Example DataFrame Values -
0 78
1 38
2 42
3 48
4 31
5 89
6 94
7 102
8 122
9 122
stats.percentileofscore(temp[\'INCOME\'].v
This seems to work:
A = np.sort(temp['INCOME'].values)
np.interp(sample, A, np.linspace(0, 1, len(A)))
For example:
>>> temp.INCOME.quantile(np.interp([37.5, 38, 122, 121], A, np.linspace(0, 1, len(A))))
0.103175 37.5
0.111111 38.0
1.000000 122.0
0.883333 121.0
Name: INCOME, dtype: float64
Please note that this strategy only makes sense if you want to query a large enough number of values. Otherwise the sorting is too expensive.