如何在 Bokeh 中制作相关热图？
import pandas as pd
import bokeh.charts
df = pd.util.testing.makeTimeDataFrame(1000)
c = df.corr()
p = bokeh.charts.HeatMap(c) # not right
# try to make it a long form
# (and it's ugly in pandas to use 'index' in melt)
c['x'] = c.index
c = pd.melt(c, 'x', ['A','B','C','D'])
# this shows the right 4x4 matrix, but values are still wrong
p = bokeh.charts.HeatMap(c, x = 'x', y = 'variable', values = 'value')
顺便问一下，我可以在侧面制作一个颜色条，而不是情节中的图例吗？以及如何选择颜色范围/映射，例如深蓝色（-1）到白色（0）到深红色（+1）？
所以我想我可以提供一个基线代码来帮助完成您所要求的工作，结合使用上面的答案和一些额外的预处理。
假设您有一个数据框df已经加载（在本例中UCI 成人数据）和计算的相关系数（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)
如果类别是整数编码的，这将导致以下绘图（这是一个可怕的数据示例）：
