The Statistics module in Python provides functions for calculating numeric (Real-valued) data statistics. The arithmetic mean is a sum of data divided by the number of data points. It measures the central location of data in a set of values that vary in range.

**Python 3** provides the **statistics** module with handy functions like **mean**, median, mode, etc.

**Python mean**

The** mean()** is a built-in **Python** statistics **function** used to **calculate** the **average** of **numbers** and **lists**. The **mean()** function accepts **data** as an **argument** and **returns** the **mean** of the **data**.

To use the **mean()** method in the **Python** program, import the Python statistics module, and then we can use the mean function to return the mean of the given list.

Let’s see some examples of the Statistics module.

# app.py import statistics data = [11, 21, 11, 19, 46, 21, 19, 29, 21, 18, 3, 11, 11] x = statistics.mean(data) print(x) y = statistics.median(data) print(y) z = statistics.mode(data) print(z) a = statistics.stdev(data) print(a) b = statistics.variance(data) print(b)

See the following output.

➜ pyt python3 app.py 18.53846153846154 19 11 10.611435534486562 112.6025641025641 ➜ pyt

In the above code example, we have used Mean, mode, median, variance, stddev functions.

**Python average of a list**

To calculate a mean or average of the list in Python,

- Using
**statistics.mean()**function. - Use the
**sum()**and**len()**functions. - Using Python
**numpy.mean()**.

The formula to calculate the average is achieved by calculating the sum of the numbers in the list divided by a count of numbers in the list.

# app.py import statistics spiList = [5.55, 5.72, 7.3, 7.75, 8.4, 9, 8.8, 8.2] print(statistics.mean(spiList))

See the following output.

In the above example, we have eight data points and use statistics.mean() function, we calculated the mean of the list.

**Using For loop to calculate the mean**

In the following code example, we have initialized the variable sumOfNumbers to 0 and used a for loop.

The for loop will loop through the elements present in the list, and each number is added and saved inside the sumOfNumbers variable.

The average is calculated using the sumOfNumbers divided by the count of the numbers in the list using the len() built-in function.

# app.py def averageOfList(num): sumOfNumbers = 0 for t in num: sumOfNumbers = sumOfNumbers + t avg = sumOfNumbers / len(num) return avg print("The average of List is", averageOfList([19, 21, 46, 11, 18]))

**Output**

python3 app.py The average of List is 23.0

In the above code, we use the for loop to the sum of all items and then divide that sum by several items to get the average in Python.

**Using sum() and len() functions**

Python sum() is a built-in function that returns the sum of all list elements. Likewise, the len() function gives the number of items in the list. We will use the combination of these two built-in functions to get the mean of the list.

# app.py def averageOfList(numOfList): avg = sum(numOfList) / len(numOfList) return avg print("The average of List is", round(averageOfList([19, 21, 46, 11, 18]), 2))

**Output**

python3 app.py The average of List is 23.0

**Using numpy.mean() function**

**NumPy.mean()** function returns the average of the array elements. The average is taken over the flattened array by default; otherwise over the specified axis.

Numpy library is a commonly used library to work on large multi-dimensional arrays. It also has an extensive collection of mathematical functions on arrays to perform various tasks.

One important thing to note here is that the mean() function will give us the average for the list provided.

See the below code.

# app.py from numpy import mean number_list = [19, 21, 46, 11, 18] avg = mean(number_list) print("The average of List is ", round(avg, 2))

**Output**

python3 app.py The average of List is 23.0

**More Examples**

First, we will import the statistics module and then call the mean() function to get the mean of data.

# app.py import statistics data = [21, 19, 18, 46, 30] print(statistics.mean(data))

See the below output.

**Calculating the Mean of a tuple in Python.**

To find the mean of a tuple in Python, use the statistics.mean() method is the same as finding the list’s mean. We have to pass the tuple as a parameter.

Let’s calculate the mean of the tuple using the following code.

# app.py import statistics tupleA = (1, 9, 2, 1, 1, 8) print(statistics.mean(tupleA))

The output of the above code is the following.

It will work the same as a list. It merely returns the **Mean **of the numbers inside the **tuple.**

**Calculating the Mean of Dictionary in Python**

To calculate the mean of the Dictionary, we can use statistics.mean() method. In Dictionary, the mean function only counts the keys as numbers and returns the mean of that Dictionary based on the dictionary keys.

# app.py import statistics dictA = {1: 19, 2:21, 3:18, 4:46, 5:30} print(statistics.mean(dictA))

See the below output.

**Calculating a mean of a tuple of a negative set of integers**

We use statistics to find the mean of a tuple of the negative set.mean() method. We need to pass the negative tuple as a parameter to the mean() function, and in return, we will get the output.

Let’s see the tuple of negative integers.

# app.py import statistics data = (-11, -21, -18, -19, -46) print(statistics.mean(data))

See the following output.

➜ pyt python3 app.py -23 ➜ pyt

Let’s take an example of the tuple of a mixed range of numbers.

# app.py import statistics data = (11, 21, -18, 19, -46) print(statistics.mean(data))

See the following output.

➜ pyt python3 app.py -2.6 ➜ pyt

**Calculating the mean of a list of a negative set of integers**

To calculate the mean of the list in Python, use statistics.mean() method. We will pass the list of the negative set to the mean() method, and in the output, we will calculate the mean.

Let’s take a list of negative integers and apply the **mean() function**.

# app.py import statistics data = [-11, -21, -18, -19, -46] print(statistics.mean(data))

See the following output.

➜ pyt python3 app.py -23 ➜ pyt

Let’s take an example of the list of a mixed range of numbers.

# app.py import statistics data = [11, 21, -18, -19, 46] print(statistics.mean(data))

See the output.

➜ pyt python3 app.py 8.2 ➜ pyt

**Calculating the mean of the list of fractions**

To calculate the mean of the list of fractions, use statistics.mean() method. First, we have to import the statistics and fractions module and then create a fraction of numbers, and in the output, we will get the mean values.

# app.py import statistics from fractions import Fraction as fr data = [fr(1, 2), fr(44, 12), fr(10, 3), fr(2, 3)] print(statistics.mean(data))

See the following output.

➜ pyt python3 app.py 49/24 ➜ pyt

**TypeError in Python**

Let’s take string keys in the Python dictionary and get the mean of the string values. It will get the error because we can not find the string’s mean value.

See the following code.

# app.py import statistics data = {"a": 11, "b": 21, "c": 19, "d": 29, "e": 18, "f": 46} print(statistics.mean(data))

See the following output.

➜ pyt python3 app.py Traceback (most recent call last): File "app.py", line 6, in print(statistics.mean(data)) File "/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/statistics.py", line 312, in mean T, total, count = _sum(data) File "/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/statistics.py", line 148, in _sum for n,d in map(_exact_ratio, values): File "/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/statistics.py", line 230, in _exact_ratio raise TypeError(msg.format(type(x).__name__)) TypeError: can't convert type 'str' to numerator/denominator ➜ pyt

**Conclusion**

We can use the following ways to find the average or mean of the List in Python.

- Using the statistics.mean() method.
- Using Python sum() and len() method.
- Using Numpy.mean() function.
- Using for loop and len() method.

That’s it.

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