# Finding the mean value or rolling average of a scattered dataset with python

When working with a scattered dataset, it is often useful to find the mean value or rolling average to get a better understanding of the overall trend. In this article, we will explore three different ways to solve this problem using Python.

## Method 1: Using the statistics module

The first method involves using the statistics module in Python. This module provides various functions for mathematical statistics, including finding the mean value. Here is a sample code that demonstrates this approach:

``````import statistics

data = [1, 2, 3, 4, 5]
mean = statistics.mean(data)

print("Mean value:", mean)``````

This code imports the statistics module and defines a list of data points. The mean function from the statistics module is then used to calculate the mean value of the dataset. Finally, the result is printed to the console.

## Method 2: Using numpy

The second method involves using the numpy library, which provides a wide range of mathematical functions and operations. Here is a sample code that demonstrates this approach:

``````import numpy as np

data = [1, 2, 3, 4, 5]
mean = np.mean(data)

print("Mean value:", mean)``````

This code imports the numpy library and defines a list of data points. The mean function from numpy is then used to calculate the mean value of the dataset. Finally, the result is printed to the console.

## Method 3: Using a custom function

The third method involves creating a custom function to calculate the mean value. This approach allows for more flexibility and customization. Here is a sample code that demonstrates this approach:

``````def calculate_mean(data):
total = sum(data)
count = len(data)
mean = total / count
return mean

data = [1, 2, 3, 4, 5]
mean = calculate_mean(data)

print("Mean value:", mean)``````

This code defines a custom function called calculate_mean that takes a list of data points as input. The function calculates the total sum of the data points, counts the number of data points, and then calculates the mean value. Finally, the result is printed to the console.

After exploring these three different methods, it is clear that using the statistics module (Method 1) is the best option. It provides a simple and efficient way to calculate the mean value of a scattered dataset. Additionally, it handles various edge cases and provides additional statistical functions that can be useful in further analysis.

Rate this post

### 13 Responses

Method 2 using numpy seems like the easiest and most efficient way to find the mean value.

2. Justin Galvan says:

Method 2 using numpy is the real MVP! Its like a cheat code for calculating mean values effortlessly. 🤓🔥

3. Chaya Jenkins says:

Method 2 with numpy is the way to go – its like having a math wizard in your code! 🧙‍♂️🔢

4. Shepherd says:

Method 2 using numpy is the way to go! Its like magic, making calculations feel effortless. #NumpyFanClub

5. Jeremy says:

Method 3 seems interesting, but I prefer Method 1 because its simpler and faster. What do you guys think?

1. Daxton says:

I completely disagree! Method 3 is far superior because it offers more flexibility and accuracy. Method 1 may be simpler and faster, but it lacks the depth and precision that Method 3 provides. Its all about quality over quantity, my friend.

6. Jovie says:

Method 2 seems cool, but I wonder if Method 3 gives more flexibility and control?

7. Brooks Vang says:

Method 3 is a game-changer! Who needs built-in modules when you can customize your own function? #PythonPower

Method 2 with numpy seems way cooler and more efficient than the others, dont you think? 🤔

1. Bristol says:

Nah, I disagree. Method 2 might be flashy, but efficiency isnt everything. The other methods could be more readable, maintainable, and easier to grasp for beginners. Its all about finding the right balance, my friend.

9. Sarah Ho says:

Method 3 seems like a hassle, why not just stick to Method 1 or 2?

10. Niko Hicks says:

Method 3 seems like a lot of work. Why not stick with the easy-peasy numpy? 🤷‍♀️

1. Noah Sosa says:

Sure, numpy might be easy-peasy for some, but not everyone wants to rely on a single library. Method 3 offers a different approach and allows for more flexibility. Variety is the spice of life, my friend. 🌶️