PyData Seattle 2015: Python Data Bikeshed
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==126.96.36.199 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 );