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From: Virgil S. <vs...@it...> - 2012-08-21 14:58:28
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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?
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From: Jeffrey B. <jbl...@al...> - 2012-08-21 15:52:39
|
On Aug 21, 2012, at 10:58 AM, Virgil Stokes 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?
This is easily calculated from your data. Numpy will even do it for
you: np.median(timings)
> 2) How to find the outliers (outside the whiskers)?
From the boxplot documentation: the whiskers extend to the most
extreme data point within distance X of the bottom or top of the box,
where X is 1.5 times the extent of the box. Any points more extreme
than that are the outliers. The box itself of course extends from the
25th percentile to the 75th percentile of your data. Again, you can
easily calculate these values from your data.
> 3) How to find the width of the notch?
Again, from the docs: with bootstrap=5000, it calculates the width of
the notch by bootstrap resampling your data (the timings array) 5000
times and finding the 95% confidence interval of the median, and uses
that as the notch width. You can redo that yourself pretty easily.
Here is some bootstrap code for you to adapt:
http://mail.scipy.org/pipermail/scipy-user/2009-July/021704.html
I encourage you to read the documentation! This page is very useful
for reference:
http://matplotlib.sourceforge.net/api/pyplot_api.html
-Jeff
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From: Paul H. <pmh...@gm...> - 2012-08-21 15:55:22
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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
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From: Paul H. <pmh...@gm...> - 2012-08-21 15:59:09
|
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
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From: Virgil S. <vs...@it...> - 2012-08-22 09:23:55
|
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?
|
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From: Virgil S. <vs...@it...> - 2012-08-22 14:24:51
|
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 <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?
I found the solution,
one must have,
patch_artist=False
in the boxplot call.
:-)
|
|
From: Virgil S. <vs...@it...> - 2012-08-22 14:04:10
|
On 21-Aug-2012 17:52, Jeffrey Blackburne wrote:
>
> On Aug 21, 2012, at 10:58 AM, Virgil Stokes 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?
>
> This is easily calculated from your data. Numpy will even do it for you:
> np.median(timings)
>
>> 2) How to find the outliers (outside the whiskers)?
>
> From the boxplot documentation: the whiskers extend to the most extreme data
> point within distance X of the bottom or top of the box, where X is 1.5 times
> the extent of the box. Any points more extreme than that are the outliers. The
> box itself of course extends from the 25th percentile to the 75th percentile
> of your data. Again, you can easily calculate these values from your data.
>
>> 3) How to find the width of the notch?
>
> Again, from the docs: with bootstrap=5000, it calculates the width of the
> notch by bootstrap resampling your data (the timings array) 5000 times and
> finding the 95% confidence interval of the median, and uses that as the notch
> width. You can redo that yourself pretty easily. Here is some bootstrap code
> for you to adapt:
> http://mail.scipy.org/pipermail/scipy-user/2009-July/021704.html
>
> I encourage you to read the documentation! This page is very useful for
> reference:
> http://matplotlib.sourceforge.net/api/pyplot_api.html
>
> -Jeff
>
Yes Jeff,
These are very useful links; however, box plots have a parameter called the
"adjacent value" (from the McGill reference),
"The plotted whisker extends to the adjacent value, which is the most extreme
data value that is not an outlier."
It seems there should be one for the lower and one for the upper whisker --- how
can one get these two values from boxplot?
Also, is there anyway to directly get the indices of the outliers?
|
|
From: Jeffrey B. <jbl...@al...> - 2012-08-22 15:29:43
|
On Aug 22, 2012, at 10:04 AM, Virgil Stokes wrote:
> On 21-Aug-2012 17:52, Jeffrey Blackburne wrote:
>>
>> On Aug 21, 2012, at 10:58 AM, Virgil Stokes 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?
>>
>> This is easily calculated from your data. Numpy will even do it
>> for you: np.median(timings)
>>
>>> 2) How to find the outliers (outside the whiskers)?
>>
>> From the boxplot documentation: the whiskers extend to the most
>> extreme data point within distance X of the bottom or top of the
>> box, where X is 1.5 times the extent of the box. Any points more
>> extreme than that are the outliers. The box itself of course
>> extends from the 25th percentile to the 75th percentile of your
>> data. Again, you can easily calculate these values from your data.
>>
>>> 3) How to find the width of the notch?
>>
>> Again, from the docs: with bootstrap=5000, it calculates the width
>> of the notch by bootstrap resampling your data (the timings array)
>> 5000 times and finding the 95% confidence interval of the median,
>> and uses that as the notch width. You can redo that yourself
>> pretty easily. Here is some bootstrap code for you to adapt:
>> http://mail.scipy.org/pipermail/scipy-user/2009-July/021704.html
>>
>> I encourage you to read the documentation! This page is very
>> useful for reference:
>> http://matplotlib.sourceforge.net/api/pyplot_api.html
>>
>> -Jeff
>>
> Yes Jeff,
> These are very useful links; however, box plots have a parameter
> called the "adjacent value" (from the McGill reference),
>
> "The plotted whisker extends to the adjacent value, which is the
> most extreme data value that is not an outlier."
>
> It seems there should be one for the lower and one for the upper
> whisker --- how can one get these two values from boxplot?
Look at bp['whiskers']
For those who got here by searching: bp is the object returned by
plt.boxplot()
> Also, is there anyway to directly get the indices of the outliers?
Look into np.where()
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