NumPy: Attributes of Arrays

Attributes of Arrays

• Every NumPy array object has some attributes that describe its structure.

1) .ndim:

It means number of dimensions. It tells how many dimensions (axes) an array has. In simple words, it shows whether the array is 1D, 2D or 3D

Python program

import numpy as np

arr1 = np.array([10,20,30])

arr2 = np.array([[1,2,3],[4,5,6]])

print("Dimension of arr1: ",arr1.ndim)

print("Dimension of arr2: ",arr2.ndim)

Output

Dimension of arr1: 1

Dimension of arr2: 2

2) .shape:

It gives the size of each dimension as a tuple. The first value indicates the number of rows and second value indicates the number of columns.

Python program

import numpy as np

arr = np.array([[10,20,30],[40,50,60]])

print(arr.shape)

Output

(2, 3)

Here the array is created as follows

10 20 30

40 50 60

That means there are two rows and three columns.

3) .size:

The size attribute tells how many elements are present in the entire array. This attribute is useful when checking the total number of values stored ‒ especially for large dataset.

Python program

import numpy as np

ап = np.array([[10,20,30],[40,50,60]])

print(arr.size)

Output

6

4) .dtype:

This attribute tells what type of values are stored in the array. For instance ‒ integer, float, string, booleans etc)

Python program

import numpy as np

arr = np.array([1,2,3], dtype = float)

print(arr.dtype)

Output

float64

5) .itemsize:

It shows how much memory(in bytes) each element of array takes. For instance ‒ float64 takes 8 bytes per element, while int32 takes 4 bytes.

Python program

import numpy as np

arr1 = np.array([1,2,3], dtype=np.int32)

arr2= np.array([1.0,2.0,3.0], dtype=np.float64)

print(arr1.itemsize)

print(arr2.itemsize) do

Output

4

8

Note that, Each integer in arr1 uses 4 bytes of memory. Each floating‒point number in arr2 uses 8 bytes.

Review Question

1. Explain attributes of NumPy arrays with the help of Python programs.