pandas 分组比:高于阈值的百分比

问题描述

我有一个想要在其上使用groupby的DataFrame，但我正在寻找一些不寻常的函数来进行汇总.我想使每个组中观察值的百分比高于某个阈值.例如，如果阈值为0，则DataFrame

I have a DataFrame that I'm looking to use a groupby on but I'm looking for a little bit of an unusual function to aggregate with. I would like to get the percentage of observations in each group above a certain threshold. For example, with a threshold of 0, the DataFrame

df = pd.DataFrame(dict(day=[1, 1, 1, 2, 2, 2, 3, 3, 3, 4], value=[0, 4, 0, 4, 0, 4, 0, 4, 0, 4]))

df
   day  value
0    1      0
1    1      4
2    1      0
3    2      4
4    2      0
5    2      4
6    3      0
7    3      4
8    3      0
9    4      4

应该成为

df_group = pd.DataFrame(dict(day=[1, 2, 3, 4], value=[.33, .67, .33, 1.0]))

df_group
   day  value
0    1   0.33
1    2   0.67
2    3   0.33
3    4   1.00

我还在处理相当大的数据集，因此，我希望将计算时间考虑在内.

I am also working with a fairly large data set, so I'd appreciate taking computation time into account.

推荐答案

>>> df.groupby('day')['value'].apply(lambda c: (c>0).sum()/len(c))
day
1      0.333333
2      0.666667
3      0.333333
4      1.000000
Name: value, dtype: float64

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