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Matplotlib boxplot using pre-calculated (summary) statistics

I need to make boxplot (in Python and matplotlib), but I do not have the original raw data. I have pre-calculated values ​​for max, min, mean, median and IQR (normal distribution), but still I would like to make boxplot. Of course, overstating emissions is impossible, but besides, I think all the information is there.

I searched everything to find an answer without success. The closest I came, this is the same question, but for R (which I am not familiar with). See Is it possible to easily plot a graph from previously calculated statistics (in R?)

Can someone show me how to make boxplot?

Many thanks!

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3 answers

In older versions, you had to do this manually by modifying the boxplot elements individually:

Mean=[3.4] #mean IQR=[3.0,3.9] #inter quantile range CL=[2.0,5.0] #confidence limit A=np.random.random(50) D=plt.boxplot(A) # a simple case with just one variable to boxplot D['medians'][0].set_ydata(Mean) D['boxes'][0]._xy[[0,1,4], 1]=IQR[0] D['boxes'][0]._xy[[2,3],1]=IQR[1] D['whiskers'][0].set_ydata(np.array([IQR[0], CL[0]])) D['whiskers'][1].set_ydata(np.array([IQR[1], CL[1]])) D['caps'][0].set_ydata(np.array([CL[0], CL[0]])) D['caps'][1].set_ydata(np.array([CL[1], CL[1]])) _=plt.ylim(np.array(CL)+[-0.1*np.ptp(CL), 0.1*np.ptp(CL)]) #reset the limit

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Thanks to @tacaswell's comment, I was able to find the necessary documentation and create an example using Matplotlib 1.4.3. However, this example does not automatically scale the shape to the correct size.

 import matplotlib.pyplot as plt item = {} item["label"] = 'box' # not required item["mean"] = 5 # not required item["med"] = 5.5 item["q1"] = 3.5 item["q3"] = 7.5 #item["cilo"] = 5.3 # not required #item["cihi"] = 5.7 # not required item["whislo"] = 2.0 # required item["whishi"] = 8.0 # required item["fliers"] = [] # required if showfliers=True stats = [item] fig, axes = plt.subplots(1, 1) axes.bxp(stats) axes.set_title('Default') y_axis = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9] y_values = ["0", "1", "2", "3", "4", "5", "6", "7", "8", "9"] plt.yticks(y_axis, y_values)

Relevant documentation links:

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Referring to the answer of @MKroehnert and Boxplot drawer function at https://matplotlib.org/gallery/statistics/bxp.html , the following may be useful:

 import matplotlib.pyplot as plt stats = [{ "label": 'A', # not required "mean": 5, # not required "med": 5.5, "q1": 3.5, "q3": 7.5, # "cilo": 5.3 # not required # "cihi": 5.7 # not required "whislo": 2.0, # required "whishi": 8.0, # required "fliers": [] # required if showfliers=True }] fs = 10 # fontsize fig, axes = plt.subplots(nrows=1, ncols=1, figsize=(6, 6), sharey=True) axes.bxp(stats) axes.set_title('Boxplot for precalculated statistics', fontsize=fs) plt.show()
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