In Python, different libraries and modules are used to perform the arithmetic and computation operations. Python’s “NumPy” library performs different computation operations such as mean, median and standard deviation, etc. This write-up will provide a complete overview of the “mean of the Numpy array” with detailed examples:

**What is the Mean of Numpy Array in Python?****Example 1: Using Numpy to Calculate the Mean of 1-D Array****Example 2: Using Numpy to Calculate the Mean of 2-D Array****Example 3: Using Numpy to Calculate the Mean of 3-D Array with Multiple Axis**

So let’s begin!

**What is the Mean of Numpy Array in Python?**

In Python, the “**array**” data structure is used to store multiple elements of the same data simultaneously. The “**Mean**” value is generally defined as the average of all input numbers. The function named “**numpy.mean()**” is used to calculate the mean of any input array, including “**1D**”,”**2D**”, and “**3D**”. The syntax of the “numpy.mean()” function is shown below:

```
numpy.mean(arr, axis=None, dtype=None, out=None)
```

In the above syntax:

- The first parameter named “
**arr**” takes an array variable as an input. This parameter is mandatory to initialize otherwise, the numpy.mean() function does not execute. - The second parameter named “
**axis**” is used to define the axis in which the mean is calculated. The default value of the axis parameter in which the mean is computed is “flattened array”. - The “
**dtype**” parameter is used to define the type of calculation. The default type for all the ”integer” values is float. - The “
**out**” parameter is used for result placement, and it is an alternative output array.

Let’s understand the mean of Numpy using a few examples:

**Example 1: Using Numpy to Calculate the Mean of 1-D Array**

The “**np.array()**” function (the second name of the numpy.mean() function) is used to create the “**1D**” array and the “**np.mean()**” function is used to calculate the mean of the “1D” Array. Let’s calculate the mean of “**1-D**” array via the following code:

**Code**

```
#Using Numpy to Calculate the Mean of 1-D Array
import numpy as np
array_1D = np.array([12, 17, 52, 83, 49,44])
# Calculating the mean of 1-D array
array = np.mean(array_1D)
print(array)
```

In the above code:

- The “
**NumPy**” library is accessed at the beginning of code. - The “
**1D array**” is created using the “**np.array()**” function and stored in a variable named “**array_1D**” - The mean of the “
**1D array**” is calculated using the “**np.mean()**” function by taking the argument value of the variable named “**array_1D**”. - The print function prints the mean value on screen.

**Output**

Analyzing the smallest and the largest value inside the array, the mean of the array is calculated.

**Example 2: Using Numpy to Calculate the Mean of 2-D Array**

The “**2D**” or “**Two-dimensional array**” is just like a 1D array, but the 2-D array can be arranged in a row-column structure. In this example, the mean of the 2D array can be calculated using the “np.mean()” function. Let’s understand it with an example of code given below:

**Code**

```
#Using Numpy to Calculate the Mean of 2-D Array
import numpy as np
array_2D = np.array([[15, 8, 32, 73], [39, 44, 2, 62]])
# calculating the mean of 2-D array
array = np.mean(array_2D)
print(array)
```

In the above code:

- The “
**NumPy**” library is accessed at the start of code. - The “
**2D array**” is created using the “**np.array()**” function and stored in a variable named “**array_2D**”. - The mean of the “
**2D array**” is calculated using the “**np.mean()**” function by taking the argument value of the variable named “**array_2D**”. - The print function prints the mean value on screen.

**Output**

The “**np.mean()**” function has returned the mean of the “**2-D**” array.

**Example 3: Using Numpy to Calculate the Mean of 3-D Array With Multiple Axis**

The “np.mean()” function of Python can also calculate the mean of the NumPy array according to the specified axis. For the mean calculation of the “**3D**” array, we must specify more than one axis in the “**axis** **parameter**”. Let’s see how the mean of the 3-D array with multiple axis is calculated:

**Code**

```
#Using Numpy to Calculate the Mean of 3-D Array with multiples axis
import numpy as np
array_3D = [[[6, 7, 4], [8, 5, 3]],[[4, 8, 6], [3, 5, 4]]]
axis_none = np.mean(array_3D)
axis_0 = np.mean(array_3D, axis=0)
axis_1 = np.mean(array_3D, axis=1)
print("\nMean value of 3D array, when axis = None : ", axis_none)
print("\nMean value of 3D array, when axis = 0 : ", axis_0)
print("\nMean value of 3D array, when axis = 1 : ", axis_1)
```

In the above code:

- The “
**NumPy**” library is accessed at the start of code. - The “
**3D array**” is created using the “**np.array()**” function and stored in a variable named “**array_3D**”. - There are three “
**np.mean()**” functions used in the program. - The first “
**np.mean()**” function takes a variable named “**array_3D**” as an input parameter and saves the value of an array with a “**flattened axis”**(which means 1D-Iterator over the array) - The second “np.mean()” function takes a variable named “
**array_3D**” and “**axis value=0**” as an input parameter. - The third “np.mean()” function takes a variable named “
**array_3D**” and “**axis value=1**” as an input parameter. - Three print statements are used to print the mean value of a “
**3D array**” with different axis values such as “**flattened**”, “**axis=0**” and “**axis=1**”.

**Output**

In the above output, the mean value will be printed on the screen according to the axis value.

**Note**: The 2-Dimension has two axes which mean one axis is its row (axis 0), and another is its columns (axis 1). Similarly, 3-D arrays have more than two axes.

That’s it from this informative guide!

**Conclusion**

In Python, the mean of the NumPy array is calculated using the “**np.mean()**” function. This function can calculate the mean of “**1-Dimensional**”,”**2-Dimensional**” and “**3-Dimensional**” arrays along with the axis. The “np.mean()” function calculates the arithmetic mean of an array along with the single axis and for multiple axes. This article briefly explained the “**Mean of Python Numpy Array**” along with multiple “**axes**” with numerous examples.

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