The **numpy.mean()** method **“calculates the arithmetic mean (average) of the elements in an array”** or along a specified axis of a multidimensional array.

**Syntax**

`numpy.mean(`*a*, *axis=None*, *dtype=None*, *out=None*, *keepdims=<no value>*, ***, *where=<no value>*)

**Parameters**

**a**: The input array or a sequence that can be converted to an**array.**

**axis:**The axis along which the means are computed. By default, it is**None**, and the mean is calculated for the flattened array.

**dtype:**The data type to use in the calculation. By default, it is**None,**and the data type of the input array is used.

**out:**An optional output array to store the result.

**keepdims:**If set to**True,**the reduced axes are left in the result as dimensions with size one. The default value is**False**.

**Example**

```
import numpy as np
# Sample 1D array
arr_1d = np.array([1, 2, 3, 4, 5])
# Calculate the mean of the 1D array
mean_1d = np.mean(arr_1d)
print("Mean of the 1D array:", mean_1d)
# Sample 2D array
arr_2d = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]])
# Calculate the mean of the 2D array
mean_2d = np.mean(arr_2d)
print("\nMean of the 2D array:", mean_2d)
# Calculate the mean along axis 0 (columns) of the 2D array
mean_axis_0 = np.mean(arr_2d, axis=0)
print("\nMean along axis 0 (columns) of the 2D array:", mean_axis_0)
# Calculate the mean along axis 1 (rows) of the 2D array
mean_axis_1 = np.mean(arr_2d, axis=1)
print("\nMean along axis 1 (rows) of the 2D array:", mean_axis_1)
```

**Output**

`Mean of the 1D array: `**3.0**
Mean of the 2D array: **5.0**
Mean along axis 0 (columns) of the 2D array: **[4. 5. 6.]**
Mean along axis 1 (rows) of the 2D array: **[2. 5. 8.]**

In this example, we calculated the mean of a **1D array,** a **2D array,** and the means along axis 0 (columns) and axis 1 (rows) of a 2D array using the **numpy.mean()** method.