ny-taxi-data-workspace + Docker

Just a workspace where I will work on some data crunching with dockerized Jupyter notebooks.

HOWTO In 5-minutes

Start it by running this command from root of project folder docker-compose up or docker-compose up -d

Once started, the former command (Docker console output) will tell you the approximate URL you need to login to the Jupyter notebook instance.

Something like this: http://localhost:10000/?token=<<unique-id>>

Once inside, open one of the Notebooks and run one step at a time.

At the end of 2 of the notebooks is a graphing functionn to plot data on a graph.

Source Data

http://www.nyc.gov/html/tlc/html/about/trip_record_data.shtml

Info On Jupyter

https://jupyter-docker-stacks.readthedocs.io/en/latest/using/selecting.html#jupyter-datascience-notebook

What is included here

Using: docker pull jupyter/datascience-notebook

Minimally-functional Jupyter Notebook server (e.g., no pandoc for saving notebooks as PDFs)
Miniconda Python 3.x in /opt/conda
No preinstalled scientific computing packages
Unprivileged user jovyan (uid=1000, configurable, see options) in group users (gid=100) with ownership 
  over the /home/jovyan and /opt/conda paths
tini as the container entrypoint and a start-notebook.sh script as the default command
A start-singleuser.sh script useful for launching containers in JupyterHub
A start.sh script useful for running alternative commands in the container (e.g. ipython, jupyter kernelgateway,
  jupyter lab)
Options for a self-signed HTTPS certificate and passwordless sudo
Pandoc and TeX Live for notebook document conversion
git, emacs, jed, vim, and unzip
The R interpreter and base environment
IRKernel to support R code in Jupyter notebooks
tidyverse packages, including ggplot2, dplyr, tidyr, readr, purrr, tibble, stringr, lubridate, and broom 
  from conda-forge
plyr, devtools, shiny, rmarkdown, forecast, rsqlite, reshape2, nycflights13, caret, rcurl,
  and randomforest packages from conda-forge
pandas, numexpr, matplotlib, scipy, seaborn, scikit-learn, scikit-image, sympy, cython, patsy, statsmodel, 
  cloudpickle, dill, numba, bokeh, sqlalchemy, hdf5, vincent, beautifulsoup, protobuf, and xlrd packages
ipywidgets for interactive visualizations in Python notebooks
Facets for visualizing machine learning datasets
The Julia compiler and base environment
IJulia to support Julia code in Jupyter notebooks
HDF5, Gadfly, and RDatasets packages

NOTES

Run it at scale possibly: https://github.com/jupyterhub/kubespawner