Notebooks offered in Noteable
Here are all the Notebook Servers with the respective primary libraries listed, which are currently available in Noteable.
These are not a comprehensive lists, however it does list the libraries and extensions we have specifically chosen to install.
Hover over the library name for a brief description of that library, and the verson installed. Each library should also link you to documentation for that library.
Standard Notebook
Standard Notebook is the default python 3 notebook, with a wide selection of libraries includes.
 Based on the jupyter/minimalnotebook image.
 Includes nbgrader and +GitRepo tools for document sharing.
 Provide rubberband, exercise, and exercise2 extensions. Note these are not related to nbgrader in any way.
 Enables delete directories with content.
 Data Access libraries: beautifulsoup4, dill, libxml2, lxml, protobuf, sqlalchemy, textblob, unixodbc, and xlrd
 Data Science libraries: pandas and dask
 Data Visualization tools: bokeh, graphviz, ipywidgets, ipympl, k3d, mpld3, plotly, seaborn, widgetsnbextension, and wordcloud
 Image Processing: imagemagick, opencv, scikitimage, and spectral
 Machine Learning libraries: scikitlearn
 Stats & Data Modelling libraries: h5py, hdf5, numexpr, numpy, pytables, quantecon, scipy, statsmodels, and sympy
 Other libraries include: cloudpickle, cython, ffmpeg, netcdf4, nltk, numba, patsy, rise, vega, and vincent
SageMath Notebook
The Sagemath Notebook provides Sagemath 9 in python 3 notebook, with a wide selection of libraries included.
 Based on the sagemath/sagemath image.
 Includes nbgrader and +GitRepo tools for document sharing.
 Provide rubberband, exercise, and exercise2 extensions. Note these are not related to nbgrader in any way.
 Enables delete directories with content.
 Data Access libraries: beautifulsoup4, dill, libxml2, lxml, protobuf, sqlalchemy, textblob, unixodbc, and xlrd
 Data Science libraries: pandas and dask
 Data Visualization tools: bokeh, graphviz, ipywidgets, ipympl, k3d, mpld3, plotly, seaborn, widgetsnbextension, and wordcloud
 Image Processing: imagemagick, opencv, scikitimage, and spectral
 Machine Learning libraries: scikitlearn
 Stats & Data Modelling libraries: h5py, hdf5, numexpr, numpy, pytables, scipy, statsmodels, and sympy
 Other libraries include: cloudpickle, cython, ffmpeg, netcdf4, nltk, numba, patsy, rise, vega, and vincent
RStudio Notebook
The RStudio UI overlayed on a Jupyter notebook, with an extensive selection of additional libraries included.
 Based on the jupyter/rnotebook image.
 A personal & individual RStudio server providing the RStudio IDE
 Data Manipulation in R: rarm and rdata.table
 Some open Source data sets: rnhanes, rnycflights13, and rukbabynames
 Tools for profiling code: rbench, rdevtools, rlintr, rmicrobenchmark, rprofvis, rstyler, rtestthat, and rusethis
 Document and article writing in R: rblogdown, rbookdown, rflexdashboard, rreprex, rrmarkdown, rrticles, and rxaringan
 GeoSpatial tools in R: rggally, rggforce, rggmap, rleaflet, rleaflet.extras, rmapedit, rmapview, rrgdal, rrgeos, rsf, rspdep, and rstars
 Visualisations & graphics uner R: rcowplot, rdiagrammer, rfields, rforecast, rgganimate, rggplot2, rggraph, rigraph, rmagick, rplotrix, rvcd, and rviridis
 A selection of RLibraries: rbestglm, rboot, rbrms, rcar, rcaret, rcoda, rcrayon, rdbi, rdomc, rdoparallel, rdrat, rforeach, rforeign, rfs, rfurrr, rfuture, rgbm, rgh, rglmnet, rglue, rgridextra, rhere, rhexbin, rhtmltools, rhtmlwidgets, rhttr, rjaneaustenr, rjsonlite, rkableextra, rkeras, rknitr, rrlang, rlars, rlasso2, rlme4, rlobstr, rloo, rlubridate, rmcmcpack, rmgcv, rnlme, ropenxlsx, rpkgdown, rplyr, rpool, rpromises, rquanteda, rr2jags, rrandomforest, rrcpp, rrcpparmadillo, rrcppeigen, rrcurl, rreadxl, rreshape2, rrjags, rrocr, rrodbc, rroxygen2, rrsqlite, rrvest, rsessioninfo, rshiny, rshinydashboard, rshinyjs, rshinythemes, rsmoothmest, rstargazer, rtensorflow, rtidybayes, rtidygraph, rtidymodels, rtidyverse, rtopicmodels, rtree, rxml, rxml2, rxmlschema, rxtable, and rpy2
 Data Access libraries: beautifulsoup4, dill, libxml2, lxml, protobuf, sqlalchemy, textblob, unixodbc, xlrd, and xmlschema
 Data Science libraries: pandas and dask
 Data Visualization tools: bokeh, graphviz, ipywidgets, ipympl, k3d, mpld3, plotly, seaborn, widgetsnbextension, and wordcloud
 Image Processing: imagemagick, opencv, scikitimage, and spectral
 Machine Learning libraries: scikitlearn
 Stats & Data Modelling libraries: h5py, hdf5, numexpr, numpy, pytables, quantecon, scipy, statsmodels, and sympy
 Other libraries include: cloudpickle, cython, ffmpeg, netcdf4, nltk, numba, patsy, rise, vega, and vincent
R with Stan Notebook
The R + Stan notebook adds Stan packages to R, with a wide selection of additional libraries included.
