Python equivalent of Excel's PERCENTILE.EXC

I am using Pandas to compute some financial risk analytics, including Value at Risk. In short, to compute Value at Risk (VaR), you take a time series of simulated portfolio changes in value, and then compute a specific tail percentile loss. For example, 95% VaR is the 5th percentile figure in that time series.

I have my time series in a Pandas dataframe, and am currently using the pd.quantile() function to compute the percentile. My question is, typical market convention for VaR is use an exclusionary percentile (ie: 95% VaR is interpreted as: there is a 95% chance your portfolio will not loose MORE than the computed number) - akin to how MS Excel PERECENTILE.EXC() works. Pandas quantile() works akin to how Excel's PERCENTILE.INC() works - it includes the specified percentile. I have scoured several python math packages as well as this forum for a python solution that uses the same methodology as PERCENTILE.EXC() in Excel with no luck. I was hoping someone here might have a suggestion?

Here is sample code.

import pandas as pd
import numpy as np

test_pd = pd.Series([15,14,18,-2,6,-78,31,21,98,-54,-2,-36,5,2,46,-72,3,-2,7,9,34])
test_np = np.array([15,14,18,-2,6,-78,31,21,98,-54,-2,-36,5,2,46,-72,3,-2,7,9,34])

print 'pandas: ' + str(test_pd.quantile(.05))
print 'numpy: '+ str(np.percentile(test_np,5))

The answer i am looking for is -77.4




  • It won't be as efficient as Pandas' own percentile but it should work:

    def quantile_exc(ser, q):
        ser_sorted = ser.sort_values()
        rank = q * (len(ser) + 1) - 1
        assert rank > 0, 'quantile is too small'
        rank_l = int(rank)
        return ser_sorted.iat[rank_l] + (ser_sorted.iat[rank_l + 1] - 
                                         ser_sorted.iat[rank_l]) * (rank - rank_l)
    ser = pd.Series([15,14,18,-2,6,-78,31,21,98,-54,-2,-36,5,2,46,-72,3,-2,7,9,34])
    quantile_exc(ser, 0.05)
    Out: -77.400000000000006
    quantile_exc(ser, 0.1)
    Out: -68.399999999999991
    quantile_exc(ser, 0.3)
    Out: -2.0

    Note that Excel fails for small percentiles; it is not a bug. It is because ranks that go below the minimum value is not suitable for interpolation. So you might want to check if rank > 0 in the quantile_exc function (see the assertion part).