Risk Matrix Method in Python

The risk matrix, a popular risk assessment method ​1​, presents risks in the form of a likelihood / impact heatmap. In this post, I share a convenient and elegant Python snippet for constructing risk matrices, used to generate this hypothetical project risk assessment:

Below are the code details:

import pandas as pd
import matplotlib as mpl
import matplotlib.pyplot as plt
import numpy as np

likelihod_cols = ["Rare","Unlikely","Moderate","Likely","Almost certain"]
impact_cols = ["Insignificant","Minor","Significant","Major","Severe"]

score_vals = np.arange(len(impact_cols)).reshape(-1,1) + np.arange(len(likelihod_cols)).reshape(1,-1)
cmap = mpl.colormaps["RdYlGn"].reversed().resampled(9)
norm = mpl.colors.BoundaryNorm(range(10),10)

fig, ax = plt.subplots(figsize=(8,8))

annotations = {
    (2,3): ["competition"],
    (3,2): ["too ambitiuous"],
    (3,3): ["hardware expertise"],
    (2,2): ["time crunch"],
    (2,1): ["stretched resources"],

annots_style = {'fontweight':'bold'}
for (i,j),text in annotations.items():
  text = '\n'.join( text )
  _ = ax.text(i, j, text, ha="center", va="center", color="b", **annots_style)

ax.set_xticks(range(len(impact_cols)), labels=impact_cols)
ax.set_yticks(range(len(likelihod_cols)), labels=likelihod_cols)



  1. 1.
    Peace C. The risk matrix: uncertain results? Policy and Practice in Health and Safety. Published online July 3, 2017:131-144. doi:10.1080/14773996.2017.1348571

Published by mskorski

Scientist, Consultant, Learning Enthusiast

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