 Based on the jupyter/rnotebook image.
 Includes nbgrader and +GitRepo tools for document sharing.
 Provide rubberband, exercise, and exercise2 extensions. Note these are not related to nbgrader in any way.
 Enables delete directories with content.
 The following R libraries from cran.rstudio.com: alr3, greta, monomvn, repurrrsive, and spBayes
 A selection of RLibraries: rbase, rcaret, rcrayon, rdevtools, rforecast, rhexbin, rhtmltools, rhtmlwidgets, rlobstr, rnycflights13, ropenxlsx, rplyr, rquanteda, rrandomforest, rrcurl, rreadxl, rreshape2, rrlang, rrmarkdown, rrodbc, rrsqlite, rshiny, rtidyverse, rtopicmodels, rukbabynames, rxml, rxml2, rxmlschema, and rpy2
 Some Stanspecific libraries: rbart, rbas, rbayesplot, rbayestree, rboa, rbrms, rcoda, rcodetools, rdplyr, reffects, rggplot2, rirdisplay, rislr, rlme4, rloo, rmcmcpack, rnhanes, rnlme, ropenintro, rplotmcmc, rquanteda, rrepr, rrstan, rrstanarm, and rxgboost
 Data Access libraries: beautifulsoup4, dill, libxml2, lxml, protobuf, sqlalchemy, textblob, unixodbc, xlrd, and xmlschema
 Data Science libraries: pandas and dask
 Data Visualization tools: bokeh, graphviz, ipywidgets, ipympl, k3d, mpld3, plotly, seaborn, widgetsnbextension, and wordcloud
 Image Processing: imagemagick, opencv, scikitimage, and spectral
 Machine Learning libraries: scikitlearn
 Stats & Data Modelling libraries: h5py, hdf5, numexpr, numpy, pytables, quantecon, scipy, statsmodels, and sympy
 Other libraries include: cloudpickle, cython, ffmpeg, netcdf4, nltk, numba, patsy, rise, vega, and vincent
Python2 Notebook
The Python 2 notebook provides a selection of libraries under the python 2 environment. It is not actively maintained.
 Python 2 is no longer officially supported by the Python community and was removed from the Jupyter notebook images in summer 2019.
 Based on an old release of jupyter/minimalnotebook image.
 Provide rubberband, exercise, and exercise2 extensions. Note these are not related to nbgrader in any way.
 Enables delete directories with content.
 Data Access libraries: beautifulsoup4, dill, sqlalchemy, and xlrd
 Data Science libraries: pandas
 Data Visualization tools: bokeh, ipywidgets, matplotlib, mpld3, and seaborn
 for Image Processing: scikitimage and spectral
 Machine Learning libraries: scikitlearn and tensorflow
 Stats & Data Modelling libraries: h5py, hdf5, numexpr, numpy, scipy, statsmodels, and sympy
 Other libraries include: cloudpickle, cython, netcdf4, nltk, numba, patsy, rise, and vincent
Language and Machine Learning
The Language and Machine Learning Notebook is a specialist python 3 notebook, with a selection of libraries that focus on Natural Language Processing and Machine Learning.
 Based on the jupyter/tensorflownotebook image.
 Includes nbgrader and +GitRepo tools for document sharing.
 Provide rubberband, exercise, and exercise2 extensions. Note these are not related to nbgrader in any way.
 Enables delete directories with content.
