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# Python statistics module

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.

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)
```

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