a talk write talk in markdown, so it’s super easy to version control issues: presenter mode, full screen, with extended monitor hard to do. notes and timer on laptop, full presentation on extended screen super cool but requires full screening with mouse Podium uses similar syntax as remark.js and I think uses remark under the hood. but it’s a native app, not a browser Handles the presenter mode and extended screen smoothly, like keynote and others. Removes the need for boilerplate html in your markdown file (remark.js md files have cruft). Can’t wait to try this out for my next presentation Michael #4: pytest-mock-resources via Daniel Cardin pytest fixture factories to make it easier to test against code that depends on external resources like Postgres, Redshift, and MongoDB. Code which depends on external resources such a databases (postgres, redshift, etc) can be difficult to write automated tests for. Conventional wisdom might be to mock or stub out the actual database calls and assert that the code works correctly before/after the calls. Whether the actual query did the correct thing truly requires that you execute the query. Having tests depend upon a real postgres instance running somewhere is a pain, very fragile, and prone to issues across machines and test failures. Therefore pytest-mock-resources (primarily) works by managing the lifecycle of docker containers and providing access to them inside your tests. Brian #5: How James Bennet is testing in 2020 Follow up from Testing Django applications in 2018 Favors unittest over pytest. tox for testing over multiple Django and Python versions, including tox-travis plugin pyenv for local Python installation management and pyenv-virtualenv plugin for venvs. Custom runtests.py for setting up environment and running tests. Changed to src/ directory layout. Coverage and reporting failure if coverage dips, with a healthy perspective: “… this isn’t because I have 100% coverage as a goal. Achieving that is so easy in most projects that it’s meaningless as a way to measure quality. Instead, I use the coverage report as a canary. It’s a thing that shouldn’t change, and if it ever does change I want to know, because it will almost always mean something else has gone wrong, and the coverage report will give me some pointers for where to look as I start investigating.” Testing is more than tests, it’s also black, isort, flake8, mypy, and even spell checking sphinx documentation. Using tox.ini for utility scripts, like cleanup, pipupgrade, … Michael #6: Python and PyQt: Building a GUI Desktop Calculator by by Leodanis Pozo Ramos at realpython Some interesting take-aways: Basics of PyQt Widgets: QWidget is the base class for all user interface objects, or widgets. These are rectangular-shaped graphical components that you can place on your application’s windows to build the GUI. Layout Managers: Layout managers are classes that allow you to size and position your widgets at the places you want them to be on the application’s form. Main Windows: Most of the time, your GUI applications will be Main Window-Style. This means that they’ll have a menu bar, some toolbars, a status bar, and a central widget that will be the GUI’s main element. Applications: The most basic class you’ll use when developing PyQt GUI applications is QApplication. This class is at the core of any PyQt application. It manages the application’s control flow as well as its main settings. Signals and Slots: PyQt widgets act as event-catchers. Widgets always emit a signal, which is a kind of message that announces a change in its state. Due to Qt licensing, you can only use the free version for non-commercial projects or internal non-redistributed or purchase a commercial license for $5,500/yr/dev. Extras Brian PyCascades 2020 livestream videos of day 1 & day 2 are available. Huge shout-out and thank you to all of the volunteers for this event. In particular Nina Zakharenko for calming me down before my talk. Michael Recording for Python for .NET devs webcast available. Take some of our free courses with our mobile app. Joke Why do programmers confuse Halloween with Christmas? Because OCT 31 == DEC 25. Speed dating is useless. 5 minutes is not enough to properly explain the benefits of the Unix philosophy.

In unserer elften Episode reden wir mit Gerrit über Python in der Wissenschaft. Themen waren diesmal das Veröffentlichen von Code, das Setzen von Code in Veröffentlichungen und Codegolf. Es war etwas warm im Wintergarten, aber falls Auphonic es schafft, das Ventilatorengeräusch herauszufiltern, sollte zumindest die Audioqualität diesmal wieder passen. Apropos Audioqualität, einer der Sprecher hatte ein schlechteres Headset als die Anderen. Könnt ihr heraushören wer? Würde mich mal interessieren, ob man das überhaupt hören kann... Shownotes Unsere E-Mail für Fragen, Anregungen & Kommentare: hallo@python-podcast.de News aus der Szene PyOxidizer Russell Keith-Magee - Keynote - PyCon 2019 PyRun - funktioniert auch mit 3.7 Jessica Garson - Making Music with Python, SuperCollider and FoxDot - PyCon 2019 Jordan Adler, Joe Gordon - Migrating Pinterest from Python2 to Python3 - PyCon 2019 Codegolf Code Golf Stack Exchange LSD Radix Python in der Wissenschaft Differentialgleichungen SIMD Efficiently and easily integrating differential equations with JiTCODE, JiTCDDE, and JiTCSDE - JiTCODE, JiTCDDE, JiTCSDE SymPy SageMath MATLAB GNU Octave Cython arXiv gnuplot Altair Picks NumPy Data Classes Per object permissions for Django Bandit is a tool designed to find common security issues in Python code Öffentliches Tag auf konektom

En este capítulo presento qué son los Módulos, cómo se usan, los disponibles directamente con el lenguaje de programación Python, y una mención a las más destacables disponibles en la comunidad. . Pagina de información oficial: https://docs.python.org/3/library/index.html . Modulos de Python: TIME, DATETIME, RANDOM, MATH, STATISTICS, OS, OS.PATH, PATHLIB, SYS, SQLITE3, HASHLIB, CSV, GZIP, ZLIB, BZ2, LZMA, ZIPFILE, TARFILE, TKINTER,... . Módulos para Python: NumPy, SciPy, SymPy, BioPython, SQLAlchemi, Colorama, wxPython, PyQT, PyGTK, Kivy, Matplotlib, Seaborn, Bokeh, PyGame, PyGlet, Twisted, Scrapy, NLTK, Request, Pillow, Keras, Pytorch, Scikit-Learn, Pandas, Theano, TensorFlow,... . Aquí tenéis mi página web: https://unosycerospatxi.wordpress.com/ . UN SALUDO!!!!! Espero que os guste!!!

  Jon and Eric ramble through a few completely random topics. Pointless flaws in Apple Preference Panes, Stripe says goodbye to Bitcoin, Burger King takes on Net Neutrality and Jon almost earns himself a Darwin Award. Links: Flaw in AppStore System Preferences: https://www.macrumors.com/2018/01/10/macos-high-sierra-app-store-password-bug/ Stripe drops support for Bitcoin: https://stripe.com/blog/ending-bitcoin-support 50 Cent is a Bitcoin Millionaire: https://www.theverge.com/2018/1/24/16930010/50-cent-rich-bitcoin-twitter-instagram-humblebrag Linus rants... https://lkml.org/lkml/2018/1/21/192 Whopper Neutrality: http://www.latimes.com/business/la-fi-net-neutrality-burger-king-20180125-story.html Montana signs on net neutrality rules: https://www.engadget.com/2018/01/22/montana-governor-executive-order-requires-net-neutrality/ SymPy: http://www.sympy.org/en/index.html @AwardsDarwin: https://twitter.com/AwardsDarwin/status/956362264453533696

Looking for an open source alternative to Mathematica or MatLab for solving algebraic equations? Look no further than the excellent SymPy project. It is a well built and easy to use Computer Algebra System (CAS) and in this episode we spoke with the current project maintainer Aaron Meurer about its capabilities and when you might want to use it.