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np.array .size In NumPy

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.

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