

Hi all,
I have the following array:
x = np.array([1920.5508, 513.4158, 1071.6049, 1412.2137, 1378.3534, 561.4679])
when I put it into a boxplot it shows a boxplot with values that I would get from np.percentile:
np.percentile(x,[0,25,50,75,100],interpolation='linear')
>>>array([ 513.4158 , 689.00215 , 1224.97915 , 1403.748625, 1920.5508 ])
I would like for the 1st and 3rd quartile to be calculated with a different interpolation and then display these different values in the boxplot, i.e.
np.percentile(x,25,interpolation='lower') >>>561.4679
np.percentile(x,75,interpolation='higher') >>>1412.2137
Does anyone know if matplotlib can set which type of interpolation to use when it's calculating the percentiles?
Thanks for your help!
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Matplotlib's boxplot function has two steps:
1) pass the data to cbook.boxplots_stats 2) pass the stats to Axes.bxp
We did it this way so that people could skip step one and use their own stats with Axes.bxp.
Axes.bxp expects a list of dictionaries with the following keys:
'label', 'mean', 'iqr', 'cilo', 'cihi', 'whishi', 'whislo', 'fliers', 'q1', 'med', 'q3'
E.g, cilo & cihi are optional if you're not drawing the notch.
We documented it here:
I use this feature myself in a library I wrote for work: Compute custom boxplot stats: Pass that list of dictionaries to Axes.bxp:
On Tue, Jan 22, 2019 at 12:13 PM Matthew Bradley < [hidden email]> wrote: Hi all,
I have the following array:
x = np.array([1920.5508, 513.4158, 1071.6049, 1412.2137, 1378.3534, 561.4679])
when I put it into a boxplot it shows a boxplot with values that I would get from np.percentile:
np.percentile(x,[0,25,50,75,100],interpolation='linear')
>>>array([ 513.4158 , 689.00215 , 1224.97915 , 1403.748625, 1920.5508 ])
I would like for the 1st and 3rd quartile to be calculated with a different interpolation and then display these different values in the boxplot, i.e.
np.percentile(x,25,interpolation='lower') >>>561.4679
np.percentile(x,75,interpolation='higher') >>>1412.2137
Does anyone know if matplotlib can set which type of interpolation to use when it's calculating the percentiles?
Thanks for your help!
_______________________________________________
Matplotlibusers mailing list
[hidden email]
https://mail.python.org/mailman/listinfo/matplotlibusers
_______________________________________________
Matplotlibusers mailing list
[hidden email]
https://mail.python.org/mailman/listinfo/matplotlibusers


Matplotlib's boxplot function has two steps:
1) pass the data to cbook.boxplots_stats 2) pass the stats to Axes.bxp
We did it this way so that people could skip step one and use their own stats with Axes.bxp.
Axes.bxp expects a list of dictionaries with the following keys:
'label', 'mean', 'iqr', 'cilo', 'cihi', 'whishi', 'whislo', 'fliers', 'q1', 'med', 'q3'
E.g, cilo & cihi are optional if you're not drawing the notch.
We documented it here:
I use this feature myself in a library I wrote for work: Compute custom boxplot stats: Pass that list of dictionaries to Axes.bxp:
On Tue, Jan 22, 2019 at 12:13 PM Matthew Bradley < [hidden email]> wrote: Hi all,
I have the following array:
x = np.array([1920.5508, 513.4158, 1071.6049, 1412.2137, 1378.3534, 561.4679])
when I put it into a boxplot it shows a boxplot with values that I would get from np.percentile:
np.percentile(x,[0,25,50,75,100],interpolation='linear')
>>>array([ 513.4158 , 689.00215 , 1224.97915 , 1403.748625, 1920.5508 ])
I would like for the 1st and 3rd quartile to be calculated with a different interpolation and then display these different values in the boxplot, i.e.
np.percentile(x,25,interpolation='lower') >>>561.4679
np.percentile(x,75,interpolation='higher') >>>1412.2137
Does anyone know if matplotlib can set which type of interpolation to use when it's calculating the percentiles?
Thanks for your help!
_______________________________________________
Matplotlibusers mailing list
[hidden email]
https://mail.python.org/mailman/listinfo/matplotlibusers

_______________________________________________
Matplotlibusers mailing list
[hidden email]
https://mail.python.org/mailman/listinfo/matplotlibusers

