pydataseattle2015

by wrobstory

PyData Seattle 2015: Python Data Bikeshed

127 Stars 16 Forks Last release: Not found 17 Commits 0 Releases

Available items

No Items, yet!

The developer of this repository has not created any items for sale yet. Need a bug fixed? Help with integration? A different license? Create a request here:

PyData Seattle 2015: Python Data Bikeshed

3508571577_1a633e7bc4_z

This repo contains the slides, data, and Jupyter Notebook for the PyData Seattle 2015 talk Python Data Bikeshed. The goal of the talk is to help answer the following question:

I have data. It’s July 2015. I want to group things. or count things. or average things. or add things.

What Python library do I use?

This talk discusses the following libraries in some depth:

  • Toolz: https://github.com/pytoolz/toolz
  • Pandas: https://github.com/pydata/pandas
  • Blaze: https://github.com/ContinuumIO/blaze
  • xray: https://github.com/xray/xray
  • bcolz: https://github.com/Blosc/bcolz
  • Dask: https://github.com/ContinuumIO/dask

The following libraries or projects are briefly mentioned:

  • Cython: https://github.com/cython/cython
  • Numexpr: https://github.com/pydata/numexpr
  • Numba: https://github.com/numba/numba
  • Numpy: https://github.com/numpy/numpy
  • Spark: http://spark.apache.org/
  • Bolt: http://bolt-project.org/
  • Dato SArray: https://dato.com/products/create/docs/generated/graphlab.SArray.html
  • Dato SFrame: https://dato.com/products/create/docs/generated/graphlab.SFrame.html#graphlab.SFrame
  • DistArray: https://github.com/enthought/distarray
  • Biggus: https://github.com/SciTools/biggus
  • Spartan: https://github.com/spartan-array/spartan
  • Ibis: https://github.com/cloudera/ibis
  • scikit-learn: https://github.com/scikit-learn/scikit-learn
  • statsmodels: https://github.com/statsmodels/statsmodels/
  • Bokeh: https://github.com/bokeh/bokeh
  • seaborn: https://github.com/mwaskom/seaborn

There is an example Notebook that goes into much more detail on the core libraries discussed in the talk, with plenty of examples; it can be read directly in Github, or on nbviewer.

If you would like to run the example yourself, you will need to either pip or conda install the follow dependencies:

Babel==1.3
Cython==0.22.1
Jinja2==2.7.3
MarkupSafe==0.23
Pygments==2.0.2
SQLAlchemy==1.0.7
Sphinx==1.3.1
alabaster==0.7.6
backports.ssl-match-hostname==3.4.0.2
bcolz==0.10.0
blaze==0.8.2
certifi==2015.04.28
dask==0.6.0
datashape==0.4.6
decorator==3.4.2
dill==0.2.3
docutils==0.12
functools32==3.2.3-2
gnureadline==6.3.3
ipython==3.2.1
jsonschema==2.5.1
mistune==0.7
multipledispatch==0.4.8
networkx==1.9.1
nose==1.3.7
numexpr==2.4.3
numpy==1.9.2
numpydoc==0.5
odo==0.3.3
pandas==0.16.2
psutil==3.1.1
psycopg2==2.6.1
ptyprocess==0.5
python-dateutil==2.4.2
pytz==2015.4
pyzmq==14.7.0
requests==2.7.0
six==1.9.0
snowballstemmer==1.2.0
sphinx-rtd-theme==0.1.8
terminado==0.5
toolz==0.7.2
tornado==4.2.1
wsgiref==0.1.2
xray==0.5.2

The Blaze demo also requires installing Postgres, creating a

pydata
database, and populating a
diamonds
table with the correct schema. Postgres can be reliably installed with homebrew or conda, and the table created with the following SQL:
CREATE TABLE diamonds (
    carat     float,
    cut       varchar(255),
    color     varchar(255),
    clarity   varchar(255),
    depth     float, 
    "table"   float,  
    price     integer,
    x         float, 
    y         float, 
    z         float  
);

We use cookies. If you continue to browse the site, you agree to the use of cookies. For more information on our use of cookies please see our Privacy Policy.