Understanding NumPy Array Attributes

Common Attributes of NumPy Arrays

NumPy arrays, also known as ndarray objects, come with several built-in attributes that help you understand the array’s structure, memory usage, and metadata. These attributes are essential when working with numerical or scientific data.

Here are the most commonly used attributes of a NumPy array:

• ndim: Returns the number of dimensions (axes) of the array.
• shape: Returns a tuple showing the size of each dimension.
• size: Returns the total number of elements in the array.
• dtype: Describes the data type of each element in the array.
• itemsize: Shows the size in bytes of each individual element.
• nbytes: Total number of bytes consumed by all elements.
• T: Returns the transpose of the array (rows become columns).

Understanding these attributes helps you analyze array structures, optimize performance, and debug issues effectively in data science and numerical applications.

💡 Tip: The most frequently used attributes are ndim, shape, size, and dtype. Start by learning those first!

Example 1: Exploring Common NumPy Array Attributes

Let’s create a simple 2D array and explore its most useful attributes. This will help you understand how NumPy internally represents the structure and data of an array.

import numpy as np

# Create a 2D NumPy array
arr = np.array([[10, 20, 30], [40, 50, 60]])

# Display basic attributes
print("Number of dimensions:", arr.ndim)
print("Shape of array:", arr.shape)
print("Total number of elements:", arr.size)
print("Data type of elements:", arr.dtype)
print("Bytes per element:", arr.itemsize)
print("Total bytes:", arr.nbytes)
print("Transpose:",)
print(arr.T)

import numpy as np

# Create a 2D NumPy array
arr = np.array([[10, 20, 30], [40, 50, 60]])

# Display basic attributes
print("Number of dimensions:", arr.ndim)
print("Shape of array:", arr.shape)
print("Total number of elements:", arr.size)
print("Data type of elements:", arr.dtype)
print("Bytes per element:", arr.itemsize)
print("Total bytes:", arr.nbytes)
print("Transpose:",)
print(arr.T)

Output

Number of dimensions: 2
Shape of array: (2, 3)
Total number of elements: 6
Data type of elements: int64
Bytes per element: 8
Total bytes: 48
Transpose:
 [[10 40]
  [20 50]
  [30 60]]

Number of dimensions: 2
Shape of array: (2, 3)
Total number of elements: 6
Data type of elements: int64
Bytes per element: 8
Total bytes: 48
Transpose:
 [[10 40]
  [20 50]
  [30 60]]

As you can see:

• ndim is 2 because it’s a 2D array.
• shape is (2, 3), meaning 2 rows and 3 columns.
• size is 6, which is the total number of elements.
• dtype is int64, showing the data type.
• itemsize is 8, indicating each int64 uses 8 bytes.
• nbytes is 48, total memory used (6 × 8).
• T gives the transposed view (rows ↔ columns).

💡 Tip: Try modifying the array or reshaping it and observe how these attributes change!