Descriptive statistics#

The term descriptive statistics refers to methods that allow summarizing collections of data. To demonstrate the most important methods, we start by defining a dataset first.

measurements = [5, 2, 6, 4, 8, 6, 2, 5, 1, 3, 3, 6]

Measurements of central tendency#

We can measure the location of our measurement in space using numpy’s statistics functions and Python’s statistics module.

import numpy as np
import statistics as st
np.mean(measurements)
4.25
np.median(measurements)
4.5
st.mode(measurements)
6

Measurements of spread#

Numpy also allows measuring the spread of measurements.

np.std(measurements)
2.0052015692526606
np.var(measurements)
4.020833333333333
np.min(measurements), np.max(measurements)
(1, 8)
np.percentile(measurements, [25, 50, 75])
array([2.75, 4.5 , 6.  ])

Exercise#

Find out if the median of a sample dataset is always a number within the sample. Use these three examples to elaborate on this:

example1 = [3, 4, 5]
example2 = [3, 4, 4, 5]
example3 = [3, 4, 5, 6]