I tried the following (most output text is deleted):
In [1]: ob1=[1,1,2,2,1,2,4,3,2,2,2,3,4,5,6,7,8,9,7,6,4,5,5]
In [2]: import matplotlib.pyplot as plt In [3]: plt.figure() In [4]: plt.boxplot(ob1) In [5]: plt.savefig('test.png') In [6]: import scipy.stats In [7]: scipy.stats.scoreatpercentile(ob1,75) Out[7]: 5.5
Note that the 75th percentile is 5.5. R agrees with this calculation. However, in the boxplot, the top of the box is around 6, not 5.5. Isn't the top of the box supposed to be at the 75th percentile?
prctile does not handle the case where the exact percentile lies between two items. scoreatpercentile does.
···
On 2009-09-14 13:49 PM, Gökhan Sever wrote:
On Mon, Sep 14, 2009 at 12:30 PM, <jason-sage@...2130... > <mailto:jason-sage@…2130…>> wrote:
I tried the following (most output text is deleted):
In [1]: ob1=[1,1,2,2,1,2,4,3,2,2,2,3,4,5,6,7,8,9,7,6,4,5,5]
In [2]: import matplotlib.pyplot as
plt
In [3]:
plt.figure()
In [4]:
plt.boxplot(ob1)
In [5]:
plt.savefig('test.png')
In [6]: import
scipy.stats
In [7]:
scipy.stats.scoreatpercentile(ob1,75)
Out[7]: 5.5
Note that the 75th percentile is 5.5. R agrees with this calculation.
However, in the boxplot, the top of the box is around 6, not 5.5. Isn't
the top of the box supposed to be at the 75th percentile?
Thanks,
Jason
--
Jason Grout
From matplotlib/lib/matplotlib/axes.py
You can see how matplotlib calculating percentiles. And yes it doesn't
conform with scipy's scoreatpercentile()
# get median and quartiles
q1, med, q3 = mlab.prctile(d,[25,50,75])
I[36]: q1
O[36]: 2.0
I[37]: med
O[37]: 4.0
I[38]: q3
O[38]: 6.0
Could this be due to a rounding? I don't know, but I am curious to hear
the explanations for this discrepancy.
--
Robert Kern
"I have come to believe that the whole world is an enigma, a harmless enigma
that is made terrible by our own mad attempt to interpret it as though it had
an underlying truth."
-- Umberto Eco
Now there are 3 different 75 percentiles :). Any ideas, which is one the most correct?
I have used matplotlib’s percentile outputs on some of my abstracts and posters, not yet in a paper. Not a big difference amongst them, but still makes me think, should I compare similar other function results with other programs when I do data analyses.
···
On Mon, Sep 14, 2009 at 3:45 PM, <jason-sage@…2781…> wrote:
Robert Kern wrote:
prctile does not handle the case where the exact percentile lies between two
items. scoreatpercentile does.
If mlab is supposed to be compatible with matlab, then isn’t this a problem?
Of course, the 75th percentile is different here too (5.75 instead of
scipy's 5.5). I don't know how to explain that discrepancy.
Jason
Now there are 3 different 75 percentiles :). Any ideas, which is one the
most correct?
--
Robert Kern
"I have come to believe that the whole world is an enigma, a harmless enigma
that is made terrible by our own mad attempt to interpret it as though it had
an underlying truth."
-- Umberto Eco
On Mon, Sep 14, 2009 at 12:30 PM, <jason-sage@...2130... >> <mailto:jason-sage@…2130…>> wrote:
I tried the following (most output text is deleted):
In [1]: ob1=[1,1,2,2,1,2,4,3,2,2,2,3,4,5,6,7,8,9,7,6,4,5,5]
In [2]: import matplotlib.pyplot as
plt
In [3]:
plt.figure()
In [4]:
plt.boxplot(ob1)
In [5]:
plt.savefig('test.png')
In [6]: import
scipy.stats
In [7]:
scipy.stats.scoreatpercentile(ob1,75)
Out[7]: 5.5
Note that the 75th percentile is 5.5. R agrees with this calculation.
However, in the boxplot, the top of the box is around 6, not 5.5. Isn't
the top of the box supposed to be at the 75th percentile?
Thanks,
Jason
--
Jason Grout
From matplotlib/lib/matplotlib/axes.py
You can see how matplotlib calculating percentiles. And yes it doesn't
conform with scipy's scoreatpercentile()
# get median and quartiles
q1, med, q3 = mlab.prctile(d,[25,50,75])
I[36]: q1
O[36]: 2.0
I[37]: med
O[37]: 4.0
I[38]: q3
O[38]: 6.0
Could this be due to a rounding? I don't know, but I am curious to hear
the explanations for this discrepancy.
prctile does not handle the case where the exact percentile lies between two
items. scoreatpercentile does.