My Brain Cells

Easiest (and best) learning materials for anyone with a curiosity for machine learning and artificial intelligence, Deep learning, Programming, and other fun life hacks.

ML|AI|DS

Seaborn – (all functions)

4 years ago
Read Time: 3 minutes
by Anthony
Leave a comment

seaborn: statistical data visualization

#pip install seaborn #conda install seaborn

import seaborn as sns

Functions:

Relational plots

`relplot`	Figure-level interface for drawing relational plots onto a FacetGrid.
`scatterplot`	Draw a scatter plot with possibility of several semantic groupings.
`lineplot`	Draw a line plot with possibility of several semantic groupings.

Distribution plots

`displot`	Figure-level interface for drawing distribution plots onto a FacetGrid.
`histplot`	Plot univariate or bivariate histograms to show distributions of datasets.
`kdeplot`	Plot univariate or bivariate distributions using kernel density estimation.
`ecdfplot`	Plot empirical cumulative distribution functions.
`rugplot`	Plot marginal distributions by drawing ticks along the x and y axes.
`distplot`	DEPRECATED: Flexibly plot a univariate distribution of observations.

Categorical plots

`catplot`	Figure-level interface for drawing categorical plots onto a FacetGrid.
`stripplot`	Draw a scatterplot where one variable is categorical.
`swarmplot`	Draw a categorical scatterplot with non-overlapping points.
`boxplot`	Draw a box plot to show distributions with respect to categories.
`violinplot`	Draw a combination of boxplot and kernel density estimate.
`boxenplot`	Draw an enhanced box plot for larger datasets.
`pointplot`	Show point estimates and confidence intervals using scatter plot glyphs.
`barplot`	Show point estimates and confidence intervals as rectangular bars.
`countplot`	Show the counts of observations in each categorical bin using bars.

Regression plots

`lmplot`	Plot data and regression model fits across a FacetGrid.
`regplot`	Plot data and a linear regression model fit.
`residplot`	Plot the residuals of a linear regression.

Matrix plots

`heatmap`	Plot rectangular data as a color-encoded matrix.
`clustermap`	Plot a matrix dataset as a hierarchically-clustered heatmap.

Multi-plot grids

Facet grids

`FacetGrid`	Multi-plot grid for plotting conditional relationships.
`FacetGrid.map`	Apply a plotting function to each facet’s subset of the data.
`FacetGrid.map_dataframe`	Like `.map` but passes args as strings and inserts data in kwargs.

Pair grids

`pairplot`	Plot pairwise relationships in a dataset.
`PairGrid`	Subplot grid for plotting pairwise relationships in a dataset.
`PairGrid.map`	Plot with the same function in every subplot.
`PairGrid.map_diag`	Plot with a univariate function on each diagonal subplot.
`PairGrid.map_offdiag`	Plot with a bivariate function on the off-diagonal subplots.
`PairGrid.map_lower`	Plot with a bivariate function on the lower diagonal subplots.
`PairGrid.map_upper`	Plot with a bivariate function on the upper diagonal subplots.

Joint grids

`jointplot`	Draw a plot of two variables with bivariate and univariate graphs.
`JointGrid`	Grid for drawing a bivariate plot with marginal univariate plots.
`JointGrid.plot`	Draw the plot by passing functions for joint and marginal axes.
`JointGrid.plot_joint`	Draw a bivariate plot on the joint axes of the grid.
`JointGrid.plot_marginals`	Draw univariate plots on each marginal axes.

Themeing

`set_theme`	Set multiple theme parameters in one step.
`axes_style`	Return a parameter dict for the aesthetic style of the plots.
`set_style`	Set the aesthetic style of the plots.
`plotting_context`	Return a parameter dict to scale elements of the figure.
`set_context`	Set the plotting context parameters.
`set_color_codes`	Change how matplotlib color shorthands are interpreted.
`reset_defaults`	Restore all RC params to default settings.
`reset_orig`	Restore all RC params to original settings (respects custom rc).
`set`	Alias for `set_theme()`, which is the preferred interface.

Color palettes

`set_palette`	Set the matplotlib color cycle using a seaborn palette.
`color_palette`	Return a list of colors or continuous colormap defining a palette.
`husl_palette`	Get a set of evenly spaced colors in HUSL hue space.
`hls_palette`	Get a set of evenly spaced colors in HLS hue space.
`cubehelix_palette`	Make a sequential palette from the cubehelix system.
`dark_palette`	Make a sequential palette that blends from dark to `color`.
`light_palette`	Make a sequential palette that blends from light to `color`.
`diverging_palette`	Make a diverging palette between two HUSL colors.
`blend_palette`	Make a palette that blends between a list of colors.
`xkcd_palette`	Make a palette with color names from the xkcd color survey.
`crayon_palette`	Make a palette with color names from Crayola crayons.
`mpl_palette`	Return discrete colors from a matplotlib palette.

Palette widgets

`choose_colorbrewer_palette`	Select a palette from the ColorBrewer set.
`choose_cubehelix_palette`	Launch an interactive widget to create a sequential cubehelix palette.
`choose_light_palette`	Launch an interactive widget to create a light sequential palette.
`choose_dark_palette`	Launch an interactive widget to create a dark sequential palette.
`choose_diverging_palette`	Launch an interactive widget to choose a diverging color palette.

Utility functions

`load_dataset`	Load an example dataset from the online repository (requires internet).
`get_dataset_names`	Report available example datasets, useful for reporting issues.
`get_data_home`	Return a path to the cache directory for example datasets.
`despine`	Remove the top and right spines from plot(s).
`desaturate`	Decrease the saturation channel of a color by some percent.
`saturate`	Return a fully saturated color with the same hue.
`set_hls_values`	Independently manipulate the h, l, or s channels of a color.

Related

Anthony

Leave a Reply Cancel reply

Back to top