pandalone 0.1.12.dev0

Overview

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…

Quick-start

Assuming that you have a working python-environment, open a command-shell, (in Windows use program(cmd.exe) BUT ensure program(python.exe) is in its envvar(PATH)), try the following commands:

Install:

$ pip install pandalone                 ## Use `--pre` if version-string has a build-suffix.

Or in case you need the very latest from master branch :

$ pip install git+https://github.com/pandalone/pandalone.git

See: doc(install)

Run:

$ pandalone --version

Python installation

As explained above, this project depends on packages with native-backends that require the use of C and Fortran compilers to build from sources. To avoid this hassle, you should choose one of the user-friendly distributions suggested below.

Below is a matrix of the two suggested self-wrapped python distributions for running this program (we excluded here default python included in linux). Both distributions:

• are free (as of freedom),

• do not require admin-rights for installation in Windows, and

• have been tested to run successfully this program (also tested on default linux distros).

Package installation

Before installing it, make sure that there are no older versions left over on the python installation you are using. To cleanly uninstall it, run this command until you cannot find any project installed:

$ pip uninstall pandalone                   ## Use `pip3` if both python-2 & 3 are in PATH.

You can install the project directly from the PyPi repo the “standard” way, by typing the command(pip) in the console:

$ pip install pandalone

• If you want to install a pre-release version (the version-string is not plain numbers, but ends with alpha, beta.2 or something else), use additionally option --pre.

$ pip install pandalone

• Also you can install the very latest version straight from the sources:

  $ pip install git+git://github.com/pandalone/pandalone.git  --pre

• If you want to upgrade an existing installation along with all its dependencies, add also option --upgrade (or option -U equivalently), but then the build might take some considerable time to finish. Also there is the possibility the upgraded libraries might break existing programs(!) so use it with caution, or from within a virtualenv (isolated Python environment).

• To install it for different Python environments, repeat the procedure using the appropriate program(python.exe) interpreter for each environment.

After installation, it is important that you check which version is visible in your envvar(PATH):

$ pndlcmd --version
0.1.12.dev0

To install for different Python versions, repeat the procedure for every required version.

Older versions

To install an older released version issue the console command:

$ pip install pandalone=0.0.1                   ## Use `--pre` if version-string has a build-suffix.

or alternatively straight from the sources:

$ pip install git+https://github.com/pandalone/pandalone.git@v0.0.9-alpha.3.1  --pre

Of course you can substitute v0.0.9-alpha.3.1 with any slug from “commits”, “branches” or “releases” that you will find on project’s github-repo).

Installing sources

If you download the sources you have more options for installation. There are various methods to get hold of them:

• Download the source distribution from PyPi repo.

• Download a release-snapshot from github

• Clone the git-repository at github.

  Assuming you have a working installation of git you can fetch and install the latest version of the project with the following series of commands:

  $ git clone "https://github.com/pandalone/pandalone.git" pandalone.git
  $ cd pandalone.git
  $ python setup.py install                                 ## Use `python3` if both python-2 & 3 installed.

When working with sources, you need to have installed all libraries that the project depends on:

$ pip install -r requirements/execution.pip .

The previous command installs a “snapshot” of the project as it is found in the sources. If you wish to link the project’s sources with your python environment, install the project in development mode:

$ python setup.py develop

Project files and folders

The 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

Cmd-line usage

The command-line usage below requires the Python environment to be installed, and provides for executing an experiment directly from the OS’s shell (i.e. program(cmd) in windows or program(bash) in POSIX), and in a single command.

[TBD]

GUI usage

For a quick-‘n-dirty method to explore the structure of the data-tree and run an experiment, just run:

$ pandalone gui

Excel usage

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 excel

You could type instead pandalone excel {file_path} to specify a different destination path.

[TBD]

Python usage

Example python 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:

code-block:

>>> import pandalone

>>> pandalone.__version__           ## Check version once more.
'0.1.12.dev0'

>>> pandalone.__file__              ## To check where it was installed.         # doctest: +SKIP
/usr/local/lib/site-package/pandalone-...

If everything works, create the 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]

Sources & Dependencies

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.git

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 test

Design

See architecture live-document.

Why another XXX? What about YYY?

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 works on 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 “.

Which other projects/ideas have you reviewed when building this library?

• 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.