Python 带Pythyon的风险矩阵

我正在尝试使用Python做一个风险矩阵，它集成了严重性和概率，我已经尝试使用热图，这可能是我迄今为止发现的最接近的一种图形，但我认为它并不代表风险矩阵的基本结构。下一幅图显示了我想要的矩阵图。我会很感激任何建议：图书馆，链接…任何能够绘制风险矩阵的东西

这是我试图在风险矩阵中找到的数据：

|---------------------|------------------|----------------------|
|      Component      |        KPI       |     Classification             
|---------------------|------------------|----------------------|
|          12         |         34       |    High Criticality
|---------------------|------------------|----------------------|
          Start                 38            High Criticality
|---------------------|------------------|----------------------|
         Fusela                 45            Low Criticality
|---------------------|------------------|----------------------|
          Hyd                   50           Medium Criticality
|---------------------|------------------|----------------------|
          Damp                  51           Medium Criticality
|---------------------|------------------|----------------------|
         Turbine                62           High Criticality
|---------------------|------------------|----------------------|
        Intercon                65          Medium Criticality
|---------------------|------------------|----------------------|
       Main Rotor               90           High Criticality
|---------------------|------------------|----------------------|
         AM-19                  93            High Criticality
|---------------------|------------------|----------------------|
      Main Trans                98            High Criticality
|---------------------|------------------|----------------------|

这是我已经用热图实现的代码：

import matplotlib.pyplot as plt

data = data.sort_values(by = 'KPI', ascending = False)
x = 1
for element in list(data['Componente']):
    data['Componente'] = data['Componente'].str.replace(str(element),'{}. 
{}'.format(str(x),element))
    x = x + 1
data['Clasificación'] = data['Clasificación'].str.replace('Criticidad 
Alta','1. Criticidad Alta').str.replace('Criticidad Media','2. Criticidad 
Media').str.replace('Criticidad Baja', '3. Criticidad Baja')
result = data.pivot(index='Componente',columns= 'Clasificacion', values = 
'KPI')
sb.heatmap(result, annot= True ,cmap='RdYlGn' ,fmt=".1f", vmax=100)
plt.figtext(.5,.9,'RESULTADO MATRIZ RIESGO', fontsize=14, ha='center')
plt.show()

我想要的输出类似于下一个成像仪：

---

以下是plt.imshow和plt.annotate的创意：

# function to make risk matrix
def make_risk_matrix(shape=3,levels=3):
    matrix = np.zeros((shape, shape))
    for level in range(levels):
        matrix[np.triu_indices(shape, level)] += 1
    return matrix

# specify bin borders and respective positions
likelihood_bins = [100,86,60]
positions = [0,1,2]

for position, likelihood in zip(positions, likelihood_bins):
    df.loc[df['KPI']<=likelihood, 'y'] = position

# generate x-positions from classification column
df['x'] = df['Classification'].replace({'High':2, 'Medium':1, 'Low':0})

# default offset for x -position
x_offset = -.4

# generate risk matrix and display as image
risk_matrix = make_risk_matrix()
plt.imshow(risk_matrix, cmap='RdYlGn_r')

# write individual components on it
# as some components will end up in hte same bin,
# caculate y-offset on the fly
for group in df.groupby(['x', 'y']):
    y_offset = -.3
    for ix, row in group[1].iterrows():

        plt.annotate(
            row['Component'], 
            xy=(
                row['x']+x_offset,
                row['y']+y_offset
                )
            )
        y_offset +=.15 # update y_offset

plt.xlabel('Consequence')
plt.ylabel('Likelihood')

---

以下是plt.imshow和plt.annotate的创意：

# function to make risk matrix
def make_risk_matrix(shape=3,levels=3):
    matrix = np.zeros((shape, shape))
    for level in range(levels):
        matrix[np.triu_indices(shape, level)] += 1
    return matrix

# specify bin borders and respective positions
likelihood_bins = [100,86,60]
positions = [0,1,2]

for position, likelihood in zip(positions, likelihood_bins):
    df.loc[df['KPI']<=likelihood, 'y'] = position

# generate x-positions from classification column
df['x'] = df['Classification'].replace({'High':2, 'Medium':1, 'Low':0})

# default offset for x -position
x_offset = -.4

# generate risk matrix and display as image
risk_matrix = make_risk_matrix()
plt.imshow(risk_matrix, cmap='RdYlGn_r')

# write individual components on it
# as some components will end up in hte same bin,
# caculate y-offset on the fly
for group in df.groupby(['x', 'y']):
    y_offset = -.3
    for ix, row in group[1].iterrows():

        plt.annotate(
            row['Component'], 
            xy=(
                row['x']+x_offset,
                row['y']+y_offset
                )
            )
        y_offset +=.15 # update y_offset

plt.xlabel('Consequence')
plt.ylabel('Likelihood')

---

请张贴您的密码和密码data@seralok嗨，现在我加载数据和code@QuangHoang完成！现在还不清楚你希望矩阵是什么样子？就您的数据而言，x轴、y轴、每个单元格上都有什么？数据的哪一部分决定了可能性？请发布您的代码并data@seralok嗨，现在我加载数据和code@QuangHoang完成！现在还不清楚你希望矩阵是什么样子？x轴，y轴，每个单元格上的数据是什么？数据的哪一部分决定了可能性？这就是我需要的，我只需要更改轴的名称，你知道这是否可能吗？谢谢你的时间和代码这正是我需要的这是我需要的，我只需要更改轴的名称，你知道这是否可能吗？谢谢你的时间和代码，这正是我需要的