NumPy Array Attributes – Mastering Python

Essential NumPy Array Attributes

We’ll use the following array throughout our examples:

import numpy as np

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

1. ndim (Number of Dimensions):

This attribute returns the number of dimensions (axes) in the array.

print(arr.ndim)  # Output: 2 (a 2D array)

2. shape (Array Dimensions):

shape returns a tuple indicating the size of the array along each dimension.

print(arr.shape)  # Output: (2, 3) (2 rows, 3 columns)

3. size (Total Number of Elements):

This attribute gives the total number of elements in the array.

print(arr.size)  # Output: 6

4. dtype (Data Type):

dtype reveals the data type of the elements in the array.

print(arr.dtype)  # Output: int64 (or similar, depending on your system)

arr_float = np.array([[1.1, 2.2, 3.3], [4.4, 5.5, 6.6]])
print(arr_float.dtype) # Output: float64

5. itemsize (Size of Each Element):

This attribute provides the size (in bytes) of each element in the array.

print(arr.itemsize)  # Output: 8 (for int64, typically 4 for int32)
print(arr_float.itemsize) # Output: 8 (for float64, typically 4 for float32)

6. nbytes (Total Bytes Consumed):

nbytes calculates the total number of bytes occupied by the array in memory. It’s simply itemsize * size.

print(arr.nbytes)  # Output: 48 (8 bytes/element * 6 elements)
print(arr_float.nbytes) # Output: 48 (8 bytes/element * 6 elements)

7. T (Transpose):

The T attribute returns the transpose of the array (swaps rows and columns). This is particularly useful for matrix operations.

print(arr.T)  # Output: [[1 4] [2 5] [3 6]]

8. real and imag (Real and Imaginary Parts):

For complex number arrays, real and imag return the real and imaginary components, respectively. For arrays of other datatypes, they return the array itself (for real) or an array of zeros (for imag).

complex_arr = np.array([[1+2j, 3+4j], [5+6j, 7+8j]])
print(complex_arr.real) #Output: [[1. 3.] [5. 7.]]
print(complex_arr.imag) #Output: [[2. 4.] [6. 8.]]

print(arr.real) #Output: [[1 2 3] [4 5 6]]
print(arr.imag) #Output: [[0 0 0] [0 0 0]]

These attributes are fundamental tools for understanding and manipulating NumPy arrays. Mastering them is crucial for writing efficient and robust data processing code. Further exploration into NumPy’s functionalities will reveal even more powerful features built upon this foundation.