Label the rows, columns, any dimension, of your NumPy arrays.

## Project description

The main class of the la package is a labeled array, larry. A larry consists
of data and labels. The data is stored as a NumPy array and the labels as a
list of lists (one list per dimension).

Here's larry in schematic form::

date1 date2 date3
'AAPL' 209.19 207.87 210.11
y = 'IBM' 129.03 130.39 130.55
'DELL' 14.82 15.11 14.94

The larry above is stored internally as a Numpy <http://www.numpy.org>_
array and a list of lists::

y.label = [['AAPL', 'IBM', 'DELL'], [date1, date2, date3]]
y.x = np.array([[209.19, 207.87, 210.11],
[129.03, 130.39, 130.55],
[ 14.82, 15.11, 14.94]])

A larry can have any number of dimensions except zero. Here, for example, is
one way to create a one-dimensional larry::

>>> import la
>>> y = la.larry([1, 2, 3])

In the statement above the list is converted to a Numpy array and the labels
default to range(n), where *n* in this case is 3.

larry has built-in methods such as **ranking, merge, shuffle, move_sum,
zscore, demean, lag** as well as typical Numpy methods like **sum, max, std,
sign, clip**. NaNs are treated as missing data.

Alignment by label is automatic when you add (or subtract, multiply, divide)
two larrys. You can also specify the join method (inner, outer, left, right)
for binary operations on two larrys with unaligned labels.

You can archive larrys in HDF5 format using **save** and **load** or using a
dictionary-like interface::

>>> io = la.IO('/tmp/dataset.hdf5')
>>> io['y'] = y # <--- save
>>> z = io['y'] # <--- load
>>> del io['y'] # <--- delete from archive

For the most part larry acts like a Numpy array. And, whenever you want,
example if you have a function, *myfunc*, that works on Numpy arrays and
doesn't change the shape or ordering of the array, then you can use it on a
larry, *y*, like this::

y.x = myfunc(y.x)

larry adds the convenience of labels, provides many built-in methods, and
let's you use your existing array functions.

=======

The la package is distributed under a Simplified BSD license. Parts of
NumPy, Scipy, and numpydoc, which all have BSD licenses, are included in
la. Parts of matplotlib are also included. See the LICENSE file, which
is distributed with the la package, for details.

Install
=======

Requirements:

======================== ====================================================
la Python, NumPy 1.5.1, Bottleneck 0.4.3
Unit tests nose
======================== ====================================================

Optional:

============================= ================================================
Archive larrys in HDF5 h5py, HDF 1.8
Compile for speed boost gcc or MinGW
lar.ranking(norm='gaussian') SciPy 0.8.0
============================= ================================================

You can download the latest version of la <http://pypi.python.org/pypi/la>_
and Bottleneck <http://pypi.python.org/pypi/Bottleneck>_ from the Python
Package Index.

**GNU/Linux, Mac OS X et al.**

To install la::

$python setup.py build$ sudo python setup.py install

Or, if you wish to specify where la is installed, for example inside
/usr/local::

$python setup.py build$ sudo python setup.py install --prefix=/usr/local

**Windows**

In order to (optionally) compile the C code in the la package you need a
Windows version of the gcc compiler. MinGW (Minimalist GNU for Windows)
contains gcc and has been used to successfully compile la on Windows.

Install MinGW and add it to your system path. Then install la with the
commands::

python setup.py build --compiler=mingw32
python setup.py install

**Post install**

After you have installed la, run the suite of unit tests::

>>> import la
>>> la.test()
<snip>
Ran 2975 tests in 2.017s
OK
<nose.result.TextTestResult run=2975 errors=0 failures=0>

The la package contains C extensions that speed up common alignment
operations such as adding two unaligned larrys. If the C extensions don't
compile when you build la then there's an automatic fallback to python
versions of the functions. To see whether you are using the C functions or the
Python functions::

>>> la.info()
la version 0.5.0
la file /usr/local/lib/python2.6/dist-packages/la/__init__.pyc
NumPy 1.5.1
Bottleneck 0.4.2
HDF5 archiving Available (h5py 1.3.0)
listmap Faster C version
listmap_fill Faster C version

Since la can run in a pure python mode, you can use la by just saving
it and making sure that python can find it.

URLs
====

=============== ========================================================
docs http://berkeleyanalytics.com/la
code https://github.com/kwgoodman/la
issue tracker https://github.com/kwgoodman/la/issues
=============== ========================================================

============================================================================================
This is an old version. Click here <http://pypi.python.org/pypi/la>_ for latest version
============================================================================================

## Project details

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