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The statistics module provides functions to mathematical statistics of numeric data. there are a variety of useful functions are available
The statistics module was introduced in Python 3.4, so if you are using an older version of python you will have to install a newer one if you want to use these.
Lets look at the functions that are available
Averages and measures of central location
These functions calculate an average or typical value from a population or sample.
mean() |
Calculates the Arithmetic mean (“average”) of data. |
fmean() |
Calculates the Fast, floating point arithmetic mean. |
geometric_mean() |
Calculates the Geometric mean of data. |
harmonic_mean() |
Calculates the Harmonic mean of data. |
median() |
Calculates the Median (middle value) of data. |
median_low() |
Calculates the Low median of data. |
median_high() |
Calculates the High median of data. |
median_grouped() |
Calculates the Median, or 50th percentile, of grouped data. |
mode() |
Calculates the Single mode (most common value) of discrete or nominal data. |
multimode() |
Calculates the List of modes (most common values) of discrete or nomimal data. |
quantiles() |
Divide data into intervals with equal probability. |
Measures of spread
These functions calculate a measure of how much the population or sample tends to deviate from the typical or average values.
pstdev() |
Calculates the Population standard deviation of data. |
pvariance() |
Calculates the Population variance of data. |
stdev() |
Calculates the Sample standard deviation of data. |
variance() |
Calculates the Sample variance of data. |
Code
This example shows some of the more common function sin action
import statistics list_example = [4,6,7,2,6,5,5,5,5,2,5,6,1,4,2] a = statistics.mean(list_example) print(a) b = statistics.median(list_example) print(b) c = statistics.mode(list_example) print(c) d = statistics.stdev(list_example) print(d) e = statistics.variance(list_example) print(e)