blaze 0.7.3

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<p align="center" style="padding: 20px">
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**Blaze** translates a subset of modified NumPy and Pandas-like syntax to
databases and other computing systems. Blaze allows Python users a familiar
interface to query data living in other data storage systems.


Example
-------

We point blaze to a simple dataset in a foreign database (PostgreSQL).
Instantly we see results as we would see them in a Pandas DataFrame.

```Python
>>> import blaze as bz
>>> iris = bz.Data('postgresql://localhost::iris')
>>> iris
sepal_length sepal_width petal_length petal_width species
0 5.1 3.5 1.4 0.2 Iris-setosa
1 4.9 3.0 1.4 0.2 Iris-setosa
2 4.7 3.2 1.3 0.2 Iris-setosa
3 4.6 3.1 1.5 0.2 Iris-setosa
```

These results occur immediately. Blaze does not pull data out of Postgres,
instead it translates your Python commands into SQL (or others.)

```Python
>>> iris.species.distinct()
species
0 Iris-setosa
1 Iris-versicolor
2 Iris-virginica

>>> bz.by(iris.species, smallest=iris.petal_length.min(),
... largest=iris.petal_length.max())
species largest smallest
0 Iris-setosa 1.9 1.0
1 Iris-versicolor 5.1 3.0
2 Iris-virginica 6.9 4.5
```

This same example would have worked with a wide range of databases, on-disk text
or binary files, or remote data.


What Blaze is not
-----------------

Blaze does not perform computation. It relies on other systems like SQL,
Spark, or Pandas to do the actual number crunching. It is not a replacement
for any of these systems.

Blaze does not implement the entire NumPy/Pandas API, nor does it interact with
libraries intended to work with NumPy/Pandas. This is the cost of using more
and larger data systems.

Blaze is a good way to inspect data living in a large database, perform a small
but powerful set of operations to query that data, and then transform your
results into a format suitable for your favorite Python tools.


In the Abstract
---------------

Blaze separates the computations that we want to perform:

```Python
>>> accounts = Symbol('accounts', 'var * {id: int, name: string, amount: int}')

>>> deadbeats = accounts[accounts.amount < 0].name
```

From the representation of data

```Python
>>> L = [[1, 'Alice', 100],
... [2, 'Bob', -200],
... [3, 'Charlie', 300],
... [4, 'Denis', 400],
... [5, 'Edith', -500]]
```

Blaze enables users to solve data-oriented problems

```Python
>>> list(compute(deadbeats, L))
['Bob', 'Edith']
```

But the separation of expression from data allows us to switch between
different backends.

Here we solve the same problem using Pandas instead of Pure Python.

```Python
>>> df = DataFrame(L, columns=['id', 'name', 'amount'])

>>> compute(deadbeats, df)
1 Bob
4 Edith
Name: name, dtype: object
```

Blaze doesn't compute these results, Blaze intelligently drives other projects
to compute them instead. These projects range from simple Pure Python
iterators to powerful distributed Spark clusters. Blaze is built to be
extended to new systems as they evolve.


Getting Started
---------------

Blaze is available on conda or on PyPI

conda install blaze
pip install blaze

Development builds are accessible

conda install blaze -c blaze
pip install http://github.com/ContinuumIO/blaze --upgrade

You may want to view [the docs](http://blaze.pydata.org), [the
tutorial](http://github.com/ContinuumIO/blaze-tutorial), or [some
blogposts](http://http://continuum.io/blog/tags/blaze).


License
-------

Released under BSD license. See [LICENSE.txt](LICENSE.txt) for details.

Blaze development is sponsored by Continuum Analytics.