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Python statistics | mean() function
• Last Updated : 13 Apr, 2018

Prerequisite : Introduction to Statistical Functions

Python is a very popular language when it comes to data analysis and statistics. Luckily, Python3 provide `statistics` module, which comes with very useful functions like `mean()`, `median()`, `mode()` etc.

`mean()` function can be used to calculate mean/average of a given list of numbers. It returns mean of the data set passed as parameters.

Arithmetic mean is the sum of data divided by the number of data-points. It is a measure of the central location of data in a set of values which vary in range. In Python, we usually do this by dividing the sum of given numbers with the count of number present.

```Given set of numbers : [n1, n2, n3, n5, n6]

Sum of data-set = (n1 + n2 + n3 + n4 + n5)
Number of data produced = 5

Average or arithmetic mean  = (n1 + n2 + n3 + n4 + n5) / 5
```

`Syntax` : mean([data-set])

`Parameters` :
[data-set] : List or tuple of a set of numbers.

`Returns` : Sample arithmetic mean of the provided data-set.

`Exceptions` :
TypeError when anything other than numeric values are passed as parameter.

Code #1 : Working

 `# Python program to demonstrate mean()``# function from the statistics module`` ` `# Importing the statistics module``import` `statistics`` ` `# list of positive integer numbers``data1 ``=` `[``1``, ``3``, ``4``, ``5``, ``7``, ``9``, ``2``]`` ` `x ``=` `statistics.mean(data1)`` ` `# Printing the mean``print``(``"Mean is :"``, x)`

Output :

` Mean is : 4.428571428571429`

Code #2 : Working

 `# Python program to demonstrate mean()``# function from the statistics module`` ` `# Importing the statistics module``from` `statistics ``import` `mean`` ` `# Importing fractions module as fr``# Enables to calculate mean of a ``# set in Fraction ``from` `fractions ``import` `Fraction as fr`` ` ` ` `# tuple of positive integer numbers``data1 ``=` `(``11``, ``3``, ``4``, ``5``, ``7``, ``9``, ``2``)`` ` `# tuple of a negative set of integers``data2 ``=` `(``-``1``, ``-``2``, ``-``4``, ``-``7``, ``-``12``, ``-``19``)`` ` `# tuple of mixed range of numbers``data3 ``=` `(``-``1``, ``-``13``, ``-``6``, ``4``, ``5``, ``19``, ``9``)`` ` `# tuple of a set of fractional numbers``data4 ``=` `(fr(``1``, ``2``), fr(``44``, ``12``), fr(``10``, ``3``), fr(``2``, ``3``))`` ` `# dictionary of a set of values``# Only the keys are taken in``# consideration by mean()``data5 ``=` `{``1``:``"one"``, ``2``:``"two"``, ``3``:``"three"``}`` ` ` ` `# Printing the mean of above datsets``print``(``"Mean of data set 1 is % s"` `%` `(mean(data1)))``print``(``"Mean of data set 2 is % s"` `%` `(mean(data2)))``print``(``"Mean of data set 3 is % s"` `%` `(mean(data3)))``print``(``"Mean of data set 4 is % s"` `%` `(mean(data4)))``print``(``"Mean of data set 5 is % s"` `%` `(mean(data5)))`

Output :

```Mean of data set 1 is 5.857142857142857
Mean of data set 2 is -7.5
Mean of data set 3 is 2.4285714285714284
Mean of data set 4 is 49/24
Mean of data set 5 is 2
```

Code #3 : TypeError

 `# Python3 code to demonstrate TypeError`` ` `# importing statistics module``from` `statistics ``import` `mean`` ` `# While using dictionaries, only keys are``# taken into consideration by mean()``dic ``=` `{``"one"``:``1``, ``"three"``:``3``, ``"seven"``:``7``,``       ``"twenty"``:``20``, ``"nine"``:``9``, ``"six"``:``6``}`` ` `# Will raise TypeError``print``(mean(dic))`

Output :

```Traceback (most recent call last):
File "/home/9f8a941703745a24ddce5b5f6f211e6f.py", line 29, in
print(mean(dic))
File "/usr/lib/python3.5/statistics.py", line 331, in mean
T, total, count = _sum(data)
File "/usr/lib/python3.5/statistics.py", line 161, in _sum
for n, d in map(_exact_ratio, values):
File "/usr/lib/python3.5/statistics.py", line 247, in _exact_ratio
raise TypeError(msg.format(type(x).__name__))
TypeError: can't convert type 'str' to numerator/denominator
```

Applications :
Mean/Arithmetic average is one of the very important function, while working with statistics and large values. So, with the function like mean(), trending and featured values can be extracted from the large data sets.

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