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datashell-0.4.4
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Because namespaces are a honking great idea, but loading a gazillion packages to take a quick peek at some data is not.
| ویژگی | مقدار |
|---|---|
| سیستم عامل | - |
| نام فایل | datashell-0.4.4 |
| نام | datashell |
| نسخه کتابخانه | 0.4.4 |
| نگهدارنده | [] |
| ایمیل نگهدارنده | [] |
| نویسنده | Stijn Debrouwere |
| ایمیل نویسنده | stijn@debrouwere.org |
| آدرس صفحه اصلی | https://github.com/debrouwere/datashell/ |
| آدرس اینترنتی | https://pypi.org/project/datashell/ |
| مجوز | ISC |
Data Shell
==========
If you have a statistics background, you're probably familiar with R.
And if you've been using R for a while, the thing you start to
appreciate most is that once you have that terminal open, you can get
down to business pretty much immediately. Dumping functionality as
diverse as numerical optimization, linear regression and the cumulative
distribution function for a Poisson distribution all into the global
namespace is *probably* not a good idea, but boy is it useful for quick
data exploration. That's what Data Shell does for Python.
Install with
.. code:: sh
pip3 install datashell
datashell-install
Open up an IPython-based data shell for Python 3 by typing ``datashell``
into your terminal. For inline plotting, use ``datashell-qt`` instead.
*Pro tip:* alias these shells to something shorter. For example, put
``alias dash=datashell`` and ``alias dashi=datashell-qt`` into your
``~/.bashrc`` or wherever your shell customizations live.
Convenience functions
---------------------
Currently, it loads convenience functions from ``math``, ``random``,
``numpy``, ``scipy.stats``, ``statsmodels``, ``sympy`` as well as
``pandas``.
All functions are lazy-loaded, so startup time is not much different
than a regular IPython terminal.
Data shell does a star import of various packages into the global
namespace, but also keeps them available under their respective
namespaces, so you can access functionality both ways.
To give just one example, once you're in your IPython data shell, a
linear regression on a dataset in your working directory is simply:
.. code:: python
ols('y ~ x', data=tables.test).fit().summary()
Behind the scenes, this will load ``statsmodels.formulas.api.ols`` to
perform a linear regression, and ``tables.test`` will load ``test.csv``.
Datashell can also be used in (non-interactive) scripts:
.. code:: python
from datashell import *
diff(2*x**2)
(Though at some point you'll probably want to clean things up and do
proper imports.)
Data autoloader
---------------
Data shell also includes a Pandas autoloader for CSV files: you can
access a Pandas DataFrame of ``./subdir/myfile.csv`` from
``tables.subdir.myfile``.
Useful shortcuts
----------------
- from ``math``: ``ceil``, ``floor``, ``log``, ``factorial``, ``sin``
and pretty much anything you'd find on a good calculator
- from ``random``: ``shuffle``, ``choice``, ``sample`` and friends
- from ``sympy``: ``expand``, ``factor``, ``simplify`` to simplify
mathematical expressions, ``diff`` to differentiate, ``integrate`` to
integrate (many one-letter variables are also predefined: a-e, o-s
and u-z)
- from ``scipy.optimize``: ``minimize``
- from ``scipy.stats``: ``describe``, ``itemfreq``, ``relfreq``,
``kurtosis``, ``mode``, ``moment``, ``skew``, ``pearsonr``,
``spearmanr`` and others
- from ``scipy.stats.contingency``: ``expected_freq``, ``margins``
- from ``scipy.stats.distributions``: ``cdf``, ``pdf``, ``ppf``,
``sf``, ``rvs`` and various other functions on statistical
distributions from normal to gamma
- from ``statsmodels.api``: ``datasets`` and ``families`` (for use with
generalized linear models)
- from ``statsmodels.formula.api``: ``ols`` and ``gls``
نحوه نصب
نصب پکیج whl datashell-0.4.4:
pip install datashell-0.4.4.whl
نصب پکیج tar.gz datashell-0.4.4:
pip install datashell-0.4.4.tar.gz