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NumPy and Pandas interface to Big Data
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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.


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.

>>> 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.)

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

>>>, 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:

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

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

From the representation of data

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

Blaze enables users to solve data-oriented problems

>>> 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.

>>> 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 --upgrade

You may want to view the docs, the tutorial, some blogposts, or the mailing list archives.

Development setup

The quickest way to install all Blaze dependencies with conda is as follows

conda install blaze spark dynd-python -c libdynd -c blaze -c anaconda-cluster -y
conda remove odo blaze blaze-core datashape -y

After running these commands, clone odo, blaze, and datashape from GitHub directly. These three projects release together. Run python develop to make development installations of each.


Released under BSD license. See LICENSE.txt for details.

Blaze development is sponsored by Continuum Analytics.

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