Visualize quartiles in Python

We have seen what are quartiles and how can be useful in quickly presenting the main characteristics of a group of data.

Let’s see how to visualise them.

I will use as example the age of the Nobel Prize winners – a discrete values set – from the Nobel Prize official site.

The best way to chart a data set with its quartiles is to use a box plot:

box plot example
Boxplot of a normal distributed population

a box that goes from the upper to the lower quartile, plus optionally lines (the whiskers) extending from the box that go until a specified multiplier of the Inter-Quartile Range (IQR = upper – lower quartiles), while any other point outside this range is considered an outlier data point and displayed as a point.
Inside the box the median and the mean can be displayed, as lines or points.

The matplotlib function to draw a boxplot is appropriately called boxplot() 

The function requires to pass as input data an array or a list of vectors, for example:

agePhysics = [ 25, 31, 31, 31, ... ]  # goes on for almost 200 values

The basic plot would be:

import matplotlib.pyplot as plt
# basic plot

The defaults used in the other parameters of the boxplot function are:

notch = False : draw a rectangular box, not notched
vert = True: the box is vertical, not horizontal
sym = None: no fliers displayed
whis = 1.5 : the multipliers from the whiskers variability, they go until whis * IQR

Now let’s print how much are the quartiles and the mean before plotting and display the mean by using the parameter showmeans (default is False), by adding/changing these lines:

from datascience import stats

 print("range = ", stats.range(agePhysics))
 plt.boxplot(agePhysics, showmeans=True, whis = 99)

Output printed is:

Summary statistics
Min: 25
Lower Qu.: 45.0
Median: 54.0
Mean: 54.955
Upper Qu.: 64.0
Max: 88
That's all

And the lines above display a box-and-whiskers chart like this:

A simple box plot
A simple box plot

As you see the box itself goes from the upper to the lower quartile (45 and 64 in this case), while the whiskers (the bars extending from the box) go from the minimum to the maximum (25 and 88 in this case) because whis is set to a very high number (99) therefore including all the data points.

The red line is the median (54) while the mean (similar value) is a red square but can be changed through the parameter meanprops.

Now to add bit more fun, let’s add two more boxplots, respectively for the Literature and the Economics winners. Assuming we have the ages in two arrays called ageLiterature and ageEconomics, the first thing to do is to concatenate all the arrays and pass them to the boxplot function:

ages=[agePhysics, ageLiterature, ageEconomics]
box = plt.boxplot(ages, showmeans=True, whis=99)

Each boxplot can have its own colours, this can be set through the pyplot function setp():

# add colours
   # physics = green
plt.setp(box['boxes'][0], color='green')
plt.setp(box['caps'][0], color='green')
plt.setp(box['whiskers'][0], color='green')

and so on for the other boxplots …

As for the other plots, you can add titles, labels and a grid:

plt.ylim([20, 95]) # y axis gets more space at the extremes
plt.grid(True, axis='y') # let's add a grid on y-axis
plt.title('Distribution of the Nobel Prize winner ages', fontsize=18) # chart title
plt.ylabel('Age (years) at winning time') # y axis title
plt.xticks([1,2,3], ['Physics','Literature','Economics']) # x axis labels

this is the final graph:

The Nobel Prize winners arranged by field and age
The Nobel Prize winners arranged by field and ageSo

So, it seems that you have almost no chance to win a Nobel in Literature before you are 40 and more likely before you’re 55 years old but it’s even worse for Economics: nobody won it till now before age 50 and the mean/median are 65 …

Quartiles and summary statistics in Python

We have seen how to calculate measures of central tendency as mode and mean, and deviation measures such as the variance. Let’s see another measure describing how the data is distributed: the quartiles.

The quartiles of a set of data values are the three points that divide the ranked data set (i.e. you need to order the data points first) into four equal groups, each group comprising a quarter of the data.
Quartiles are actually a type of quantiles which are values taken at regular intervals; another popular type of quantiles are the percentiles – where you divide the data sets into 100 groups – like in “a student scoring above the 80th percentile of a standardised test”.

Back to the quartiles, the three data points are:

  • first quartile, also called the lower quartile: splits off the lowest quarter (25%) of data from the rest
  • second quartile, also called the median: cuts data set in half
  • third quartile, also called the upper quartile: splits off the highest quarter (25%) of data from the rest

Let’s see how to calculate them with Python.
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