pandalone is a collection of utilities for working with hierarchical-data using relocatable-paths.
| Release: | 0.5.0 |
|---|---|
| Date: | 2020-05-14 23:59 |
| Documentation: | https://pandalone.readthedocs.org/ |
| Source: | https://github.com/pandalone/pandalone |
| PyPI repo: | https://pypi.python.org/pypi/pandalone |
| Keywords: | calculation, data, dependencies, engineering, excel, library, numpy, pandas, processing, python, resolution, scientific, simulink, tree, utility |
| Copyright: | 2015 European Commission (JRC-IET) |
| License: | EUPL 1.1+ |
Currently only 2 portions of the envisioned functionality are ready for use:
- :mod:`pandalone.xleash`: A mini-language for "throwing the rope" around rectangular areas of Excel-sheets.
- :mod:`pandalone.mappings`: Hierarchical string-like objects that may be used for indexing, facilitating renaming keys and column-names at a later stage.
Our goal is to facilitate the composition of engineering-models from loosely-coupled components. Initially envisioned as an indirection-framework around pandas coupled with a dependency-resolver, every such model should auto-adapt and process only values available, and allow remapping of the paths accessing them, to run on renamed/relocated value-trees without component-code modifications.
It is an open source library written and tested on Python-3.6+ , Windows and Linux.
Note
The project, as of May-2015, is considered at an alpha-stage, without any released version in pypi yet.
Table of Contents
At the most fundamental level, an "execution" or a "run" of any data-processing can be thought like that:
.--------------. _____________ .-------------.
; DataTree ; | | ; DataTree ;
;--------------; ==> | <cfunc_1> | ==> ;--------------;
; /some/data ; | <cfunc_2> | ; /some/data ;
; /some/other ; | ... | ; /some/other ;
; /foo/bar ; |_____________| ; /foo/bar ;
'--------------' '--------------.
The data-tree might come from json, hdf5, excel-workbooks, or plain dictionaries and lists. Its values are strings and numbers, numpy-lists, pandas or xray-datasets, etc.
The component-functions must abide to the following simple signature:
cfunc_do_something(pandelone, datatree)
and must not return any value, just read and write into the data-tree.
Here is a simple component-function:
def cfunc_standardize(pandelone, datatree): pin, pon = pandelone.paths(), df = datatree.get(pin.A) df[pon.A.B_std] = df[pin.A.B] / df[pin.A.B].std()
Notice the use of the relocatable-paths marked specifically as input or output.
TODO: continue rough example in tutorial...
The program runs on Python-3.5+ and requires numpy, pandas and (optionally) win32 libraries along with their native backends.
pip install pandalone ## Use `--pre` if version-string has a build-suffix.... but probably you need the following for xleash to work:
pip install pandalone[xlrd]All "extras" are: test, doc, excel, pandas, xlrd, dev, all
In case you need the very latest from master branch :
pip install git+https://github.com/pandalone/pandalone.gitOr in to install in develop mode, with all dependencies needed for development, and with pre-commit hook for auto-formatting python-code with black, clone locally this project from the remote repo, and run:
pip install -e <pandalone-dr>[dev]
pre-commit installThe files and folders of the project are listed below:
+--pandalone/ ## (package) Python-code +--tests/ ## (package) Test-cases +--doc/ ## Documentation folder +--setup.py ## (script) The entry point for `setuptools`, installing, testing, etc +--requirements/ ## (txt-files) Various pip and conda dependencies. +--README.rst +--CHANGES.rst +--AUTHORS.rst +--CONTRIBUTING.rst +--LICENSE.txt
Currently 2 portions of this library are ready for use: :mod:`pandalone.xleash` and :mod:`pandalone.mappings`
Attention!
Desktop UI requires Python 3!
For a quick-'n-dirty method to explore the structure of the data-tree and run an experiment, just run:
$ pandalone guiAttention!
Excel-integration requires Python-3 and Windows or OS X!
In Windows and OS X you may utilize the excellent xlwings library to use Excel files for providing input and output to the experiment.
To create the necessary template-files in your current-directory you should enter:
$ pandalone excelYou could type instead :samp:`pandalone excel {file_path}` to specify a different destination path.
[TBD]
Example python :abbr:`REPL (Read-Eval-Print Loop)` example-commands are given below that setup and run an experiment.
First run :command:`python` or :command:`ipython` and try to import the project to check its version:
>>> import pandalone
>>> pandalone.__version__ ## Check version once more.
'0.5.0'
>>> pandalone.__file__ ## To check where it was installed. # doctest: +SKIP
/usr/local/lib/site-package/pandalone-...If everything works, create the :term:`data-tree` to hold the input-data (strings and numbers). You assemble data-tree by the use of:
- sequences,
- dictionaries,
- :class:`pandas.DataFrame`,
- :class:`pandas.Series`, and
- URI-references to other data-trees.
[TBD]
This project is hosted in github. To provide feedback about bugs and errors or questions and requests for enhancements, use github's Issue-tracker.
To get involved with development, you need a POSIX environment to fully build it (Linux, OSX or Cygwin on Windows).
First you need to download the latest sources:
$ git clone https://github.com/pandalone/pandalone.git pandalone.git
$ cd pandalone.gitVirtualenv
You may choose to work in a virtualenv (isolated Python environment), to install dependency libraries isolated from system's ones, and/or without admin-rights (this is recommended for Linux/Mac OS).
Attention!
