**Introduction**

In the world of programming and data analysis, tools like NumPy help us work with numbers efficiently. When dealing with sets of numbers, we often want to find averages. Two functions in NumPy, np.mean() and np.average(), are used to calculate these averages. While they might seem similar, they have distinct features that cater to different needs.

Let's explore the differences between these functions to better understand when and how to use them

**np.mean()**

In the world of numbers and coding, np.mean() is like a helper that tells you the average of a group of numbers. It adds up all those numbers and then divides the sum by how many numbers there are. So, it gives you a general idea of what the "middle" number might be in your group. It's handy when you just want a quick way to know what's kind of "normal" in your bunch of numbers

**Definition and Purpose **

np.mean() is a helpful tool in the Python programming world, especially when you're dealing with numbers. Its job is simple: it calculates the average of a bunch of numbers. Imagine you have a collection of numbers, and you want to find the number that's kind of in the middle. That's the average! np.mean() adds up all those numbers and then divides the total by how many numbers there are. This gives you a basic idea of what's the "typical" value in your group of numbers. It's like finding the average score of a game, it helps you understand the overall performance.The purpose of np.mean() is to find the average (or mean) of a set of numbers in an array. It's like finding the typical value in a collection of values.

**Syntax and Parameters**

The syntax for using np.mean() is straightforward. You provide the array containing the numbers you want to find the mean of. The function takes the following form:

`np.mean(array)`

Here, "array" is the placeholder for the actual array you're working with. You replace it with your specific array of numbers. The function then calculates the total sum of all the numbers in the array and divides it by the count of numbers to get the average.

**Example**

**Code**

**Output**

**Explanation**

In this code:

1. We import the NumPy library as np to access its functions.

2. We create a list named data containing the numbers for which we want to find the mean.

3. By calling np.mean(data), we compute the mean value of the numbers in the data list.

4. The result is stored in the variable mean_value.

5. Finally, we use print() to display the calculated mean value.

In summary, np.mean() is a handy tool when you want to quickly find the average value of a group of numbers in an array. It's especially useful for obtaining a basic idea of the central tendency of your data.