Is it possible to draw a matplotlib boxplot given the percentile values instead of the original inputs?

Question

From what I can see, boxplot() method expects a sequence of raw values (numbers) as input, from which it then computes percentiles to draw the boxplot(s).

I would like to have a method by which I could pass in the percentiles and get the corresponding boxplot.

For example:

Assume that I have run several benchmarks and for each benchmark I've measured latencies ( floating point values ). Now additionally, I have precomputed the percentiles for these values.

Hence for each benchmark, I have the 25th, 50th, 75th percentile along with the min and max.

Now given these data, I would like to draw the box plots for the benchmarks.

Answer 1

As of 2020, there is a better method than the one in the accepted answer.

The matplotlib.axes.Axes class provides a bxp method, which can be used to draw the boxes and whiskers based on the percentile values. Raw data is only needed for the outliers, and that is optional.

Example:

import matplotlib.pyplot as plt  fig, ax = plt.subplots() boxes = [     {         'label' : "Male height",         'whislo': 162.6,    # Bottom whisker position         'q1'    : 170.2,    # First quartile (25th percentile)         'med'   : 175.7,    # Median         (50th percentile)         'q3'    : 180.4,    # Third quartile (75th percentile)         'whishi': 187.8,    # Top whisker position         'fliers': []        # Outliers     } ] ax.bxp(boxes, showfliers=False) ax.set_ylabel("cm") plt.savefig("boxplot.png") plt.close() 

This produces the following image: