{ "info": { "author": "Stijn Debrouwere", "author_email": "stijn@debrouwere.org", "bugtrack_url": null, "classifiers": [ "Development Status :: 4 - Beta", "Intended Audience :: Developers", "License :: OSI Approved :: ISC License (ISCL)", "Operating System :: OS Independent", "Programming Language :: Python", "Programming Language :: Python :: 3", "Programming Language :: Python :: 3.4", "Topic :: Scientific/Engineering :: Information Analysis" ], "description": "Data Shell\n==========\n\nIf you have a statistics background, you're probably familiar with R.\nAnd if you've been using R for a while, the thing you start to\nappreciate most is that once you have that terminal open, you can get\ndown to business pretty much immediately. Dumping functionality as\ndiverse as numerical optimization, linear regression and the cumulative\ndistribution function for a Poisson distribution all into the global\nnamespace is *probably* not a good idea, but boy is it useful for quick\ndata exploration. That's what Data Shell does for Python.\n\nInstall with\n\n.. code:: sh\n\n pip3 install datashell\n datashell-install\n\nOpen up an IPython-based data shell for Python 3 by typing ``datashell``\ninto your terminal. For inline plotting, use ``datashell-qt`` instead.\n\n*Pro tip:* alias these shells to something shorter. For example, put\n``alias dash=datashell`` and ``alias dashi=datashell-qt`` into your\n``~/.bashrc`` or wherever your shell customizations live.\n\nConvenience functions\n---------------------\n\nCurrently, it loads convenience functions from ``math``, ``random``,\n``numpy``, ``scipy.stats``, ``statsmodels``, ``sympy`` as well as\n``pandas``.\n\nAll functions are lazy-loaded, so startup time is not much different\nthan a regular IPython terminal.\n\nData shell does a star import of various packages into the global\nnamespace, but also keeps them available under their respective\nnamespaces, so you can access functionality both ways.\n\nTo give just one example, once you're in your IPython data shell, a\nlinear regression on a dataset in your working directory is simply:\n\n.. code:: python\n\n ols('y ~ x', data=tables.test).fit().summary()\n\nBehind the scenes, this will load ``statsmodels.formulas.api.ols`` to\nperform a linear regression, and ``tables.test`` will load ``test.csv``.\n\nDatashell can also be used in (non-interactive) scripts:\n\n.. code:: python\n\n from datashell import *\n diff(2*x**2)\n\n(Though at some point you'll probably want to clean things up and do\nproper imports.)\n\nData autoloader\n---------------\n\nData shell also includes a Pandas autoloader for CSV files: you can\naccess a Pandas DataFrame of ``./subdir/myfile.csv`` from\n``tables.subdir.myfile``.\n\nUseful shortcuts\n----------------\n\n- from ``math``: ``ceil``, ``floor``, ``log``, ``factorial``, ``sin``\n and pretty much anything you'd find on a good calculator\n- from ``random``: ``shuffle``, ``choice``, ``sample`` and friends\n- from ``sympy``: ``expand``, ``factor``, ``simplify`` to simplify\n mathematical expressions, ``diff`` to differentiate, ``integrate`` to\n integrate (many one-letter variables are also predefined: a-e, o-s\n and u-z)\n- from ``scipy.optimize``: ``minimize``\n- from ``scipy.stats``: ``describe``, ``itemfreq``, ``relfreq``,\n ``kurtosis``, ``mode``, ``moment``, ``skew``, ``pearsonr``,\n ``spearmanr`` and others\n- from ``scipy.stats.contingency``: ``expected_freq``, ``margins``\n- from ``scipy.stats.distributions``: ``cdf``, ``pdf``, ``ppf``,\n ``sf``, ``rvs`` and various other functions on statistical\n distributions from normal to gamma\n- from ``statsmodels.api``: ``datasets`` and ``families`` (for use with\n generalized linear models)\n- from ``statsmodels.formula.api``: ``ols`` and ``gls``", "description_content_type": null, "docs_url": null, "download_url": "http://www.github.com/debrouwere/datashell/tarball/master", "downloads": { "last_day": -1, "last_month": -1, "last_week": -1 }, "home_page": "https://github.com/debrouwere/datashell/", "keywords": "data analytics statistics", "license": "ISC", "maintainer": null, "maintainer_email": null, "name": "datashell", "package_url": "https://pypi.org/project/datashell/", "platform": "UNKNOWN", "project_url": "https://pypi.org/project/datashell/", "project_urls": { 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