我有以下 df
country street postcode id
SA XX0 1
GB 17 abc road 2
BE 129 def street 127 3
US nan nan 4
我想计算的值的熵country，street和postcode; 空字符串或NaN将0.25默认获得值；
from entropy import shannon_entropy
vendor_fields_to_measure_entropy_on = ('country', 'vendor_name', 'town', 'postcode', 'street')
fields_to_update = tuple([key + '_entropy_val' for key in vendor_fields_to_measure_entropy_on])
for fields in zip(vendor_fields_to_measure_entropy_on, fields_to_update):
entropy_score = []
for item in df[fields[0]].values:
item_as_str = str(item)
if len(item_as_str) > 0 and item_as_str != 'NaN':
entropy_score.append(shannon_entropy(item_as_str))
else:
entropy_score.append(.25)
df[fields[1]] = entropy_score
我想知道什么是最好的方法，所以结果看起来像是，
country street postcode id
SA XX0 1
GB 17 abc road 2
BE 129 def street 127 3
US nan nan 4
country_entropy_val street_entropy_val postcode_entropy_val
0.125 0.25 0.11478697512328288
0.125 0.38697440929431765 0.25
0.125 0.39775073104910885 0.19812031562256
0.125 0.25 0.25
>>> fields = ['country', 'street', 'postcode']
>>> for col in fields:
... df[f'{col}_entropy'] = df[col].apply(lambda x: shannon_entropy(str(x)) if not pd.isna(x) else 0.25)
...
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我来说两句