Matplotlib：仅将单元格颜色应用于某些列/单元格

让我自己陷入了困境。

我正在 Matplotlib 中创建一个基本表（通过 Pandas，但这不是问题）。我想要完成的是创建一个表，其中第一列（将是字符串值）保持白色...但列 2,3,4,5,6 是浮动/整数，并将由自定义着色标准化颜色图。

I've started with the basics, and created the 'colored' table via the code below. This only plots the columns with integer values at this point, see here:

我最终需要做的是用附加列绘制此图，例如在列“A”之前或在列“F”之后，其中包含字符串值，例如[“MBIAS”、“RMSE”、“BAGSS”、“MBIAS”、“MBIAS”]。

但是，如果我尝试将下面代码中的 cellColours 方法应用到混合字符串和浮点/整数列表的表，它显然会失败。

有没有一种方法可以将单元格颜色方案仅应用于某些单元格或行/列？我可以循环遍历，将自定义颜色图应用到特定单元格吗？

任何帮助或提示将不胜感激！

Code:

import numpy as np
import matplotlib
from matplotlib import cm
import matplotlib.pyplot as plt
from pandas import *


#Create sample data in pandas dataframe
idx = Index(np.arange(1,6))
df = DataFrame(abs(2*np.random.randn(5, 5)), index=idx, columns=['A', 'B', 'C', 'D', 'E'])
model = ['conusarw', 'conusarw', 'conusarw', 'nam04', 'emhrrr']
df['Model'] = model
df1 = df[['A','B','C','D','E']]
test = df1.round({'A':2,'B':2,'C':2,'D':2,'E':2})
print(test)
vals = test.values
print(vals)

#Creates normalized list (from 0-1) based a user provided range and center of distribution.
norm = matplotlib.colors.TwoSlopeNorm(vmin=0,vcenter=1,vmax=10)
#Merges colormap to the normalized data based on customized normalization pattern from above.
colours = plt.cm.coolwarm(norm(vals))

#Create figure in Matplotlib in which to plot table.
fig = plt.figure(figsize=(15,8))
ax = fig.add_subplot(111, frameon=False, xticks=[], yticks=[])
#Plot table, using pandas dataframe information and data.
#Customized lists of data and names can also be provided.
the_table=plt.table(cellText=vals, rowLabels=model, colLabels=df.columns,
                    loc='center', cellColours=colours)

plt.savefig('test_table.png')

而不是快速矢量化调用colours = plt.cm.coolwarm(norm(vals))，您可以将常规 Python 循环与 if 测试结合使用。下面的代码循环遍历各个行，然后遍历各个元素并测试它们是否是数字。类似的循环准备舍入值。速度并不是真正的问题，除非您有数千个元素。

（代码使用import pandas as pd, as import * from pandas不推荐。）

import matplotlib.pyplot as plt
from matplotlib.colors import to_rgba, TwoSlopeNorm
import pandas as pd
import numpy as np

# Create sample data in pandas dataframe
idx = pd.Index(np.arange(1, 6))
df = pd.DataFrame(abs(2 * np.random.randn(5, 5)), index=idx, columns=['A', 'B', 'C', 'D', 'E'])
df['Model'] = ['conusarw', 'conusarw', 'conusarw', 'nam04', 'emhrrr']

cmap = plt.cm.coolwarm
norm = TwoSlopeNorm(vmin=0, vcenter=1, vmax=10)
colours = [['white' if not np.issubdtype(type(val), np.number) else cmap(norm(val)) for val in row]
           for row in df.values]
vals = [[val if not np.issubdtype(type(val), np.number) else np.round(val, 2) for val in row]
        for row in df.values]

fig = plt.figure(figsize=(15, 8))
ax = fig.add_subplot(111, frameon=False, xticks=[], yticks=[])
the_table = plt.table(cellText=vals, rowLabels=df['Model'].to_list(), colLabels=df.columns,
                      loc='center', cellColours=colours)
plt.show()

PS：如果速度是一个问题，下面的代码有点棘手。它用：

• 设置颜色图的“坏颜色”
• pd.to_numeric(..., errors='coerce')将所有字符串转换为 nan
• as pd.to_numeric()仅适用于一维数组，ravel() and reshape()被使用
• 使用相同的数组，np.where可以进行四舍五入

cmap = plt.cm.coolwarm.copy()
cmap.set_bad('white')
norm = TwoSlopeNorm(vmin=0, vcenter=1, vmax=10)
values = pd.to_numeric(df.values.ravel(), errors='coerce').reshape(df.shape)
colours = cmap(norm(values))
vals = np.where(np.isnan(values), df.values, np.round(values, 2))

fig = plt.figure(figsize=(15, 8))
ax = fig.add_subplot(111, frameon=False, xticks=[], yticks=[])
the_table = plt.table(cellText=vals, rowLabels=df['Model'].to_list(), colLabels=df.columns,
                      loc='center', cellColours=colours)