Eliminating all data over a given percentile

Solution 1:

Use the Series.quantile() method:

In [48]: cols = list('abc')

In [49]: df = DataFrame(randn(10, len(cols)), columns=cols)

In [50]: df.a.quantile(0.95)
Out[50]: 1.5776961953820687

To filter out rows of df where df.a is greater than or equal to the 95th percentile do:

In [72]: df[df.a < df.a.quantile(.95)]
Out[72]:
       a      b      c
0 -1.044 -0.247 -1.149
2  0.395  0.591  0.764
3 -0.564 -2.059  0.232
4 -0.707 -0.736 -1.345
5  0.978 -0.099  0.521
6 -0.974  0.272 -0.649
7  1.228  0.619 -0.849
8 -0.170  0.458 -0.515
9  1.465  1.019  0.966

Solution 2:

numpy is much faster than Pandas for this kind of things :

numpy.percentile(df.a,95) # attention : the percentile is given in percent (5 = 5%)

is equivalent but 3 times faster than :

df.a.quantile(.95)  # as you already noticed here it is ".95" not "95"

so for your code, it gives :

df[df.a < np.percentile(df.a,95)]

Solution 3:

You can use query for a more concise option:

df.query('ms < ms.quantile(.95)')