Displaying 1 to 20 from 43 results

PythonDataScienceHandbook - Python Data Science Handbook: full text in Jupyter Notebooks

  •    Jupyter

This repository contains the entire Python Data Science Handbook, in the form of (free!) Jupyter notebooks. Run the code using the Jupyter notebooks available in this repository's notebooks directory.

holoviews - Stop plotting your data - annotate your data and let it visualize itself.

  •    Python

Stop plotting your data - annotate your data and let it visualize itself. HoloViews is an open-source Python library designed to make data analysis and visualization seamless and simple. With HoloViews, you can usually express what you want to do in very few lines of code, letting you focus on what you are trying to explore and convey, not on the process of plotting.

mlcourse_open - OpenDataScience Machine Learning course. Both in English and Russian

  •    Python

This is the list of published articles on medium.com 🇬🇧, habr.com 🇷🇺, and jqr.com 🇨🇳. Icons are clickable. Also, links to Kaggle Kernels (in English) are given. This way one can reproduce everything without installing a single package. Assignments will be announced each week. Meanwhile, you can pratice with demo versions. Solutions will be discussed in the upcoming run of the course.

osmnx - OSMnx: Python for street networks

  •    Python

Retrieve, construct, analyze, and visualize street networks from OpenStreetMap: full overview. You can just as easily download and work with building footprints, elevation data, street bearings/orientations, and network routing.

mpld3 - D3 Renderings of Matplotlib Graphics

  •    Jupyter

Note: mpld3 is in the process of switching maintainers: feature requests & bug reports are likely to be delayed. If you are interested in contributing to this project, please contact one of the repository owners. This is an interactive D3js-based viewer which brings matplotlib graphics to the browser. Please visit http://mpld3.github.io for documentation and examples.

matplotlib-tutorial - Matplotlib tutorial for beginner

  •    Python

All code and material is licensed under a Creative Commons Attribution-ShareAlike 4.0. You can test your installation before the tutorial using the check-installation.py script.

pynamical - Pynamical is a Python package for modeling and visualizing discrete nonlinear dynamical systems, chaos, and fractals

  •    Python

pynamical uses pandas, numpy, and numba for fast simulation, and matplotlib for visualizations and animations to explore system behavior. Compatible with Python 2 and 3. Pynamical comes packaged with the logistic map, the Singer map, and the cubic map predefined. The models may be run with a range of parameter values over a set of time steps, and the resulting numerical output is returned as a pandas DataFrame. Pynamical can then visualize this output in various ways, including with bifurcation diagrams, two-dimensional phase diagrams, three-dimensional phase diagrams, and cobweb plots.

joypy - Joyplots in matplotlib + pandas

  •    Jupyter

JoyPy is a one-function Python package based on matplotlib + pandas with a single purpose: drawing joyplots. Joyplots are stacked, partially overlapping density plots, simple as that. They are a nice way to plot data to visually compare distributions, especially those that change across one dimension (e.g., over time). Though hardly a new technique, they have become very popular lately thanks to the R package ggjoy (which is clearly much better developed/maintained than this one -- and I strongly suggest you to use that if you can use R and ggplot.) Update: the ggjoy package has now been renamed ggridges.

mpl-scatter-density - :zap: Fast scatter density plots for Matplotlib :zap:

  •    Python

The mpl-scatter-density mini-package provides functionality to make it easy to make your own scatter density maps, both for interactive and non-interactive use. Fast. The following animation shows real-time interactive use with 10 million points, but interactive performance is still good even with 100 million points (and more if you have enough RAM). When panning, the density map is shown at a lower resolution to keep things responsive (though this is customizable).

matplotlib-haskell - Haskell bindings for Python's Matplotlib

  •    Haskell

Documentation is available on Hackage. For more examples see the tests. We need -XExtendedDefaultRules to avoid having to manually having to specify certain types.

Geopython - Spatial/Geo Python explorations

  •    Jupyter

This is a repository of various geo/spatial analysis techniques using Python libraries, chiefly Numpy, Pandas, Shapely, Fiona, Descartes, Matplotlib, and Matplotlib-Basemap. These tutorials, visualisations, and libraries are an occasional side effect of being embroiled in a PhD at the Bartlett Centre for Advanced Spatial Analysis, at UCL, and teaching on the undergraduate Data Science and Visualisation course.

daltonize - Simulate and correct images for dichromatic color blindness

  •    Python

Daltonize can also adjust the color palette of an input image such that a color blind person can perceive the full information content. It can be used as a command line tool to convert pixel images but also as a Python module. If used as the latter, it provides an API to simulate and correct for color blindness in matplotlib figures. This allows to create color blind friendly vector graphics suitable for publication.

catterplotpy - A tool for creating catterplots in Python with matplotlib

  •    Python

See the catterplot docstring for all the available options. This work was inspired in large part by David L Gibbs' R catterplot. The cat shapes are directly derived from that work.