journal quality kde plots with seaborn/pandas - pandas

Question

I'm trying to do some comparative analysis for a publication. I came across seaborn and pandas and really like the ease with which I can create the analysis that I want. However, I find the manuals a bit scanty on the things that I'm trying to understand about the example plots and how to modify the plots to my needs. I'm hoping for some advice here on to get the plots I'm want. Perhaps pandas/seaborn is not what I need.
So, I would like to create subplots, (3,1) or (2,3), of the following figure:
Questions:
I would like the attached plot to have a title on the colorbar. Not sure if this is possible or exactly what is shown, i.e., is it relative frequency or occurrence or a percentage, etc? How can I put a explanatory tile on the colorbar (oriented vertically).
The text is a nice addition. The pearsonr is the correlation, but I'm not sure what is p. My guess is that it is showing the lag, or? If so, how can I remove the p in the text?
I would like to make the same kind of figure for different variables and put it all in a subplot.
Here's the code I pieced together from the seaborn manual/examples and from other users here on SO (thanks guys).
import netCDF4 as nc
import pandas as pd
import xarray as xr
import numpy as np
import seaborn as sns
import pdb
import matplotlib.pyplot as plt
from scipy import stats, integrate
import matplotlib as mpl
import matplotlib.ticker as tkr
import matplotlib.gridspec as gridspec
sns.set(style="white")
sns.set(color_codes=True)
octp = [622.0, 640.0, 616.0, 731.0, 668.0, 631.0, 641.0, 589.0, 801.0,
828.0, 598.0, 742.0,665.0, 611.0, 773.0, 608.0, 734.0, 725.0, 716.0,
699.0, 686.0, 671.0, 700.0, 656.0,686.0, 675.0, 678.0, 653.0, 659.0,
682.0, 674.0, 684.0, 679.0, 704.0, 624.0, 727.0,739.0, 662.0, 801.0,
633.0, 896.0, 729.0, 659.0, 741.0, 510.0, 836.0, 720.0, 685.0,430.0,
833.0, 710.0, 799.0, 534.0, 532.0, 605.0, 519.0, 850.0, 357.0, 858.0,
497.0,404.0, 456.0, 448.0, 836.0, 462.0, 381.0, 499.0, 673.0, 642.0,
641.0, 458.0, 809.0,562.0, 742.0, 732.0, 710.0, 658.0, 533.0, 811.0,
853.0, 856.0, 785.0, 659.0, 697.0,654.0, 673.0, 707.0, 711.0, 423.0,
751.0, 761.0, 638.0, 576.0, 538.0, 596.0, 718.0,843.0, 640.0, 647.0,
692.0, 599.0, 607.0, 537.0, 679.0, 712.0, 612.0, 641.0, 665.0,658.0,
722.0, 656.0, 656.0, 742.0, 505.0, 688.0, 805.0]
cctp = [482.0, 462.0, 425.0, 506.0, 500.0, 464.0, 486.0, 473.0, 577.0,
735.0, 390.0, 590.0,464.0, 417.0, 722.0, 410.0, 679.0, 680.0, 711.0,
658.0, 687.0, 621.0, 643.0, 690.0,630.0, 661.0, 608.0, 658.0, 624.0,
646.0, 651.0, 634.0, 612.0, 636.0, 607.0, 539.0,706.0, 614.0, 706.0,
401.0, 720.0, 746.0, 511.0, 700.0, 453.0, 677.0, 637.0, 605.0,454.0,
733.0, 535.0, 725.0, 668.0, 513.0, 470.0, 589.0, 765.0, 596.0, 749.0,
462.0,469.0, 514.0, 511.0, 789.0, 647.0, 324.0, 555.0, 670.0, 656.0,
786.0, 374.0, 757.0,645.0, 744.0, 708.0, 497.0, 654.0, 288.0, 705.0,
703.0, 446.0, 675.0, 440.0, 652.0,589.0, 542.0, 661.0, 631.0, 343.0,
585.0, 632.0, 591.0, 602.0, 365.0, 535.0, 663.0,561.0, 448.0, 582.0,
591.0, 535.0, 475.0, 422.0, 599.0, 594.0, 569.0, 576.0, 622.0,483.0,
539.0, 515.0, 621.0, 443.0, 435.0, 502.0, 443.0]
cctp = pd.Series(cctp, name='CTP [hPa]')
octp = pd.Series(octp, name='CTP [hPa]')
formatter = tkr.ScalarFormatter(useMathText=True)
formatter.set_scientific(True)
formatter.set_powerlimits((-2, 2))
g = sns.jointplot(cctp,octp, kind="kde",size=8,space=0.2,cbar=True,
n_levels=50,cbar_kws={"format": formatter})
# add a line x=y
x0, x1 = g.ax_joint.get_xlim()
y0, y1 = g.ax_joint.get_ylim()
lims = [max(x0, y0), min(x1, y1)]
g.ax_joint.plot(lims, lims, ':k')
plt.show()
plt.savefig('test_fig.png')
I know I'm asking a lot here. So I put the questions in order of priority.

Answer 1

1: To set the colorbar label, you can add the label key to the cbar_kws dict:
cbar_kws={"format": formatter, "label": 'My colorbar'}
2: To change the stats label, you need to first slightly modify the stats.pearsonr function to only return the first value, instead of the (pearsonr, p) tuple:
pr = lambda a, b: stats.pearsonr(a, b)[0]
Then, you can change that function using jointplot's stat_func kwarg:
stat_func=pr
and finally, you need to change the annotation to get the label right:
annot_kws={'stat':'pearsonr'})
Putting that all together:
pr = lambda a, b: stats.pearsonr(a, b)[0]
g = sns.jointplot(cctp,octp, kind="kde",size=8,space=0.2,cbar=True,
n_levels=50,cbar_kws={"format": formatter, "label": 'My colorbar'},
stat_func=pr, annot_kws={'stat':'pearsonr'})
3: I don't think its possible to put everything in a subplot with jointplot. Happy to be proven wrong there though.