On 22-Aug-2012 11:23, Virgil Stokes wrote:
> On 21-Aug-2012 17:59, Paul Hobson wrote:
>> On Tue, Aug 21, 2012 at 8:56 AM, Virgil Stokes <v...@it.uu.se> wrote:
>>> On 21-Aug-2012 17:50, Paul Hobson wrote:
>>>> On Tue, Aug 21, 2012 at 7:58 AM, Virgil Stokes <v...@it.uu.se> wrote:
>>>>> In reference to my previous email.
>>>>>
>>>>> How can I find the outliers (samples points beyond the whiskers) in the
>>>>> data
>>>>> used for the boxplot?
>>>>>
>>>>> Here is a code snippet that shows how it was used for the timings data (a
>>>>> list
>>>>> of 4 sublists (y1,y2,y3,y4), each containing 400,000 real data values),
>>>>> ...
>>>>> ...
>>>>> ...
>>>>> # Box Plots
>>>>> plt.subplot(2,1,2)
>>>>> timings = [y1,y2,y3,y4]
>>>>> pos = np.array(range(len(timings)))+1
>>>>> bp = plt.boxplot( timings, sym='k+', patch_artist=True,
>>>>> positions=pos, notch=1, bootstrap=5000 )
>>>>>
>>>>> plt.xlabel('Algorithm')
>>>>> plt.ylabel('Exection time (sec)')
>>>>> plt.ylim(0.9*ymin,1.1*ymax)
>>>>>
>>>>> plt.setp(bp['whiskers'], color='k', linestyle='-' )
>>>>> plt.setp(bp['fliers'], markersize=3.0)
>>>>> plt.title('Box plots (%4d trials)' %(n))
>>>>> plt.show()
>>>>> ...
>>>>> ...
>>>>> ...
>>>>>
>>>>> Again my questions:
>>>>> 1) How to get the value of the median?
>>>>> 2) How to find the outliers (outside the whiskers)?
>>>>> 3) How to find the width of the notch?
>>>> Virgil, the objects stuffed inside the `bp` dictionary should have
>>>> methods to retrieve their values. Let's see:
>>>>
>>>> In [35]: x = np.random.lognormal(mean=1.25, sigma=1.35, size=(37,3))
>>>>
>>>> In [36]: bp = plt.boxplot(x, bootstrap=5000, notch=True)
>>>>
>>>> In [37]: # Question 1
>>>> ...: print('medians')
>>>> ...: for n, median in enumerate(bp['medians']):
>>>> ...: print('%d: %f' % (n, median.get_ydata()[0]))
>>>> ...:
>>>> medians
>>>> 0: 6.339692
>>>> 1: 3.449320
>>>> 2: 4.503706
>>>>
>>>> In [38]: # Question 2
>>>> ...: print('fliers')
>>>> ...: for n in range(0, len(bp['fliers']), 2):
>>>> ...: print('%d: upper outliers = \t' % (n/2,))
>>>> ...: print(bp['fliers'][n].get_ydata())
>>>> ...: print('\n%d: lower outliers = \t' % (n/2,))
>>>> ...: print(bp['fliers'][n+1].get_ydata())
>>>> ...: print('\n')
>>>> ...:
>>> You had no outliers!
>>>
>>>> In [39]: # Question 3
>>>> ...: print('Confidence Intervals')
>>>> ...: for n, box in enumerate(bp['boxes']):
>>>> ...: print('%d: lower CI: %f' % (n, box.get_ydata()[2]))
>>>> ...: print('%d: upper CI: %f' % (n, box.get_ydata()[4]))
>>>> ...:
>>>> Confidence Intervals
>>>> 0: lower CI: 1.760701
>>>> 0: upper CI: 10.102221
>>>> 1: lower CI: 1.626386
>>>> 1: upper CI: 5.601927
>>>> 2: lower CI: 2.173173
>>>>
>>>> Hope that helps,
>>>> -paul
>>> Just what I was looking for Paul! Thanks very much.
>>>
>>> One final question --- Where can I find the documentation that answers my
>>> questions and gives more details about the equations used for the width of
>>> notch. etc.?
>>>
>>> Thanks again :-)
>> That should all be in the boxplot docstring. Do you use ipython? If
>> not, you should :)
>>
>> if so, just do `plt.boxplot?` at the ipython terminal and it'll show up.
>> -paul
> I still have a problem...
> Let me show the updated code snippet again
> ...
> ...
> ...
> # Box Plots
> iplt += 1
> plt.figure(iplt)
> timings = [ya[0],ya[1],ya[2],ya[3]]
> pos = np.array(range(len(timings)))+1
> bp = plt.boxplot( timings, sym='k+', patch_artist=True,
> positions=pos, notch=1, bootstrap=5000 )
> print ('medians')
> for nn,median in enumerate(bp['medians']):
> print('%d: %f' %(nn,median.get_ydata()[0]))
>
> print('fliers')
> for nn in range(0, len(bp['fliers']), 2):
> print('%d: upper outliers = \t' % (nn/2,))
> print(bp['fliers'][nn].get_ydata())
> print('\n%d: lower outliers = \t' % (nn/2,))
> print(bp['fliers'][nn+1].get_ydata())
> print('\n')
>
> print('Confidence Intervals')
> for nn, box in enumerate(bp['boxes']):
> print('%d: lower CI: %f' % (nn, box.get_ydata()[2]))<--- FAILS!
> print('%d: upper CI: %f' % (nn, box.get_ydata()[4]))
> ...
> ...
> ...
>
> Medians and fliers work perfectly; but, I get the following error message when
> trying to access the confidence intervals:
>
> AttributeError: 'PathPatch' object has no attribute 'get_ydata'
>
> Note, I am using boxplot with 4 sets of data and I am using matplotlib vers.
> 1.1.0.
>
> Any suggestions on how to fix this problem?
I found the solution,
one must have,
patch_artist=False
in the boxplot call.
:-)
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