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data-science-ipython-notebooks - Data science Python notebooks: Deep learning (TensorFlow, Theano, Caffe, Keras), scikit-learn, Kaggle, big data (Spark, Hadoop MapReduce, HDFS), matplotlib, pandas, NumPy, SciPy, Python essentials, AWS, and various command lines
IPython Notebook(s) demonstrating deep learning functionality.IPython Notebook(s) demonstrating scikit-learn functionality.
machine-learning deep-learning data-science big-data aws tensorflow theano caffe scikit-learn kaggle spark mapreduce hadoop matplotlib pandas numpy scipy kerasPythonDataScienceHandbook - Python Data Science Handbook: full text in Jupyter Notebooks
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.
scikit-learn numpy jupyter-notebook matplotlib pandasholoviews - Stop plotting your data - annotate your data and let it visualize itself.
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.
visualization analysis bokeh matplotlib interactive data-science exploratory-data-analysis pandasmlcourse_open - OpenDataScience Machine Learning course. Both in English and Russian
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.
machine-learning data-analysis data-science pandas algorithms numpy scipy matplotlib seaborn plotly scikit-learn kaggle-inclass vowpal-wabbit ipynb docker mathArtificial-Intelligence-Deep-Learning-Machine-Learning-Tutorials - A comprehensive list of Deep Learning / Artificial Intelligence and Machine Learning tutorials - rapidly expanding into areas of AI/Deep Learning / Machine Vision / NLP and industry specific areas such as Automotives, Retail, Pharma, Medicine, Healthcare by Tarry Singh until at-least 2020 until he finishes his Ph
A comprehensive list of Deep Learning / Artificial Intelligence and Machine Learning tutorials - rapidly expanding into areas of AI/Deep Learning / Machine Vision / NLP and industry specific areas such as Automotives, Retail, Pharma, Medicine, Healthcare by Tarry Singh until at-least 2020 until he finishes his Ph.D. (which might end up being inter-stellar cosmic networks! Who knows! 😀)
machine-learning deep-learning tensorflow pytorch keras matplotlib aws kaggle pandas scikit-learn torch artificial-intelligence neural-network convolutional-neural-networks tensorflow-tutorials python-data ipython-notebook capsule-networkyellowbrick - Visual analysis and diagnostic tools to facilitate machine learning model selection.
Visual analysis and diagnostic tools to facilitate machine learning model selection. Image by Quatro Cinco, used with permission, Flickr Creative Commons.
machine-learning visual-analysis model-selection visualization scikit-learn visualizer matplotlib estimator residuals transformer advantage anacondaosmnx - OSMnx: Python for street networks
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.
openstreetmap gis network street-networks shapefile visualization graph overpass-api graphs networkx matplotlib spatial-analysis geospatial maps urban-planning design transportation physics math geographympld3 - D3 Renderings of Matplotlib Graphics
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.
d3 matplotlibmatplotlib-tutorial - Matplotlib tutorial for beginner
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.
matplotlib tutorialPythonMachineLearning - Practice and tutorial-style notebooks covering wide variety of machine learning techniques
Practice and tutorial-style notebooks covering wide variety of machine learning techniques
numpy statistics pandas matplotlib regression scikit-learn classification principal-component-analysis clustering decision-trees random-forest dimensionality-reduction neural-network deep-learning artificial-intelligence data-science machine-learning k-nearest-neighbours naive-bayesmatplotlib2tikz - A converter that takes a matplotlib figure and spits out a TikZ/PGFplots figure for smooth integration into LaTeX
for native inclusion into LaTeX documents. matplotlib2tikz works with both Python 2 and Python 3.
tikz matplotlib pgfplots latexpyheat - pprofile + matplotlib = Python program profiled as an awesome heatmap!
Profilers are extremely helpful tools. They help us dig deep into code, find and understand performance bottlenecks. But sometimes we just want to lay back, relax and still get a gist of the hot zones in our code. A picture is worth a thousand words.
profiling heatmap matplotlibpynamical - Pynamical is a Python package for modeling and visualizing discrete nonlinear dynamical systems, chaos, and fractals
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.
chaos nonlinear fractal logistic visualization modeling animation math physics pandas numba numpy matplotlib ipynb bifurcation-diagram fractals systems phase-diagram cobweb-plotjoypy - Joyplots in matplotlib + pandas
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.
data-visualization matplotlib plottingmpl-scatter-density - :zap: Fast scatter density plots for Matplotlib :zap:
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 visualizationmatplotlib-haskell - Haskell bindings for Python's Matplotlib
Documentation is available on Hackage. For more examples see the tests. We need -XExtendedDefaultRules to avoid having to manually having to specify certain types.
haskell matplotlib charts plotGeopython - Spatial/Geo Python explorations
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.
geo geopandas pandas geospatial jupyter spatial-analysis matplotlib choropleth computational-geometry isochrones geographical-information-system geographically-weighted-regression shapely basemaplovecraft - A basic NLTK demo, using the collected works of H. P. Lovecraft as a corpus
A basic NLTK demo, using the collected works of H. P. Lovecraft as a corpus
nltk lovecraft frequency-count classification corpus matplotlibdaltonize - Simulate and correct images for dichromatic color blindness
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.
color-blindness matplotlib matplotlib-figures
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