Seaborn自定义范围热图 [英] Seaborn custom range heatmap

问题描述

我需要根据以下要求构建自定义的类似于海图的热图:

I need to build custom seaborn heatmap-like plot according to these requirements:

import pandas as pd
df = pd.DataFrame({"A": [0.3, 0.8, 1.3], 
                   "B": [4, 9, 15], 
                   "C": [650, 780, 900]})

df_info = pd.DataFrame({"id": ["min", "max"],
                   "A": [0.5, 0.9], 
                   "B": [6, 10], 
                   "C": [850, 880]})
df_info = df_info.set_index('id')

df

    A      B    C
0   0.3    4    650
1   0.8    9    780
2   1.3    15   900

df_info

id      A      B    C
            
min     0.5    6    850
max     0.9    10   880

df 中的每个值都应在 df_info 中定义的范围内.
例如，如果 A 列的值在0.5到0.9之间，则认为它们是正常的.超出范围的值应使用自定义的热图着色.

Each value within df is supposed to be within a range defined in df_info.
For example the values for the column A are considered normal if they are within 0.5 and 0.9. Values that are outside the range should be colorized using a custom heatmap.

尤其是:

• 落入为每一列定义的范围内的值，不要是白色背景单元格上的彩色纯黑色文本.
该列的
• 小于 min 的值应使用彩色显示，例如蓝色.它们的值从最小值开始越低，蓝色阴影越深.
该列的
• 高于 max 的值应着色，例如用红色.它们的值越高，红色阴影越深.

• Values that fall within the range defined for each column should not be colorized, plain black text on white background cell.
• Values lower than min for that column should be colorized, for example in blue. The lower their values from the min the darker the shade of blue.
• Values higher than max for that column should be colorized, for example in red. The higher their values from the max the darker the shade of red.

问:我不知道如何使用标准热图来实现这一点，我什至不确定我是否可以通过热图来实现这一点.有什么建议吗?

Q: I wouldn't know how to approach this with a standard heatmap, I'm not even sure I can accomplish this with a heatmap plot. Any suggestion?

推荐答案

据我所知，热图只能具有一个比例值.我建议规范化 df 数据框中的数据，以便每一列中的值都遵循:

As far as I know, a heatmap can only have one scale of values. I would suggest normalizing the data you have in the df dataframe so the values in every column follow:

• 介于 0 和 1 之间(如果值介于 df_info 的 min max 之间)代码>
• 低于 0 ，如果该值低于 df_info 的 min
• 在 1 之上，如果该值高于 df_info 的 max

• between 0 and 1 if the value is between df_info's min max
• below 0 if the value is below df_info's min
• above 1 if the value is above df_info's max

要标准化您的数据框，请使用:

To normalize your dataframe use :

for col in df:
    df[col] = (df[col] - df_info[col]['min']) / (df_info[col]['max'] - df_info[col]['min'])

最后，使用以下代码创建颜色编码的热图:

Finally, to create the color-coded heatmap use :

import seaborn as sns
from matplotlib.colors import LinearSegmentedColormap

vmin = df.min().min()
vmax = df.max().max()

colors = [[0, 'darkblue'],
          [- vmin / (vmax - vmin), 'white'],
          [(1 - vmin)/ (vmax - vmin), 'white'],
          [1, 'darkred']]

cmap = LinearSegmentedColormap.from_list('', colors)
sns.heatmap(df, cmap=cmap, vmin=vmin, vmax=vmax)

使用 vmin 和 vmax 进行的其他计算允许根据最小和最大差异来动态缩放颜色图.

The additional calculations with vmin and vmax allow a dynamic scaling of the colormap depending on the differences with the minimums and maximums.

使用您的输入数据框，我们有以下热图:

Using your input dataframe we have the following heatmap:

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