seaborn: statistical data visualization — seaborn 0.13.0 documentation
Seaborn is a Python data visualization library based on matplotlib. It provides a high-level interface for drawing attractive and informative statistical graphics. For a brief introduction to the ideas behind the library, you can read the introductory notes or the paper. Visit the installation page to see how you can download the package and get started with it. You can browse the example gallery
seaborn.pairplot — seaborn 0.13.2 documentation
seaborn.pairplot# seaborn.pairplot(data, *, hue=None, hue_order=None, palette=None, vars=None, x_vars=None, y_vars=None, kind='scatter', diag_kind='auto', markers=None, height=2.5, aspect=1, corner=False, dropna=False, plot_kws=None, diag_kws=None, grid_kws=None, size=None)# Plot pairwise relationships in a dataset. By default, this function will create a grid of Axes such that each numeric variab
seaborn.PairGrid — seaborn 0.13.2 documentation
seaborn.PairGrid# class seaborn.PairGrid(data, *, hue=None, vars=None, x_vars=None, y_vars=None, hue_order=None, palette=None, hue_kws=None, corner=False, diag_sharey=True, height=2.5, aspect=1, layout_pad=0.5, despine=True, dropna=False)# Subplot grid for plotting pairwise relationships in a dataset. This object maps each variable in a dataset onto a column and row in a grid of multiple axes. Dif
seaborn.kdeplot — seaborn 0.13.2 documentation
seaborn.kdeplot# seaborn.kdeplot(data=None, *, x=None, y=None, hue=None, weights=None, palette=None, hue_order=None, hue_norm=None, color=None, fill=None, multiple='layer', common_norm=True, common_grid=False, cumulative=False, bw_method='scott', bw_adjust=1, warn_singular=True, log_scale=None, levels=10, thresh=0.05, gridsize=200, cut=3, clip=None, legend=True, cbar=False, cbar_ax=None, cbar_kws=
User guide and tutorial — seaborn 0.13.2 documentation
Ctrl+K Site Navigation Installing Gallery Tutorial API Releases Citing FAQ GitHub StackOverflow Twitter On this page User guide and tutorial# An introduction to seaborn A high-level API for statistical graphics Multivariate views on complex datasets Opinionated defaults and flexible customization API Overview# Overview of seaborn plotting functions Similar functions for similar tasks Figure-level
Controlling figure aesthetics — seaborn 0.13.2 documentation
Controlling figure aesthetics# Drawing attractive figures is important. When making figures for yourself, as you explore a dataset, it’s nice to have plots that are pleasant to look at. Visualizations are also central to communicating quantitative insights to an audience, and in that setting it’s even more necessary to have figures that catch the attention and draw a viewer in. Matplotlib is highl
Example gallery — seaborn 0.13.2 documentation
Ctrl+K Site Navigation Installing Gallery Tutorial API Releases Citing FAQ GitHub StackOverflow Twitter Example gallery# lmplot scatterplot lineplot displot relplot catplot boxplot violinplot relplot jointplot histplot boxplot stripplot JointGrid jointplot FacetGrid boxenplot scatterplot lmplot FacetGrid heatmap JointGrid kdeplot displot displot lmplot PairGrid PairGrid PairGrid barplot kdeplot ba
seaborn.heatmap — seaborn 0.13.2 documentation
seaborn.heatmap# seaborn.heatmap(data, *, vmin=None, vmax=None, cmap=None, center=None, robust=False, annot=None, fmt='.2g', annot_kws=None, linewidths=0, linecolor='white', cbar=True, cbar_kws=None, cbar_ax=None, square=False, xticklabels='auto', yticklabels='auto', mask=None, ax=None, **kwargs)# Plot rectangular data as a color-encoded matrix. This is an Axes-level function and will draw the hea
Building structured multi-plot grids — seaborn 0.13.2 documentation
Building structured multi-plot grids# When exploring multi-dimensional data, a useful approach is to draw multiple instances of the same plot on different subsets of your dataset. This technique is sometimes called either “lattice” or “trellis” plotting, and it is related to the idea of “small multiples”. It allows a viewer to quickly extract a large amount of information about a complex dataset.
Visualizing distributions of data — seaborn 0.13.2 documentation
Visualizing distributions of data# An early step in any effort to analyze or model data should be to understand how the variables are distributed. Techniques for distribution visualization can provide quick answers to many important questions. What range do the observations cover? What is their central tendency? Are they heavily skewed in one direction? Is there evidence for bimodality? Are there
seaborn: statistical data visualization — seaborn 0.13.2 documentation
Seaborn is a Python data visualization library based on matplotlib. It provides a high-level interface for drawing attractive and informative statistical graphics. For a brief introduction to the ideas behind the library, you can read the introductory notes or the paper. Visit the installation page to see how you can download the package and get started with it. You can browse the example gallery
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https://cs231n.stanford.edu/slides/2017/cs231n_2017_lecture15.pdf
http://seaborn.pydata.org/introduction.html