Reputation: 7358
I am trying to classify my data in percentile buckets based on their values. My data looks like,
a = pnd.DataFrame(index = ['a','b','c','d','e','f','g','h','i','j'], columns=['data'])
a.data = np.random.randn(10)
print a
print '\nthese are ranked as shown'
print a.rank()
data
a -0.310188
b -0.191582
c 0.860467
d -0.458017
e 0.858653
f -1.640166
g -1.969908
h 0.649781
i 0.218000
j 1.887577
these are ranked as shown
data
a 4
b 5
c 9
d 3
e 8
f 2
g 1
h 7
i 6
j 10
To rank this data, I am using the rank function. However, I am interested in the creating a bucket of the top 20%. In the example shown above, this would be a list containing labels ['c', 'j']
desired result : ['c','j']
How do I get the desired result
Upvotes: 17
Views: 11711
Reputation: 35255
In [13]: df[df > df.quantile(0.8)].dropna()
Out[13]:
data
c 0.860467
j 1.887577
In [14]: list(df[df > df.quantile(0.8)].dropna().index)
Out[14]: ['c', 'j']
Upvotes: 24