v0.5.0 (November 2014)#
This is a major release from 0.4. Highlights include new functions for plotting heatmaps, possibly while applying clustering algorithms to discover structured relationships. These functions are complemented by new custom colormap functions and a full set of IPython widgets that allow interactive selection of colormap parameters. The palette tutorial has been rewritten to cover these new tools and more generally provide guidance on how to use color in visualizations. There are also a number of smaller changes and bugfixes.
heatmap()function for visualizing a matrix of data by color-encoding the values. See the docs for more information.
clustermap()function for clustering and visualizing a matrix of data, with options to label individual rows and columns by colors. See the docs for more information. This work was lead by Olga Botvinnik.
pairplot()get a new keyword argument,
markers. This can be a single kind of marker or a list of different markers for each level of the
huevariable. Using different markers for different hues should let plots be more comprehensible when reproduced to black-and-white (i.e. when printed). See the github pull request (#323) for examples.
More generally, there is a new keyword argument in
hue_kws. This similarly lets plot aesthetics vary across the levels of the hue variable, but more flexibly.
hue_kwsshould be a dictionary that maps the name of keyword arguments to lists of values that are as long as the number of levels of the hue variable.
subplot_kwshas been added to
FacetGrid. This allows for faceted plots with custom projections, including maps with Cartopy.
Added two new functions to create custom color palettes. For sequential palettes, you can use the
light_palette()function, which takes a seed color and creates a ramp from a very light, desaturated variant of it. For diverging palettes, you can use the
diverging_palette()function to create a balanced ramp between two endpoints to a light or dark midpoint. See the palette tutorial for more information.
Added the ability to specify the seed color for
dark_palette()as a tuple of
hlsspace values or as a named
xkcdcolor. The interpretation of the seed color is now provided by the new
inputparameter to these functions.
Added several new interactive palette widgets:
choose_diverging_palette(). For consistency, renamed the cubehelix widget to
choose_cubehelix_palette()(and fixed a bug where the cubehelix palette was reversed). These functions also now return either a color palette list or a matplotlib colormap when called, and that object will be live-updated as you play with the widget. This should make it easy to iterate over a plot until you find a good representation for the data. See the Github pull request or this notebook (download it to use the widgets) for more information.
Overhauled the color palette tutorial to organize the discussion by class of color palette and provide more motivation behind the various choices one might make when choosing colors for their data.
Fixed a bug in
PairGridthat gave incorrect results (or a crash) when the input DataFrame has a non-default index.
Fixed a bug in
PairGridwhere passing columns with a date-like datatype raised an exception.
Fixed a bug where
lmplot()would show a legend when the hue variable was also used on either the rows or columns (making the legend redundant).
Worked around a matplotlib bug that was forcing outliers in
boxplot()to appear as blue.
kdeplot()now accepts pandas Series for the
Using a non-default correlation method in
sig_stars=Falseas the permutation test used to significance values for the correlations uses a pearson metric.
pdf.fonttypefrom the style definitions, as the value used in version 0.4 resulted in very large PDF files.