Description
Is your feature request related to a problem?
I often need to plot a heatmap of a DataFrame which uses an IntervalIndex
as its columns (and, usually, time as its index). Such a plot could also be called a "dynamic spectrum" or "2D histogram" and is used to quickly get an idea of how a spectrum develops over time.
This is slightly different from what is usually considered as a heatmap (see #19008 for an example) as the bins are not necessarily equidistant and there is not necessarily a separate label for each bin. The y axis (which is used for the IntervalIndex
) could even have logarithmic scaling.
Describe the solution you'd like
This could use the same API df.plot(type='heatmap')
as suggested in #19008 and switch between appropriate axis scaling/labeling modes depending on whether a CategoricalIndex
, IntervalIndex
or other types of indices are used.
Describe alternatives you've considered
My current implementation (see below) uses matplotlib's pcolormesh
, but needs to do some fiddling with the bin edges to work correctly.
Matplotlib's hist2d
does not work for this use case, because the data is already stored in histogrammed form - the histogram and its bins don't need to be calculated, just plotted.
Seaborn's heatmap
function seems to be limited to plotting categorical data, so both IntervalIndex
and DatetimeIndex
are displayed as categorical data with one label per bin, equidistant spacing, and values on the y axis sorted from top to bottom instead of bottom to top:
Additional context
My current implementation looks similar to this:
binedges = np.append(df.columns.left, df.columns.right[-1])
X, Y = np.meshgrid(df.index, binedges)
pcm = ax.pcolormesh(X, Y, df.values.T)
# then add labels, colorbar etc.
This only works if the IntervalIndex
has no gaps and is non-overlapping, which would have to be checked first.