Numpy - ndarray

ndarray((short for N-dimensional array)) is a core object in NumPy. It is a homogeneous array which means it can hold elements of the same data type. It is a multi-dimensional data structure that enables fast and efficient manipulation of large dataset

Let's understand with a simple example:

import numpy as np

#1D array
arr1 = np.array([1, 2, 3, 4, 5])

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

#3D array
arr3 = np.array([[[1, 2], [3, 4]], [[5, 6], [7, 8]]])
print(arr1)
print(arr2)
print(arr3)

[1 2 3 4 5]
[[1 2 3]
 [4 5 6]]
[[[1 2]
  [3 4]]

 [[5 6]
  [7 8]]]

Attributes of ndarray

Understanding the attributes of an ndarray is essential to working with NumPy effectively. Here are the key attributes:

• ndarray.shape: Returns a tuple representing the shape (dimensions) of the array.
• ndarray.ndim: Returns the number of dimensions (axes) of the array.
• ndarray.size: Returns the total number of elements in the array.
• ndarray.dtype: Provides the data type of the array elements.
• ndarray.itemsize: Returns the size (in bytes) of each element

Example:

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

print("Shape:", arr.shape)  
print("Dimensions:", arr.ndim)  
print("Size:", arr.size) 
print("Data type:", arr.dtype)  
print("Item size:", arr.itemsize)  

Shape: (2, 3)
Dimensions: 2
Size: 6
Data type: int64
Item size: 8

Array Indexing and Slicing

NumPy allows powerful indexing and slicing operations on ndarrays, similar to Python lists but with additional functionality.

• Basic Indexing: we can index a single element in an array using square brackets.

Example:

import numpy as np
arr = np.array([10, 20, 30, 40, 50])
print(arr[2]) 

• Slicing: You can extract sub-arrays using slicing syntax.

import numpy as np
arr = np.array([10, 20, 30, 40, 50])
print(arr[1:4])

[20 30 40]

• Multi-dimensional Indexing: In multi-dimensional arrays, you can index and slice each dimension separately.

Example:

import numpy as np
arr_2d = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]])

# 6 (element at row 1, column 2)
print(arr_2d[1, 2])  

# Sub-matrix from rows 0-1, columns 1-2
print(arr_2d[0:2, 1:3])  

6
[[2 3]
 [5 6]]

Array Operations

These operations allow you to perform element-wise arithmetic or other operations on entire arrays without the need for explicit loops.

• Element-wise Operations:

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

print(arr + arr2)  
print(arr * arr2)  
print(arr - arr2)  
print(arr / arr2) 

[5 7 9]
[ 4 10 18]
[-3 -3 -3]
[0.25 0.4  0.5 ]

• Matrix Operations (Dot product):

import numpy as np
matrix1 = np.array([[1, 2], [3, 4]])
matrix2 = np.array([[5, 6], [7, 8]])

print(np.dot(matrix1, matrix2))  

[[19 22]
 [43 50]]

Broadcasting

Broadcasting is a powerful feature in NumPy that allows you to perform operations on arrays of different shapes.

Example:

import numpy as np
arr = np.array([[1, 2], [3, 4]])

# Adding 10 to each element of the array
print(arr + 10)  

[[11 12]
 [13 14]]

Reshaping and Flattening

NumPy provides functions to reshape or flatten arrays, which is useful when working with machine learning or deep learning algorithms.

• Reshaping:

import numpy as np 
arr = np.array([1, 2, 3, 4, 5, 6])
reshaped_arr = arr.reshape(2, 3)  # 2 rows, 3 columns
print(reshaped_arr)

[[1 2 3]
 [4 5 6]]

• Flattening: Convert multi-dimensional arrays into one-dimensional arrays.

import numpy as np
arr = np.array([[1, 2, 3], [4, 5, 6]])
flattened_arr = arr.flatten()
print(flattened_arr)  

[1 2 3 4 5 6]

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Article Tags :

• AI-ML-DS
• Python-numpy