docker-python

but no CAS Computer Algebra System. The JupyterLite build of JupyterLab - and https://NumPy.org/ - include the SymPy CAS Computer Algebra System and a number of other libraries; and there's an `assert` statement in Python; but you'd need to build your own JupyterLab WASM bundle to host as static HTML if you want to include something controversial like pytest-hypothesis. https://jupyterlite.rtfd.io/ Better than a TI-83 Plus emulator? Install MambaForge in a container to get the `conda` and `mamba` package managers (and LLVM-optimized CPython on Win, Mac, Lin) and then `mamba install -y jupyterlab tabulate pandas matplotlib sympy`; or login to e.g. Google Colab, Cocalc, or https://Kaggle.com/learn ( https://GitHub.com/Kaggle/docker-python ) . To install packages every time a notebook runs: !python -m pip install # or %pip install <pkgs> !conda install -y !mamba install -y But NumPy.org, JupyterLite, and Colab, and Kaggle Learn all already have a version of SymPy installed (per their reproducible software version dependency files; requirements.txt, environment.yml (Jupyter REES; repo2docker)) Like MAME, which is the emulator for the TI-83 Plus and other calculators hosted by this new Internet Archive project, Emscripten-forge builds WASM (WebAssembly) that runs in an application-sandboxed browser tab as the same user as other browser tab subprocesses.

tube.com/watch?v=sL_syMmRkoU - pysandbox (author recommends running Python in a sandbox, not the opposite) - Interactive Shells on Python.org: - Link: https://blog.pythonanywhere.com/83/ - CodeSandbox: Online web application editor (Angular, React, Vue, Vanilla JS) - Website: https://codesandbox.io/ - Repo: https://github.com/codesandbox/codesandbox-client - Kaggle: - Description: Kaggle's infrastructure and systems allow for arbitrary code execution and scoring. It would be good to check it out and see what they get right. - Kaggle Learntools: - Purpose: Check exercises and notebooks submitted by users - Link: https://github.com/Kaggle/learntools - Kaggle Docker: - Purpose: Kaggle Python docker image - Link: https://github.com/Kaggle/docker-python/ - Kaggle Infrastructure (Lessons Learned from Tens of Thousands of Kaggle Notebooks): - Link: https://www.youtube.com/watch?v=ENPBTl0uNOE - Miscellaneous Links: - Jinja has a sandboxed environment: - Link: https://github.com/pallets/jinja/blob/master/jinja2/sandbox.py

or more processes in the container (which may have read access to /etc or environment variables and/or --privileged) - Necessary to build basically everything on non x86[-64] architectures for every container build Disadvantages of conda / conda+pip: - Different package repo infrastructure to mirror - Users complaining that they don't need conda who then proceed to re-download and re-build wheels locally multiple times a day Additional attributes for comparison: - The new pip solver (which is slower than the traditional iterative non-solver), conda, and mamba - repo2docker (and thus BinderHub) can build an up-to-date container from requirements.txt, environment.yml, install.R, postBuild and any of the other dependency specification formats supported by REES: Reproducible Environment Execution Standard; which may be helpful as Docker Hub images will soon be deleted if they're not retrieved at least once every 6 months (possibly with a GitHub Actions cron task)

nd relational datasets into feature matrices for machine learning. The PLoS 10 Simple Rules papers distill a number of best practices: "Ten Simple Rules for Reproducible Computational Research" http://www.ploscompbiol.org/article/info%3Adoi%2F10.1371%2Fj... “Ten Simple Rules for Creating a Good Data Management Plan” http://journals.plos.org/ploscompbiol/article?id=10.1371/jou... In terms of the scientific method, a null hypothesis like "there is no significant relation between the [independent and dependent] variables" may be dangerously unprofessional p-hacking and data dredging; and may result in an overfit model that seems to predict or classify the training and test data (when split with e.g. sklearn.model_selection.train_test_split and a given random seed). One of these days (in the happy new year!) I'll get around to updating these notes with the aforementioned tools and docs: https://wrdrd.github.io/docs/consulting/data-science#scienti... IDK what https://kaggle.com/learn has specifically in terms of analysis workflow? Their docker containers have very many tools configured in a reproducible way: https://github.com/Kaggle/docker-python/blob/master/Dockerfi...