In NumPy, the size of an array can be obtained using the `.size`

attribute, which returns the total number of elements in the array. It’s important to note that `.size`

returns the total count of elements, not the dimensions of the array.

Here’s an example:

`import numpy as np`

`# Creating a NumPy array`

array = np.array([[1, 2, 3], [4, 5, 6]])

`# Getting the size of the array`

size = array.size

`print(size) # Output: 6`

In this example, `array`

is a 2×3 matrix (2 rows and 3 columns), so it has 6 elements in total, and therefore `array.size`

returns 6.

### Understanding `.size`

, `.shape`

, and `.ndim`

In NumPy, these three attributes give you different but complementary information about an array:

`.size`

: Total number of elements in the array.`.shape`

: Dimensions of the array (a tuple indicating the size in each dimension).`.ndim`

: Number of dimensions (axes) of the array.

### Examples

#### Example 1: Basic 1D Array

`import numpy as np`

`# Creating a 1D array`

arr_1d = np.array([1, 2, 3, 4, 5])

`print("Size:", arr_1d.size) # Output: 5`

print("Shape:", arr_1d.shape) # Output: (5,)

print("Number of Dimensions:", arr_1d.ndim) # Output: 1

Here, `arr_1d`

is a one-dimensional array with 5 elements.

#### Example 2: 2D Array (Matrix)

`# Creating a 2D array (matrix)`

arr_2d = np.array([[1, 2, 3], [4, 5, 6]])

`print("Size:", arr_2d.size) # Output: 6`

print("Shape:", arr_2d.shape) # Output: (2, 3)

print("Number of Dimensions:", arr_2d.ndim) # Output: 2

`arr_2d`

is a two-dimensional array (or matrix) with 2 rows and 3 columns, hence 6 elements in total.

#### Example 3: 3D Array

`# Creating a 3D array`

arr_3d = np.array([[[1, 2], [3, 4]], [[5, 6], [7, 8]]])

`print("Size:", arr_3d.size) # Output: 8`

print("Shape:", arr_3d.shape) # Output: (2, 2, 2)

print("Number of Dimensions:", arr_3d.ndim) # Output: 3

This is a three-dimensional array. It can be thought of as two 2×2 matrices. It has 8 elements in total.

#### Example 4: Changing Shape of an Array

`# Reshaping an array`

arr = np.array([1, 2, 3, 4, 5, 6])

reshaped_arr = arr.reshape(2, 3)

`print("Original Size:", arr.size) # Output: 6`

print("Reshaped Size:", reshaped_arr.size) # Output: 6

print("Reshaped Shape:", reshaped_arr.shape) # Output: (2, 3)

Reshaping an array changes its shape but not its size.

### Using `.size`

in Operations

The `.size`

attribute can be particularly useful in operations where you need to know the total number of elements. For instance:

`# Creating a 2D array`

arr = np.array([[1, 2, 3], [4, 5, 6]])

`# Compute the average of all elements`

average = np.sum(arr) / arr.size

`print("Average:", average)`

In this example, `arr.size`

is used to compute the average value of all elements in the array.

### Important

- To get the shape of an array (dimensions in each direction), use the
`.shape`

attribute. For the above example,`array.shape`

would return`(2, 3)`

. - To get the number of dimensions (or axes) of the array, use the
`.ndim`

attribute. For the above array,`array.ndim`

would return 2. - The
`.size`

attribute is particularly useful when you need to iterate over all elements in an array or when you’re performing operations that depend on the number of elements.

Understanding `.size`

, `.shape`

, and `.ndim`

is fundamental to working with NumPy arrays, as these properties are critical to many array operations, reshaping, slicing, and more. They provide a complete picture of the structure of the array, which is crucial for correctly manipulating and analyzing data in array form.