Python statistics.mean() is a built-in method that calculates the sample arithmetic mean (commonly known as the “average”) of input numeric data. The numeric data can be anything from numbers, lists, dictionaries, sets, or tuples.

The basic formula to calculate the arithmetic average is: sum(data) / len(data)

To calculate the average of a list, pass the list to the statistics.mean() method. It returns the average of the whole list. Underneath, it calculates the sum of all elements and divides it by the total number of elements.

import statistics

main_list = [1, 1, 2, 2, 3]

mean_of_list = statistics.mean(main_list)

print(mean_of_list)

# Output: 1.8

Syntax

mean(input)

Parameters

Argument	Description
input	It represents a non-empty iterable (e.g., list, tuple, range, or generator) containing real-valued numbers. It includes int, float, fractions.Fraction, and decimal.Decimal

Exceptions

The mean() function may throw a TypeError exception when anything other than numeric values is passed as an argument. It may raise StatisticsError error if the data is empty.

List of negative integers

What if the input list contains negative integers? Well, it still works the same. It first calculates the sum of all elements and divides it by the number of elements.

import statistics

negative_list = [-11, -21, -18, -19, -46]

mean_of_negative_list = statistics.mean(negative_list)

print(mean_of_negative_list)

# Output: -23

Let’s consider a list that includes a mixed range of numbers.

import statistics

mixed_list = [11, 21, -18, -19, 46]

print(statistics.mean(mixed_list))

# Output: 8.2

Mean of the dictionary

If the input is a dictionary, it only counts keys as elements to calculate the mean of it.

In Python, dictionary keys must be unique. If a dictionary is defined with duplicate keys, only the last assignment is kept.

import statistics

main_dict = {1: 19, 2: 18, 3: 46, 4: 30}

mean_of_dict = statistics.mean(main_dict)

print(mean_of_dict)

# Output: 2.5 (1 + 2 + 3 + 4) / 4

The keys in the dictionary are 1, 2, 3, 4, and their mean is 2.5.

Mean of a tuple

It does not matter if the numbers in a tuple are positive or negative. It should be a real number. 

Let’s define a tuple with negative integers and calculate its mean.

import statistics

negative_tuple = (-11, -21, -18, -19, -46)

mean_of_negative_tuple = statistics.mean(negative_tuple)

print(mean_of_negative_tuple)

# Output: -23.0

Let’s consider a tuple containing a mixed range of numbers.

import statistics

mixed_tuple = (11, 21, -18, 19, -46)

print(statistics.mean(mixed_tuple))

# Output: -2.6

Mean with decimal (High Precision)

For high-precision decimal values, we can utilize the built-in Decimal module to create a list of highly decimal values and calculate their mean.

from statistics import mean
from decimal import Decimal

data = [Decimal("0.3"), Decimal("0.6"), Decimal("0.1")]

print(mean(data))

# Output: 0.3333333333333333333333333333

Mean with Fraction (Exact Rational Arithmetic)

The Fraction class (from Python’s fractions module) represents rational numbers exactly, as a numerator/denominator pair.

1. Fraction(1, 4) → 1 / 4​
2. Fraction(1, 2) → 1 / 2​
3. Fraction(3, 4) → 3 / 4

from fractions import Fraction
from statistics import mean

data = [Fraction(1, 4), Fraction(1, 2), Fraction(3, 4)]

print(mean(data))

# Output: 1/2

From the above output, you can see that since the input was a list of Fraction objects, statistics.mean() preserves the type and returns another Fraction, not a float.

Empty Data (Edge Case)

If your input list is empty and you attempt to find its mean, it will throw statistics.StatisticsError: mean requires at least one data point

However, we can handle it using the try/except mechanism.

import statistics

empty_list = []

try:
    print(statistics.mean(empty_list))
except statistics.StatisticsError as e:
    print(e)

# Output: mean requires at least one data point

Ensures at least one data point; prevents division by zero.

TypeError: can’t convert type ‘str’ to numerator/denominator

Let’s say we have a dictionary with keys that are characters, not integers. If you directly pass the dictionary to the mean() function, it will throw TypeError: can’t convert type ‘str’ to numerator/denominator because the keys are characters, not valid numbers.

import statistics

data = {"a": 11, "b": 21, "c": 19, "d": 29, "e": 18}

print(statistics.mean(data))

# Error: TypeError: can't convert type 'str' to numerator/denominator

We encountered a TypeError because statistics.mean() requires an iterable of numbers, whereas we provided an entire dictionary of data instead of just its values.

To fix this TypeError, the correct approach is to extract the values from the dictionary using dict.values() function and then calculate the mean.

import statistics

data = {"a": 11, "b": 21, "c": 19, "d": 29, "e": 18}

# Extract the values from the dictionary and calculate the mean
mean_of_values = statistics.mean(data.values())

print(mean_of_values)

# Output: 19.6

That’s all!