 Data Access libraries: beautifulsoup4, libxml2, lxml, protobuf, sqlalchemy, textblob, unixodbc, and xlrd
 Data Science libraries: pandas and dask
 Data Visualization tools: bokeh, graphviz, ipywidgets, ipympl, k3d, mpld3, plotly, seaborn, widgetsnbextension, and wordcloud
 Image Processing: imagemagick, opencv, scikitimage, and spectral
 Machine Learning libraries: nltk, scikitlearn, spacy, and tensorflow

Includes the full nltk data collection (
“all”
), theen_core_web_sm
for spaCY's default model, and the Official Models and datasets for tensorflow  Stats & Data Modelling libraries: numexpr, numpy, pytables, scipy, statsmodels, and sympy
 Other libraries include: cloudpickle, cython, ffmpeg, numba, patsy, rise, vega, and vincent
Julia Notebook
The Julia Notebook Julia on top of R, with a wide selection of libraries includes.
 Based on the jupyter/datasciencenotebook image.
 Includes nbgrader and +GitRepo tools for document sharing.
 Provide rubberband, exercise, and exercise2 extensions. Note these are not related to nbgrader in any way.
 Enables delete directories with content.
 The Julia compiler and base environment with IJulia to support Julia code in Jupyter notebooks.
 The following Julia libraries: Gadfly, HDF5, RDatasets, Plots, PyCall, and QuantEcon
 Data Access libraries: beautifulsoup4, dill, libxml2, lxml, protobuf, sqlalchemy, textblob, unixodbc, xlrd, and xmlschema
 Data Science libraries: pandas and dask
 Data Visualization tools: bokeh, graphviz, ipywidgets, ipympl, k3d, mpld3, plotly, seaborn, widgetsnbextension, and wordcloud
 Image Processing: imagemagick, opencv, scikitimage, and spectral
 Machine Learning libraries: scikitlearn
 Stats & Data Modelling libraries: h5py, hdf5, numexpr, numpy, pytables, quantecon, scipy, statsmodels, and sympy
 Other libraries include: cloudpickle, cython, ffmpeg, netcdf4, nltk, numba, patsy, rise, vega, and vincent
 A selection of RLibraries: rbase, rcaret, rcrayon, rdevtools, rforecast, rhexbin, rhtmltools, rhtmlwidgets, rirkernel, rnycflights13, ropenxlsx, rplyr, rquanteda, rrandomforest, rrcurl, rreadxl, rreshape2, rrmarkdown, rrodbc, rrsqlite, rshiny, rtidyverse, rtopicmodels, rukbabynames, rxml, rxml2, rxmlschema, and rpy2
Haskell Notebook
The Haskell Notebook comes with a selection of haskell libraries.
 Based on the crosscompass/ihaskellnotebook Jupyter Community image.
 Includes nbgrader and +GitRepo tools for document sharing.
 Provide rubberband, exercise, and exercise2 extensions. Note these are not related to nbgrader in any way.
 Enables delete directories with content.
 The following haskell libraries are included: GLUT, JuicyPixels, OpenGL, OpenGLRaw, QuickCheck, gloss, glossrendering, vector, and split
Geospatial Notebook
The geospatial Notebook is a python 3 notebook, with a wide selection of libraries that includes geospatial libraries.
 Based on the jupyter/minimalnotebook image.
 Includes nbgrader and +GitRepo tools for document sharing.
 Provide rubberband, exercise, and exercise2 extensions. Note these are not related to nbgrader in any way.
 Enables delete directories with content.
 The following geocentric libraries are included: apsg, basemap, basemapdatahires, cartopy, contextily, datashader, descartes, earthpy, folium, gdal, geofeather, geopandas, georasters, googlemaps, ipyleaflet, keplergl, landlab, lsdtopotools, momepy, mplleaflet, obspy, osmnx, pygeos, pysal, rasterio, rasterstats, reportlab, sentinelsat, smmap2, smopy, sompy, splot, urbanaccess, windrose, and xarray
 arcgis from ESRI
 Data Access libraries: beautifulsoup4, dill, libspatialite, libxml2, lxml, mysqlconnectorpython, protobuf, sqlalchemy, textblob, unixodbc, xlrd, and xlsxwriter
 Data Science libraries: dask, pandas, and pointpats
 Data Visualization tools: bokeh, graphviz, hvplot, ipywidgets, ipympl, k3d, matplotlib, mpld3, multicoretsne, palettable, plotly, pykrige, pymc3, seaborn, widgetsnbextension, and wordcloud
 Image Processing: imagemagick, opencv, scikitimage, and spectral
 Machine Learning libraries: daskml and scikitlearn
 Stats & Data Modelling libraries: h5py, hdf5, numexpr, numpy, pytables, scipy, statsmodels, and sympy
 Other libraries include: cloudpickle, cmake, cython, ffmpeg, gitdb2, gitpython, ipyparallel, mklservice, netcdf4, nltk, numba, patsy, polyline, qgrid, rise, tzlocal, vega, and vincent