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

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

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?

Ooops. Here's my reply -- this time to whole list
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')
    ...:

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