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

[Paul H. <pmh...@gm...>]

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