为Seaborn热图分配特定的颜色 [英] Assign specific color to seaborn heatmap

问题描述

我正在尝试使用seaborn制作热图，但被卡住以更改特定值的颜色.假设值0应该是白色，值1应该是灰色，然后在上面使用cmap提供的调色板.

I'm trying to make heatmap using seaborn, but got stuck to change color on specific values. Suppose, the value 0 should be white, and value 1 should be grey, then over that uses the palette as provided by cmap.

试图使用面具，但感到困惑.

Was trying to use mask, but got confused.

import matplotlib.pyplot as plt
import numpy as np
import seaborn as sns
import pandas as pd

df = pd.read_csv('/home/test.csv', index_col=0)

fig, ax = plt.subplots()
sns.heatmap(df, cmap="Reds", vmin=0, vmax=15)
plt.show()

此为示例数据

TAG     A   B   C   D   E   F   G   H   I   J
TAG_1   1   0   0   5   0   7   1   1   0   10
TAG_2   0   1   0   6   0   6   0   0   0   7
TAG_3   0   1   0   2   0   4   0   0   1   4
TAG_4   0   0   0   3   1   3   0   0   0   10
TAG_5   1   0   1   5   0   2   1   1   0   11
TAG_6   0   0   0   0   0   0   0   0   0   12
TAG_7   0   1   0   0   1   0   0   0   0   0
TAG_8   0   0   0   1   0   0   1   0   1   0
TAG_9   0   0   1   0   0   0   0   0   0   0
TAG_10  0   0   0   0   0   0   0   0   0   0

推荐答案

df.set_index('TAG'，inplace = True)告诉seaborn标记应该用作标记，而不是数据

df.set_index('TAG', inplace=True) tells seaborn that the tags should be used as tags, not as data.

二进制"颜色图从较低值的白色平滑过渡到最高值的深黑色.玩 vmin 和 vmax ，将 vmin = 0 和 vmax 设置为1.5到大约5之间的值0将为白色，而1将为任何所需的灰色类型.

The 'binary' colormap goes smoothly from white for the lower values to dark black for the highest. Playing with vmin and vmax, setting vmin=0 and vmax to a value between 1.5 and about 5, value 0 will be white and 1 will be any desired type of gray.

要设置遮罩，数据框应转换为2D numpy数组，并且类型应为float.

To set a mask, the dataframe should be converted to a 2D numpy array and be of type float.

import matplotlib.pyplot as plt
import seaborn as sns
import pandas as pd
from io import StringIO

data_str = StringIO('''TAG     A   B   C   D   E   F   G   H   I   J
TAG_1   1   0   0   5   0   7   1   1   0   10
TAG_2   0   1   0   6   0   6   0   0   0   7
TAG_3   0   1   0   2   0   4   0   0   1   4
TAG_4   0   0   0   3   1   3   0   0   0   10
TAG_5   1   0   1   5   0   2   1   1   0   11
TAG_6   0   0   0   0   0   0   0   0   0   12
TAG_7   0   1   0   0   1   0   0   0   0   0
TAG_8   0   0   0   1   0   0   1   0   1   0
TAG_9   0   0   1   0   0   0   0   0   0   0
TAG_10  0   0   0   0   0   0   0   0   0   0''')

df = pd.read_csv(data_str, delim_whitespace=True)
df.set_index('TAG', inplace=True)
values = df.to_numpy(dtype=float)
ax = sns.heatmap(values, cmap='Reds', vmin=0, vmax=15, square=True)
sns.heatmap(values, xticklabels=df.columns, yticklabels=df.index,
            cmap=plt.get_cmap('binary'), vmin=0, vmax=2, mask=values > 1, cbar=False, ax=ax)
plt.show()

或者，可以创建自定义颜色图.这样，颜色栏也将显示调整后的颜色.

Alternatively, a custom colormap could be created. That way the colorbar will also show the adapted colors.

from matplotlib.colors import LinearSegmentedColormap

cmap_reds = plt.get_cmap('Reds')
num_colors = 15
colors = ['white', 'grey'] + [cmap_reds(i / num_colors) for i in range(2, num_colors)]
cmap = LinearSegmentedColormap.from_list('', colors, num_colors)
ax = sns.heatmap(df, cmap=cmap, vmin=0, vmax=num_colors, square=True, cbar=False)
cbar = plt.colorbar(ax.collections[0], ticks=range(num_colors + 1))
plt.show()

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