python bokeh, how to make a correlation plot?

Question

How can I make a correlation heatmap in Bokeh?

import pandas as pd
import bokeh.charts

df = pd.util.testing.makeTimeDataFrame(1000)
c = df.corr()

p = bokeh.cha         


        

Answer 1

So I think I can provide a baseline code to help do what you are asking using a combination of the answers above and some extra pre-processing.

Let's assume you have a dataframe df already loaded (in this case the UCI Adult Data) and the correlation coefficients calculated (p_corr).

import bisect
#
from math import pi
from numpy import arange
from itertools import chain
from collections import OrderedDict
#
from bokeh.palettes import RdBu as colors  # just make sure to import a palette that centers on white (-ish)
from bokeh.models import ColorBar, LinearColorMapper

colors = list(reversed(colors[9]))  # we want an odd number to ensure 0 correlation is a distinct color
labels = df.columns
nlabels = len(labels)

def get_bounds(n):
    """Gets bounds for quads with n features"""
    bottom = list(chain.from_iterable([[ii]*nlabels for ii in range(nlabels)]))
    top = list(chain.from_iterable([[ii+1]*nlabels for ii in range(nlabels)]))
    left = list(chain.from_iterable([list(range(nlabels)) for ii in range(nlabels)]))
    right = list(chain.from_iterable([list(range(1,nlabels+1)) for ii in range(nlabels)]))
    return top, bottom, left, right

def get_colors(corr_array, colors):
    """Aligns color values from palette with the correlation coefficient values"""
    ccorr = arange(-1, 1, 1/(len(colors)/2))
    color = []
    for value in corr_array:
        ind = bisect.bisect_left(ccorr, value)
        color.append(colors[ind-1])
    return color

p = figure(plot_width=600, plot_height=600,
           x_range=(0,nlabels), y_range=(0,nlabels),
           title="Correlation Coefficient Heatmap (lighter is worse)",
           toolbar_location=None, tools='')

p.xgrid.grid_line_color = None
p.ygrid.grid_line_color = None
p.xaxis.major_label_orientation = pi/4
p.yaxis.major_label_orientation = pi/4

top, bottom, left, right = get_bounds(nlabels)  # creates sqaures for plot
color_list = get_colors(p_corr.values.flatten(), colors)

p.quad(top=top, bottom=bottom, left=left,
       right=right, line_color='white',
       color=color_list)

# Set ticks with labels
ticks = [tick+0.5 for tick in list(range(nlabels))]
tick_dict = OrderedDict([[tick, labels[ii]] for ii, tick in enumerate(ticks)])
# Create the correct number of ticks for each axis 
p.xaxis.ticker = ticks
p.yaxis.ticker = ticks
# Override the labels 
p.xaxis.major_label_overrides = tick_dict
p.yaxis.major_label_overrides = tick_dict

# Setup color bar
mapper = LinearColorMapper(palette=colors, low=-1, high=1)
color_bar = ColorBar(color_mapper=mapper, location=(0, 0))
p.add_layout(color_bar, 'right')

show(p)

This will result in the following plot if the categories are integer encoded (this is a horrible data example):