我有一个看起来像这样的熊猫数据框：
**I SI weights**
1 3 0.3
2 4 0.2
1 3 0.5
1 5 0.5
我需要这样做：给定I值，考虑SI的每个值并加上总重量。最后，对于每个实现，我都应该有这样的东西：
I = 1 SI = 3 weight = 0.8
SI = 5 weight = 0.5
I = 2 SI = 4 weight = 0.2
这可以通过调用groupby和sum轻松实现：
name = ['I', 'SI','weight']
Location = 'Simulationsdata/prova.csv'
df = pd.read_csv(Location, names = name,sep='\t',encoding='latin1')
results = df.groupby(['I', 'real', 'SI']).weight.sum()
现在，我希望将权重归一化，这样权重应该是这样的：
I = 1 SI = 3 weight = 0.615
SI = 5 weight = 0.385
I = 2 SI = 4 weight = 1
我尝试了这个：
for idx2, j in enumerate(results.index.get_level_values(1).unique()):
norm = [float(i)/sum(results.loc[j]) for i in results.loc[j]]
但是当我尝试为每个I绘制SI的分布时，我发现SI也被标准化了，我不希望这种情况发生。
PS这个问题是关系到这一个，但是，因为它涉及到这个问题的另一个方面，我因子评分，这将是最好分头问吧
您应该可以将weight列除以自己的总和：
# example data
df
I SI weight
0 1 3 0.3
1 2 4 0.2
2 1 3 0.5
3 1 5 0.5
# two-level groupby, with the result as a DataFrame instead of Series:
# df['col'] gives a Series, df[['col']] gives a DF
res = df.groupby(['I', 'SI'])[['weight']].sum()
res
weight
I SI
1 3 0.8
5 0.5
2 4 0.2
# Get the sum of weights for each value of I,
# which will serve as denominators in normalization
denom = res.groupby('I')['weight'].sum()
denom
I
1 1.3
2 0.2
Name: weight, dtype: float64
# Divide each result value by its index-matched
# denominator value
res.weight = res.weight / denom
res
weight
I SI
1 3 0.615385
5 0.384615
2 4 1.000000
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