If you decide to reuse stystem-installed packages using option --system-site-packages
with virtualenv <= 1.11.6
(to avoid, for instance, having to reinstall numpy and pandas that require native-libraries)
you may be bitten by bug #461 which
prevents you from upgrading any of the pre-installed packages with :command:`pip`.
Liclipse IDE
Within the sources there are two sample files for the comprehensive LiClipse IDE:
Remove the eclipse prefix, (but leave the dot(.)) and import it as "existing project" from Eclipse's File menu.
Another issue is caused due to the fact that LiClipse contains its own implementation of Git, EGit, which badly interacts with unix symbolic-links, such as the :file:`docs/docs`, and it detects working-directory changes even after a fresh checkout. To workaround this, Right-click on the above file :menuselection:`Properties --> Team --> Advanced --> Assume Unchanged`
Then you can install all project's dependencies in `development mode using the :file:`setup.py` script:
$ python setup.py --help ## Get help for this script.
Common commands: (see '--help-commands' for more)
setup.py build will build the package underneath 'build/'
setup.py install will install the package
Global options:
--verbose (-v) run verbosely (default)
--quiet (-q) run quietly (turns verbosity off)
--dry-run (-n) don't actually do anything
...
$ python setup.py develop ## Also installs dependencies into project's folder.
$ python setup.py build ## Check that the project indeed builds ok.You should now run the test-cases to check that the sources are in good shape:
$ python setup.py testNote
The above commands installed the dependencies inside the project folder and for the virtual-environment. That is why all build and testing actions have to go through :samp:`python setup.py {some_cmd}`.
If you are dealing with installation problems and/or you want to permantly install dependant packages, you have to deactivate the virtual-environment and start installing them into your base python environment:
$ deactivate
$ python setup.py developor even try the more permanent installation-mode:
$ python setup.py install # May require admin-rightsSee architecture live-document.
These are the knowingly related python projects:
- OpenMDAO: It has influenced pandalone's design. It is planned to interoperate by converting to and from it's data-types. But it is Python-2 only and its architecture needs attending from programmers (no setup.py, no official test-cases).
- PyDSTool: It does not overlap, since it does not cover IO and dependencies of data. Also planned to interoperate with it (as soon as we have a better grasp of it :-). It has some issues with the documentation, but they are working on it.
- xray: Pandas for higher dimensions; data-trees should in principle work with "xray".
- Blaze: NumPy and Pandas interface to Big Data; data-trees should in principle work with "blaze".
- netCDF4: Hierarchical file-data-format similar to hdf5; a data-tree may derive in principle from "netCDF4 ".
- hdf5: Hierarchical file-data-format, supported natively by pandas; a data-tree may derive in principle from "netCDF4 ".
- bubbles ETL: Processing-pipelines for (mostly) categorical data.
- Data-protocols:
- JTSKit, A utility library for working with JSON Table Schema in Python.
- Data Packages
- Celery: Execute distributed asynchronous tasks using message passing on a single or more worker servers using multiprocessing, Eventlet, or gevent.
- Fuzzywuzzy and Jellyfish: Fuzzy string matching in python. Use it for writting code that can read coarsely-known column-names.
- "Other's people's messy data (and how not to hate it)", PyCon 2015(Canada) presentation by Mali Akmanalp.
.. glossary::
data-tree
The *container* of data consumed and produced by a :term`model`, which
may contain also the model.
Its values are accessed using :term:`path` s.
It is implemented by :class:`pandalone.pandata.Pandel` as
a mergeable stack of :term:`JSON-schema` abiding trees of strings and
numbers, formed with:
- sequences,
- dictionaries,
- :mod:`pandas` instances, and
- URI-references.
value-tree
That part of the :term:`data-tree` that relates only to the I/O data
processed.
model
A collection of :term:`component` s and accompanying :term:`mappings`.
component
Encapsulates a data-transformation function, using :term:`path`
to refer to its inputs/outputs within the :term:`value-tree`.
path
A `/file/like` string functioning as the *id* of data-values
in the :term:`data-tree`.
It is composed of :term:`step`, and it follows the syntax of
the :term:`JSON-pointer`.
step
pstep
path-step
The parts between between two conjecutive slashes(`/`) within
a :term:`path`. The :class:`Pstep` facilitates their manipulation.
pmod
pmods
pmods-hierarchy
mapping
mappings
Specifies a transformation of an "origin" path to
a "destination" one (also called as "from" and "to" paths).
The mapping always transforms the *final* path-step, and it can
either *rename* or *relocate* that step, like that::
ORIGIN DESTINATION RESULT_PATH
------ ----------- -----------
/rename/path foo --> /rename/foo ## renaming
/relocate/path foo/bar --> /relocate/foo/bar ## relocation
/root a/b/c --> /a/b/c ## Relocates all /root sub-paths.
The hierarchy is formed by :class:`Pmod` instances,
which are build when parsing the :term:`mappings` list, above.
JSON-schema
The `JSON schema <http://json-schema.org/>`_ is an `IETF draft
<http://tools.ietf.org/html/draft-zyp-json-schema-03>`_
that provides a *contract* for what JSON-data is required for
a given application and how to interact with it.
JSON Schema is intended to define validation, documentation,
hyperlink navigation, and interaction control of JSON data.
You can learn more about it from this `excellent guide
<http://spacetelescope.github.io/understanding-json-schema/>`_,
and experiment with this `on-line validator <http://www.jsonschema.net/>`_.
JSON-pointer
JSON Pointer(:rfc:`6901`) defines a string syntax for identifying
a specific value within a JavaScript Object Notation (JSON) document.
It aims to serve the same purpose as *XPath* from the XML world,
but it is much simpler.