PyData Seattle 2015: Python Data Bikeshed
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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:
The following libraries or projects are briefly mentioned:
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==22.214.171.124 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
pydatadatabase, and populating a
diamondstable 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 );