Array Attributes in NumPy

NumPy arrays come with a variety of attributes that provide useful information about the array’s structure, dimensions, data type, and more. These attributes make it easier to analyze and manipulate arrays effectively.

Summary of Array Attributes

Key Array Attributes

1. ndim: Number of Dimensions

The ndim attribute returns the number of dimensions (axes) of the array.

import numpy as np
 
array = np.array([[1, 2, 3], [4, 5, 6]])
print("Number of dimensions:", array.ndim)

Output:

Number of dimensions: 2

2. shape: Shape of the Array

The shape attribute provides the dimensions of the array as a tuple (rows, columns, etc.).

print("Shape of the array:", array.shape)

Output:

Shape of the array: (2, 3)

3. size: Total Number of Elements

The size attribute returns the total number of elements in the array.

print("Total number of elements:", array.size)

Output:

Total number of elements: 6

4. dtype: Data Type of Elements

The dtype attribute shows the data type of the elements in the array.

print("Data type of elements:", array.dtype)

Output:

Data type of elements: int64

5. itemsize: Size of Each Element in Bytes

The itemsize attribute returns the size (in bytes) of each element in the array.

print("Size of each element in bytes:", array.itemsize)

Output:

Size of each element in bytes: 8

6. nbytes: Total Bytes Consumed by the Array

The nbytes attribute returns the total number of bytes consumed by the array.

print("Total bytes consumed:", array.nbytes)

Output:

Total bytes consumed: 48

7. T: Transpose of the Array

The T attribute returns the transpose of the array (rows become columns and vice versa).

print("Transpose of the array:\n", array.T)

Output:

Transpose of the array:
 [[1 4]
  [2 5]
  [3 6]]

8. flat: Iterator Over Array Elements

The flat attribute provides an iterator to iterate through all the elements of the array.

for element in array.flat:
    print(element, end=" ")

Output:

1 2 3 4 5 6

9. real and imag: Real and Imaginary Parts

If the array contains complex numbers, the real and imag attributes return the real and imaginary parts, respectively.

complex_array = np.array([1 + 2j, 3 + 4j])
print("Real part:", complex_array.real)
print("Imaginary part:", complex_array.imag)

Output:

Real part: [1. 3.]
Imaginary part: [2. 4.]

10. base: View or Copy Indicator

The base attribute indicates if the array is a view or a copy. If the array is a view, base points to the original array; otherwise, it returns None.

view_array = array.view()
copy_array = array.copy()
 
print("View base:", view_array.base)
print("Copy base:", copy_array.base)

Output:

View base: [[1 2 3]
 [4 5 6]]
Copy base: None

Try It Yourself

Problem 1: Array Analysis

Create a 2D array of shape (3, 4) with random integers and print its attributes (shape, size, dtype, and nbytes).

import numpy as np
 
array = np.random.randint(1, 100, (3, 4))
print("Array:\n", array)
print("Shape:", array.shape)
print("Size:", array.size)
print("Data type:", array.dtype)
print("Total bytes:", array.nbytes)

Problem 2: Transpose and Iterate

Create a 2D array of shape (2, 3). Print its transpose and iterate through all its elements.

import numpy as np
 
array = np.array([[10, 20, 30], [40, 50, 60]])
print("Transpose:\n", array.T)
 
print("Elements:")
for element in array.flat:
    print(element, end=" ")