Scaling Data Science in Python Tutorial

Tutorial on Scaling Data Science in Python at Data Science Summit SF 2016 Slides: TODO

Setup

Clone or download the repo

First get local copies of the tutorial notebooks:

$ git clone https://github.com/chdoig/dss-scaling-tutorial.git

Or download from: https://github.com/chdoig/dss-scaling-tutorial/archive/master.zip

Install the dependencies

This tutorial has been tested on:

• bokeh=0.12.0
• pandas=0.18.1
• dask=0.10.0
• datashader=0.3.2
• jupyter=1.0.0

Other combinations may work also. Packages are available via PyPI and anaconda.org.

Installing with Anaconda

Install anaconda

Anaconda should come with all the dependencies included, but you may need to update your versions.

Install with miniconda

Install miniconda.

Use the command line to create an environment and install the packages:

$ conda create -n dss_scaling python=3.4
$ source activate dss_scaling
$ conda install bokeh pandas jupyter dask
$ conda install -c bokeh datashader

Run Jupyter/IPython notebook

From this folder run jupyter notebook, and open the notebooks.

$ jupyter notebook