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John Tukey’s stem-and-leaf plot first appeared in 1970. Although quite useful back then, it cannot handle more than 300 data points and is completely text-based. Stemgraphic is an easy to use python package providing a solution to these limitations (no size limit, graphical tool). It also supports categorical and text as input.
A typical stem_graphic output:
For an in depth look at the algorithms and the design of stemgraphic, see
Stemgraphic: A Stem-and-Leaf Plot for the Age of Big Data
Documentation is available as pdf stemgraphic.pdf and online html.
The official website of stemgraphic is: http://stemgraphic.org
A Stem-and-leaf Timeline: timeline pdf on artchiv.es
See also: Are you smarter than a fifth grader?
Stemgraphic requires docopt, matplotlib and pandas. Optionally, having Scipy installed will give you secondary plots and Dask (see requirements_dev.txt for all needed to run all the functional tests) will allow for out of core, big data visualization. See more python packages that can be installed for more functionality in the section "Optional Requirements".
Installation is simple:
pip3 install -U stemgraphic
or from this cloned repository, in the package root:
python3 setup.py install
If you only have python3, pip3 and python3 are probably going to be pip and python. At this time, we do not have a conda package yet, but you can install everything else with conda, and then pip install stemgraphic.
You can pip install these modules for additional functionality:
stemgraphic comes with a command line tool:
stem -h
Stem.
Stem and leaf plot from a csv or excel spreadsheet using best defaults. Can do text (text and dot) or graphic (kde,
graphic, hist, line).
Usage:
stem <input> [-c <column>] [-d] [-f] [-k <file>] [-o <file>] [-p <percent>] [-r <random>] [-s <server>] [-t <type>] [-u] [-w]
stem -h | --help
stem --version
Options:
-h --help Show this screen.
-c <column> column index
-d describe the data
-f force dask
-k <file> persist sample to file (.csv, .pkl)
-o <file> output file (.txt, .png) or stdout
-p <percent> trim data on both ends (ex: 0.2)
-r <random> random_state seed (ex: 42)
-s <server> head node for distributed cluster
-t <type> alternate type of distribution plot
-u use all data (default: 300 on text, 900 on graphics)
-w wide format (horizontal)
--version
A typical command line output:
An example Sixel graphics in the terminal:
The supported graphic chart types (-t):
The supported text chart types (-t):
pandas
>= 1.0Version bump to 0.6 due to order of params changing. Shouldn't affect using named args
Major code change and expansion for num.stem_graphic including:
Other changes:
Major new release.
Internal release for customer.
Added Heatmap
Basic PDF documentation
Quickstart notebook
Matploblib 2.0 compatibility
Persist sample from command line tool (-k filename.pkl or -k filename.csv).
Windows compatible bat file wrapper (stem.bat).
Added full command line access to dask distributed server (-d, -s, use file in '' when using glob / wildcard).
For operations with dask, performance has been increased by 25% in this latest release, by doing a compute once of min, max and count all at once. Count replaces len(x).
Added the companion PDF as it will be presented at PyData Carolinas 2016.
FAQs
Graphic and text stem-and-leaf plots
We found that stemgraphic demonstrated a healthy version release cadence and project activity because the last version was released less than a year ago. It has 1 open source maintainer collaborating on the project.
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