jezrael
2020-01-31 20:00
如果性能是重要的用途GroupBy.transform与sum和除以原始列Series.div，最后通过多次Series.mul：
df['PERCENTAGE'] = df['counts'].div(df.groupby('KEY')['counts'].transform('sum')).mul(100)
您可以按组划分每个值，但是如果较大的DataFrame或许多组效果不佳：
df['PERCENTAGE'] = df.groupby('KEY')['counts'].transform(lambda x: x / x.sum()).mul(100)
print (df)
KEY DESCR counts PERCENTAGE
0 2 to A 1 50.000000
1 2 to B 1 50.000000
2 20 to C 1 100.000000
3 35 to D 2 100.000000
4 110 to E 4 66.666667
5 110 to F 1 16.666667
6 110 to G 1 16.666667
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.apply（math.floor）吗？
@koalaok或
np.floor(df.groupby('KEY')['counts'].transform(lambda x: x / x.sum()).mul(100))是np更快，更pythonic还是只是替代品？
@koalaok-它更快，因为numpy像纯python循环一样快