Re: [Matplotlib-users] boxplot -- how (more)

[Virgil S. <vs...@it...>]

On 21-Aug-2012 17:59, Paul Hobson wrote:
> On Tue, Aug 21, 2012 at 8:56 AM, Virgil Stokes <vs...@it...> wrote:
>> On 21-Aug-2012 17:50, Paul Hobson wrote:
>>> On Tue, Aug 21, 2012 at 7:58 AM, Virgil Stokes <vs...@it...> 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?