I have values with extreme outliers and want to visualize that. But the box plot doesn't seem a good choice for my data as you can see here.enter image description here

Most of the values are less than 50,000. But some them are over 1 million. .

What type of graph/figure is a good choice for data like this?

Here is an MWE creating that data

#!/usr/bin/env python3
import seaborn as sns
import matplotlib.pyplot as plt
import numpy as np

# 40.000 values from 500 to 10.000
vals = np.random.choice(range(500, 10000), 40000, replace=True)
# 2.000 values from 20.000 to 100.000
vals = np.append(vals, np.random.choice(range(20000, 100000), 2000, replace=True))
# 300 values (extrem outliners) from 1 to 4 Mio.
vals = np.append(vals, np.random.choice(range(1000000, 4000000), 300, replace=True))


EDIT: The example data is not contrived. This distribution is very near to a real data set.

EDIT 2: The values are currencies (in €); costs. And of course I will dive deeper into the data to find out why some persons cause so much more costs then others.

  • 2
    $\begingroup$ With so much data I wonder why you'd want to use a box plot in the first place. If one doesn't know the mechanism for generating the data, why not use the log of the data and create either a histogram or a nonparametric density estimate? $\endgroup$
    – JimB
    Commented Jul 28, 2022 at 17:38
  • $\begingroup$ Would be nice if you could give an example about that based on my example data. $\endgroup$
    – buhtz
    Commented Jul 28, 2022 at 18:13
  • 1
    $\begingroup$ "What to do" in statistics depends on the context (as opposed to just the numbers). Given that the example data mimics the real data, what causes the large gaps between the 3 sets of numbers? You might get better responses if information about the background and the objectives are outlined. $\endgroup$
    – JimB
    Commented Jul 28, 2022 at 21:12

1 Answer 1


I think your example data is a bit contrived but all you need to consider is constructing a histogram or nonparametric density estimate on the log of the "extreme" data. (If you data contains negative values, then something else will need to be used.)

I don't know python but I assume there must be standard functions to produce such displays. In R the (essentially) equivalent commands would be the following:

# 40.000 values from 500 to 10.000
vals1 <- runif(40000, 500, 10000)
# 2.000 values from 20.000 to 100.000
vals2 <- runif(2000, 20000, 100000)
# 300 values (extrem outliners) from 1 to 4 Mio.
vals3 <- runif(300, 1000000, 4000000)
vals <- c(vals1, vals2, vals3)

# Histogram
hist(log(vals), breaks="Freedman-Diaconis", xlim=c(6,16), ylim=c(0,1), req=FALSE, 
axis(1, 2*c(3:8), pos=0)
axis(2, c(0:10)/10, las=1)

# Nonparametric density estimate
lines(density(log(vals)), col="red", lwd=3)

Histogram and nonparametric density estimate of log of the data

Even a box plot looks a bit better with using logs (but you're still losing insight into the distribution of the data given that there are so many data points that allows a more complete description of the data):


boxplot of log of data

  • $\begingroup$ Mhm... What is about log(). I can look into my old(!) school notes and see the formulas what it is. But this doesn't help me to understand why log() makes sense here. What do I see? How could this be described in words? Why not use the original values but there log()? What is a log() of a value? $\endgroup$
    – buhtz
    Commented Jul 29, 2022 at 7:01
  • 1
    $\begingroup$ Costs and money values more generally are often best thought of on logarithmic scale. The fact that wage and price changes are often cited as percents is part of the same idea, that change and comparison are often multiplicative rather than additive.. There are any number of websites and popular books explaining logarithms. $\endgroup$
    – Nick Cox
    Commented Jul 29, 2022 at 12:12

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