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2025年基尼辛普森指数衡量多样性

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基尼辛普森指数衡量多样性Simpson index The measure equals the probability that two entities taken at random from the dataset with replacement represent the same type where is the total number of types in the dataset

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Simpson index

\lambda =\sum_{i=1}^{R}p_{i}^{2}
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The measure equals the probability that two entities taken at random from the dataset (with replacement) represent the same type, where R is the total number of types in the dataset.

 

Gini–Simpson index

The transformation 1-\lambda equals the probability that the two entities represent different types.

分布越均衡，该指数越高；分布越集中，该指数越低。

 

Code

import pandas as pd def gini_calc(df2): sum_ = sum_square = 0 sum_ = df2['cnt'].sum() df2['cnt_prop']=df2['cnt'].apply(lambda x :x/sum_) for i in df2['cnt_prop']: sum_square += i2 return 1-sum_square df = pd.read_excel('gini.xlsx') df=df.groupby([df['population'],df['subpopulation'],df['type']],as_index=False).sum() a=[] b=[] c=[] for name,group in df.groupby([df['population'],df['subpopulation']]): index = gini_calc(group) a.append(name[0]) b.append(name[1]) c.append(index) res={"population":a, "subpopulation":b, "gini_simpson_index":c} data=pd.DataFrame(res) result=data.to_csv('gini_result.csv')

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