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