pkglint -r --network --only "migrate"
As a side-effect of migrating the homepages, pkglint also fixed a few
indentations in unrelated lines. These and the new homepages have been
checked manually.
v0.9.0:
This is a major release with several substantial and long-desired new features. There are also updates/modifications to the themes and color palettes that give better consistency with matplotlib 2.0 and some notable API changes.
Added a warning in FacetGrid when passing a categorical plot function without specifying order (or hue_order when hue is used), which is likely to produce a plot that is incorrect.
Improved compatibility between FacetGrid or PairGrid and interactive matplotlib backends so that the legend no longer remains inside the figure when using legend_out=True.
Changed categorical plot functions with small plot elements to use dark_palette instead of light_palette when generating a sequential palette from a specified color.
Improved robustness of kdeplot and distplot to data with fewer than two observations.
Fixed a bug in clustermap when using yticklabels=False.
Fixed a bug in pointplot where colors were wrong if exactly three points were being drawn.
Fixed a bug inpointplot where legend entries for missing data appeared with empty markers.
Fixed a bug in clustermap where an error was raised when annotating the main heatmap and showing category colors.
Fixed a bug in clustermap where row labels were not being properly rotated when they overlapped.
Fixed a bug in kdeplot where the maximum limit on the density axes was not being updated when multiple densities were drawn.
Improved compatibility with future versions of pandas.
in Python. It is built on top of matplotlib and tightly integrated with the
PyData stack, including support for numpy and pandas data structures and
statistical routines from scipy and statsmodels.
Some of the features that seaborn offers are
* Several built-in themes that improve on the default matplotlib aesthetics
* Tools for choosing color palettes to make beautiful plots that reveal
patterns in your data
* Functions for visualizing univariate and bivariate distributions or for
comparing them between subsets of data
* Tools that fit and visualize linear regression models for different kinds
of independent and dependent variables
* Functions that visualize matrices of data and use clustering algorithms to
discover structure in those matrices
* A function to plot statistical timeseries data with flexible estimation and
representation of uncertainty around the estimate
* High-level abstractions for structuring grids of plots that let you easily
build complex visualizations
